Article Versions
Export Article
Cite this article
  • Normal Style
  • MLA Style
  • APA Style
  • Chicago Style
Research Article
Open Access Peer-reviewed

An Assessment of Artificial Neural Networks, Support Vector Machines and Decision Trees for Land Cover Classification Using Sentinel-2A Data

Rahel Hamad
Applied Ecology and Environmental Sciences. 2020, 8(6), 459-464. DOI: 10.12691/aees-8-6-18
Received August 17, 2020; Revised September 19, 2020; Accepted September 29, 2020

Abstract

Remotely sensed images serve as a valuable source of present and archival information since they provide the geographical distribution of natural and cultural features both spatially and temporally, as well as objects on the earth’s surface. Three machine learning classifiers, namely artificial neural networks (ANN), support vector machines (SVM), and decision tree (DT) algorithms, were applied in order to classify the Sentinel-2A data over the city of Soran. The differences in classification accuracies were evaluated by the confusion matrix. The supervised ANNs obtained the most accurate classification accuracy as compared with support SVM and DTs algorithms. Furthermore, the overall accuracy assessment of ANN was 90%, with SVM at 65%, while DTs were 60%. It can be concluded that ANNs can provide the best classification machine learning technique for land cover classification.

1. Introduction

For last five decades Landsat imagery has presented a unique resource for various researchers and scientists through their studies in many and disparate areas such as forestry, education, agriculture, etc. 1. In addition, the recent Sentinel-2A multispectral satellite sensor was successfully launched by the European Space Agency (ESA) on 23 June 2015. It provides images for various purposes, and whose characteristics can meet the vast majority of analysis requirements 2.

The successful remotely sensed classification is an essential source for many application processes 3, because many environmental, social and economic applications are based on the results of classification 4. Moreover, to obtain a successful classification, a suitable classification system is required 5. Therefore, the submission of research objectives, questions, and problems are necessary from the end user before employing the classification 3. On the other hand, there are many factors must be taken into account when choosing a classification method for use, such as the spatial resolution of the remotely sensed data, different sources of data, a classification system, and availability of appropriate classification software 4. In general, the purpose of image classification is to predict any entered image categories using its features 6.

Different classification techniques have been studied by many researchers regarding the accuracy of their maps. Image processing and classification approaches, may affect the success of the classification, as the classification of remote-sensing is a complex process and requires consideration of a number of factors 4. Various types of algorithms are used to provide a suitable accuracy of classification 3. Classifying remotely sensed data is one of the basic steps to extracting information 6, via any of various classification techniques 4. Many advanced classification approaches, in the last two decades, have been applied for image classification such as artificial neural networks (ANNs) 7, 8, support vector machines (SVMs) 9, 10, decision trees (DTs) 11, 12, spectral angle classifiers 13, 14, and rule-base evidential reasoning for building expert and decision-making systems 15, 16. These aforementioned methods are non-parametric classifiers, which do not employ statistical parameters to calculate class separation.

The ideal map is one that has been well validated and achieved, and which should consider a range of factors such as methods for collecting reference points, classification scheme, sample method collection, sample size and sample unit, and the accuracy assessment calculation 17, 18. Furthermore, other important accuracy assessment elements can be derived from the confusion matrix such as producer's accuracy, user’s accuracy, and overall kappa statistics. However, obtaining the accuracy assessment is more difficult than classification 19.

The performance of different machine learning classifiers, as evaluated through many comparative studies have been investigated 1, 20, 21, 22, 23, 24. For instance, Szantoi, Escobedo 20 implemented ANN and DTs on heterogeneous wetland communities, and found that ANN significantly outperformed the DT classifier. Chen 21 found that Neural Networks were the most efficient algorithm and outperformed SVM and DTs. Huang, Davis 1 assessed SVM for land cover classification, and compared ANN and DTs. They found that SVM can produce a more accurate classification. Khatami, Mountrakis 22 found that SVM considerably outperformed neural networks and decision trees in a direct comparison. Otukei and Blaschke 23 compared DTs, SVM, and MLC for assessing land cover change, and found DTs to be more effective than SVM or MLC. Mohamed 24 revealed that the classification accuracy of the DT algorithm was better than the ANN algorithm in his comparative research. Otukei and Blaschke 23 showed decision trees to give better results than MLC and SVM techniques for land cover change assessment. Song and Ying 25 successfully showed the decision tree method to be a powerful statistical tool for classification, prediction, and interpretation that has several potential applications in medical research. Friedl and Brodley 26 found improved accuracy in complex LULC classifications using a decision tree algorithm.

The objectives of this work are to (i) compare the classification accuracy of three different algorithms and (ii) to propose the most suitable algorithm for LU/LC over the city of Soran using Sentinel- 2A data for 2019.

2. Materials and Methods

2.1. Study Site and Data

Soran was selected as a study area from the previous work by 27. The free and open source Sentinel-2A Level image was downloaded from the Copernicus website (https://scihub.copernicus.eu/) with a total of thirteen spectral bands, as acquired on August 20, 2019. The Sentinel-2A data is widely used in land cover classification. For this work, four bands - visible (blue, green, red) and near-infrared with a special resolution of 10 m - were used for the classification, as per Figure 1.

2.2. Algorithm Selection
2.2.1. Artificial Neural Networks Classification

The use of artificial neural networks in remote sensing studies dates back to the early 1990s 28. An artificial neural network (ANN) is a mathematical model 29 that takes the human brain as a model for problem solving 30. An ANN is also referred to as a simulated neural network (SNN) or, more commonly, just a neural network (NN). An ANN is an interconnected group of artificial neurons that uses a mathematical model to manipulate information based on a mathematical link approach. Furthermore, an ANN is an adaptive system that changes its structure based on external or internal information that flows across the network 31.

A single ANN architecture typically consist of an input, one or more hidden, and an output layer 32. Kanellopoulos and Wilkinson 33 identified that the classification accuracy with an ANN is often greater than traditional statistical classifiers. Each layer in an ANN is made up of many neurons where each neuron signifies a variable in the input layer 30, therefore the stimulations work in the same way as biological neural networks in the human brain process information 29. There will be one output node for each class in the classification system, through the image classification process. The general shape of an ANN is shown in Figure 2.

A neural network is generated for a specific application, such as pattern recognition or data classification, through a learning process. ANN is considered the best method in a problem solving approach for traditionally problematic techniques for which algorithmic solution is too complex to be found, or for which such a solution does not exist 31. Also, neural networks can perform image categorization reasonably quickly, although the training process itself can be extremely time consuming 35.


2.2.2. Support Vector Machines

Boser, Guyon, and Vapnik first presented support vector machines (SVMs) formally in 1992 36. The SVM method is a set of non-parametric supervised learning method used for classification techniques 37, and tries to generalize and provide reasonable predictions for new datasets by creating a hyperplane that separates the dataset into classes. Polynomial, radial basis functions, and sigmoid kernels are the most commonly used kernels for image processing 38. SVM based on statistical learning determines the border for distinguishing data belonging to two classes from each other in an optimal manner 1 if the data to be classified are separated linearly or non-linearly 37. Thus, it can be used for linearly and non-linearly separable data.

The key concept of this discriminant machine learning technique 39 is to find an optimal hyperplane or the largest amount of margin among many hyperplanes between two categories as possible 36, as per Figure 3. Thus, it seeks the ideal hyperplane to find the maximum margin in order separate the classes 40, which is why SVM is known as a large margin classifier 39.

SVM was developed from powerful implementation theory, whereas ANNs were experimentally moved from application to theory. SVM does not control the complexity of the model, as ANN does; instead, it determines the complexity model automatically by setting the number of support vectors 39. It has been found that SVM generally outperforms neural networks, which is novel pattern recognition method 1, 9.

Finally, support vector machines have been selected as a group of relatively novel statistical learning algorithms in classifying homogeneous and heterogeneous land cover types, with the associated vigour being confirmed by many various researchers 40.


2.2.3. Decision Tree Classification

Decision Trees (DTs), or Decision Tree Classifiers (DTCs), are a supervised machine learning method and are one of many classifiers from statistical-based algorithms 42 which have been successfully applied in a variety of fields for digital image classification 43. A DTC is a non-parametric classifier 44, which is an effective and popular technique for classification 45. Decision tree classification problems can be solved through identifying to which set an object belongs 46. The tree in the DT approach constitutes a root node at the top, a few hidden nodes (internal nodes) which split the objects into different categories, and a large number of terminal nodes (known leaves which contain most homogeneous classes), which is the output 45, 47. Generally speaking, a decision tree asks a question and then classifies 48 through defining the best associated tree structure and decision boundaries 23.

3. Results

In this work, three machine learning classifiers were compared for LULC classification, namely artificial neural networks, support vector machines, and decision trees.

3.1. Classification Accuracy Assessment

The confusion matrix results of all three algorithms are reported in Table 1 - Table 3 with overall accuracy, producer’s accuracy, user’s accuracy, and Kappa coefficient for the classified maps for 2019 using Sentinel-2A imaging 35. The overall classification accuracies for ANN, SVM, and DTs are 90%, 65% and 60%, and their Kappa coefficients are 0.86, 0.57, and 0.53, respectively.

3.2. Landscape Structure and Dynamics

The classified areas were measured in the percentage, as per Figure 4. Barren land was the predominant class followed by built-up areas, cultivated land and riparian zones, which covered 51.93%, 29.28%, 15.56% and 3.21%, respectively, according to the ANN algorithm. In the instance of the SVM algorithm, the built-up occupied was 50.25% followed by barren land at 42.6%, cultivated land at 22.06% and riparian zone at 3.07%. By comparison, the decision tree classifier produced another set of results. Furthermore, the maximum percentage was occupied by barren land at 39.42%, whereas the minimum class was computerized for cultivated land by 2.87%. However, the built-up area and riparian zones had almost the same percentage coverages at 29.05% and 28.63%, respectively.

The land use/land cover maps are shown in Figure 5 for the three algorithms used, including the natural colour composites. The red colour represents built-up area, orange indicates barren land, and the light green colour indicates cultivated land, while dark green represents riparian zone.

4. Discussion

4.1. Artificial Neural Networks

The results of the current work verified that the highest classification accuracy was obtained by ANN using the Sentinel-2 data. Most pixels which belong to classes were correctly classified. Thus, this indicates promising results through the application of the ANN to the whole dataset. On the other hand, it achieved only poor results for pixels in classes characterized by lower classification rates, as revealed by both SVM and DTs. The demand for the use of ANN in many real-world applications is increasing with time 49 as a result of, their utility in solving problems that do not have algorithmic solutions is due to their powerful methodology 31.

4.2. Support Vector Machine

In spite of the fact that SVM has been successfully used for data classification in many studies, but it did not prove particularly effective in the current work. In general, the main reasons for SVM producing sub-optimal results was due to the weakness of the soft margin optimization problem and the imbalanced support vector ratio 50.

For instance, in the current work, the number of training cases for built-up area are significantly outnumbered by barren land and slightly so by cultivated land, as per Figure 4. More generally, the training data sets for barren land and cultivated land fell into the built-up area classes, thus the class boundary for built-up area learned by the SVMs are sharply skewed towards barren land and cultivated land. In other words, the dataset is imbalanced with regard to built-up area with barren land and cultivated land. This imbalanced dataset, as used to develop the separating hyperplane for the SVM model, was identified by 51. Thus, the correct distances and correct orientation of the hyperplane in learned SVMs do not perform at their best, which could be due to the hyperplane separation may not being exactly between two categories. Besides, this could also indicate a purely linear SVM classifier that led to the extremely poor performance observed, or in other words there is no clear separation data lining as identified by 39. In general, it is difficult to explain SVM and different related learning algorithms 52.

4.3. Decision Tree Classifier

In the current work, the decision tree algorithm did not yield promising results though it has been successfully used in various other fields 23, 44, 53. Kumar and Kumar 54 focused on two main disadvantages of DTs, which are overfitting and not being fit for continuous variables. Overfitting, which is a common problem with decision trees, was due to noise in the current study, and can be observed as misclassification of riparian zone, cultivated land, and barren land in the decision tree classifier. Besides, overfitting due to lack of samples could also be another reason for overfitting. Furthermore, the decision trees lost information between three aforementioned classes by categorization variables, or in other words the entropy which is a measure of the uncertainty between classes and that produced low accuracy output from the test data. However more generally, decision trees are prone to overfitting because they are very data concentrated stated Choy and Flom 55.

Furthermore, the tree in the decision trees is highly dependent on the training data used, therefore any slight change in the data can cause large variation in the estimated tree, which subsequently creates a completely different tree. Another reason could be due to the physical boundaries between zones. Soran has a homogeneous landscape and all classes can be easily distinguished and separated by the naked eye, thus DTs did not perform well with the physical boundaries between zones. Finally, a lack of, or no interactions between variables also prevent DTs from working particularly well 56.

5. Conclusions

The analyses showed differences in the classification accuracies obtained with different algorithms. Artificial neural networks produced considerably better results than the support vector machine and decision trees algorithms, in the current work. In general, SVM and DTs were not as effective at representing the classification over the city of Soran. Hence, ANN is a robust method for classification and detection of change, and could potentially overcome the mixed pixel problem in the image. Relatively speaking, an accurate LULC product can be achieved using ANNs on the recently acquired Sentinel-2 sensor imagery. Thus, using Sentinel-2 data may result in better resolution, and certainly more accurate LULC produces.

Acknowledgments

Many thanks to the Scientific Research Centre (SRC) at Soran University and to Dr. K. Kolo (SRC) for valuable comments on the manuscript.

Conflict of Interests

Author has declared that no competing interests exist.

References

[1]  Huang, C., L. Davis, and J. Townshend, An assessment of support vector machines for land cover classification. International Journal of remote sensing, 2002. 23(4): p. 725-749.
In article      View Article
 
[2]  Li, J. and D.P. Roy, A global analysis of Sentinel-2A, Sentinel-2B and Landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sensing, 2017. 9(9): p. 902.
In article      View Article
 
[3]  Lu, D., S. Hetrick, and E. Moran, Impervious surface mapping with Quickbird imagery. International journal of remote sensing, 2011. 32(9): p. 2519-2533.
In article      View Article  PubMed
 
[4]  Lu, D. and Q. Weng, A survey of image classification methods and techniques for improving classification performance. International journal of Remote sensing, 2007. 28(5): p. 823-870.
In article      View Article
 
[5]  Anderson, J.R., A land use and land cover classification system for use with remote sensor data. Vol. 964. 1976: US Government Printing Office.
In article      View Article
 
[6]  Thai, L., T. Hai, and N.T. Thuy, Image classification using support vector machine and artificial neural network. International Journal of Information Technology and Computer Science (IJITCS), 2012. 4(5): p. 32-38.
In article      View Article
 
[7]  Foody, G., Supervised image classification by MLP and RBF neural networks with and without an exhaustively defined set of classes. International Journal of Remote Sensing, 2004. 25(15): p. 3091-3104.
In article      View Article
 
[8]  Verbeke, L., F. Vancoillie, and R. De Wulf, Reusing back-propagation artificial neural networks for land cover classification in tropical savannahs. International Journal of Remote Sensing, 2004. 25(14): p. 2747-2771.
In article      View Article
 
[9]  Foody, G.M. and A. Mathur, Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification. Remote Sensing of Environment, 2004. 93(1-2): p. 107-117.
In article      View Article
 
[10]  Mitra, P., B.U. Shankar, and S.K. Pal, Segmentation of multispectral remote sensing images using active support vector machines. Pattern recognition letters, 2004. 25(9): p. 1067-1074.
In article      View Article
 
[11]  Lawrence, R., et al., Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis. Remote sensing of environment, 2004. 90(3): p. 331-336.
In article      View Article
 
[12]  DeFries, R. and J.C.-W. Chan, Multiple criteria for evaluating machine learning algorithms for land cover classification from satellite data. Remote Sensing of Environment, 2000. 74(3): p. 503-515.
In article      View Article
 
[13]  Sohn, Y., E. Morán, and F. Gurri, Deforestation in North-Central Yucatan (1985-1995)- Mapping secondary succession of forest and agricultural land use in Sotuta using the cosine of the angle concept. Photogrammetric engineering and remote sensing, 1999. 65: p. 947-958.
In article      
 
[14]  Sohn, Y. and N.S. Rebello, Supervised and unsupervised spectral angle classifiers. Photogrammetric engineering and remote sensing, 2002. 68(12): p. 1271-1282.
In article      
 
[15]  Dymova, L., P. Sevastianov, and P. Bartosiewicz, A new approach to the rule-base evidential reasoning: Stock trading expert system application. Expert Systems with Applications, 2010. 37(8): p. 5564-5576.
In article      View Article
 
[16]  Yang, J.-B., et al., Belief rule-base inference methodology using the evidential reasoning approach-RIMER. IEEE Transactions on systems, Man, and Cybernetics-part A: Systems and Humans, 2006. 36(2): p. 266-285.
In article      View Article
 
[17]  Franklin, S. and M. Wulder, Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas. Progress in Physical Geography, 2002. 26(2): p. 173-205.
In article      View Article
 
[18]  Congalton, R.G. and L. Plourde, Quality assurance and accuracy assessment of information derived from remotely sensed data. Manual of geospatial science and technology, 2002: p. 349-361.
In article      View Article
 
[19]  Foody, G.M., Status of land cover classification accuracy assessment. Remote sensing of environment, 2002. 80(1): p. 185-201.
In article      View Article
 
[20]  Szantoi, Z., et al., Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features. Environmental monitoring and assessment, 2015. 187(5): p. 262.
In article      View Article  PubMed
 
[21]  Chen, J., A comparison of four data mining models: bayes, neural network. SVM and decision trees in identifying syndromes in coronary heart disease, 2007. 4491: p. 2007.
In article      View Article
 
[22]  Khatami, R., G. Mountrakis, and S.V. Stehman, A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research. Remote Sensing of Environment, 2016. 177: p. 89-100.
In article      View Article
 
[23]  Otukei, J.R. and T. Blaschke, Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 2010. 12: p. S27-S31.
In article      View Article
 
[24]  Mohamed, A.E., Comparative study of four supervised machine learning techniques for classification. International Journal of Applied, 2017. 7(2).
In article      
 
[25]  Song, Y.-Y. and L. Ying, Decision tree methods: applications for classification and prediction. Shanghai archives of psychiatry, 2015. 27(2): p. 130.
In article      
 
[26]  Friedl, M.A. and C.E. Brodley, Decision tree classification of land cover from remotely sensed data. Remote sensing of environment, 1997. 61(3): p. 399-409.
In article      View Article
 
[27]  Hamad, R., A remote sensing and GIS-based analysis of urban sprawl in Soran District, Iraqi Kurdistan. SN Applied Sciences, 2020. 2(1): p. 24.
In article      View Article
 
[28]  Atkinson, P.M. and A.R. Tatnall, Introduction neural networks in remote sensing. International Journal of remote sensing, 1997. 18(4): p. 699-709.
In article      View Article
 
[29]  Liu, C., et al., Comparison of neural networks and statistical methods in classification of ecological habitats using FIA data. Forest Science, 2003. 49(4): p. 619-631.
In article      
 
[30]  Kesikoglu, M.H., et al., Performance of ANN, SVM and MLH techniques for land use/cover change detection at Sultan Marshes wetland, Turkey. Water Science and Technology, 2019. 80(3): p. 466-477.
In article      View Article  PubMed
 
[31]  Bhatta, B., Remote sensing and GIS. 2008: Oxford University Press, USA.
In article      
 
[32]  Civco, D.L., Artificial neural networks for land-cover classification and mapping. International journal of geographical information science, 1993. 7(2): p. 173-186.
In article      View Article
 
[33]  Kanellopoulos, I. and G.G. Wilkinson, Strategies and best practice for neural network image classification. International Journal of Remote Sensing, 1997. 18(4): p. 711-725.
In article      View Article
 
[34]  Todorov, T.I., R.R. Stewart, and D.P. Hampson, Porosity prediction using attributes from 3C–3D seismic data. 1998, CREWES research report, 10, Chapt-52.
In article      View Article
 
[35]  Lillesand, T., R.W. Kiefer, and J. Chipman, Remote sensing and image interpretation. 2014: John Wiley & Sons.
In article      
 
[36]  Wang, Q., Y. Tian, and D. Liu, Adaptive FH-SVM for Imbalanced Classification. IEEE Access, 2019. 7: p. 130410-130422.
In article      View Article
 
[37]  Rudrapal, D. and M. Subhedar, Land cover classification using support vector machine. International Journal of Engineering Research & Technology (IJERT), 2015. 4(09).
In article      View Article
 
[38]  Kranjčić, N., et al., Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns. Remote Sensing, 2019. 11(6): p. 655.
In article      View Article
 
[39]  Awad, M. and R. Khanna, Support vector machines for classification, in Efficient Learning Machines. 2015, Springer. p. 39-66.
In article      View Article
 
[40]  Shi, D. and X. Yang, Support vector machines for land cover mapping from remote sensor imagery, in Monitoring and Modeling of Global Changes: A Geomatics Perspective. 2015, Springer. p. 265-279.
In article      View Article  PubMed
 
[41]  Guo, G., S.Z. Li, and K.L. Chan, Support vector machines for face recognition. Image and Vision computing, 2001. 19(9-10): p. 631-638.
In article      View Article
 
[42]  Xie, Z., et al., Classification of land cover, forest, and tree species classes with ZiYuan-3 multispectral and stereo data. Remote Sensing, 2019. 11(2): p. 164.
In article      View Article
 
[43]  Osei-Bryson, K.-M., Post-pruning in decision tree induction using multiple performance measures. Computers & operations research, 2007. 34(11): p. 3331-3345.
In article      View Article
 
[44]  Sahoo, S. and P. Raj, EVALUATION OF DIFFERENT TECHNIQUES TO DETECT LAND USE/LAND COVER OVER AN AREA. Journal of Spatial Hydrology, 2019. 14(2).
In article      
 
[45]  Sharma, R., A. Ghosh, and P. Joshi, Decision tree approach for classification of remotely sensed satellite data using open source support. Journal of Earth System Science, 2013. 122(5): p. 1237-1247.
In article      View Article
 
[46]  Michalski, R.S., J.G. Carbonell, and T.M. Mitchell, Machine Learning: An Artificial Intelligence Approach, vol. 1. Tioga, Palo Alto, California, 1983.
In article      View Article  PubMed
 
[47]  Pantazi, X.-E., D. Moshou, and D. Bochtis, Intelligent Data Mining and Fusion Systems in Agriculture. 2019: Academic Press.
In article      View Article
 
[48]  Coleman, J. and J.S. Coleman, Introducing speech and language processing. 2005: Cambridge university press.
In article      
 
[49]  Korkobi, T., M. Djemel, and M. Chtourou, Stability analysis of neural networks-based system identification. Modelling and Simulation in Engineering, 2008. 2008.
In article      View Article
 
[50]  He, H. and Y. Ma, Imbalanced learning: foundations, algorithms, and applications. 2013: John Wiley & Sons.
In article      View Article
 
[51]  Veropoulos, Campell C., and Cristanini N., “Controlling the sensitivity of support vector machines,” in Proceeding of the International Joint Conference on Artificial Intelligence (Stockholm, Sweden). 1999: p. pp. 55-60.
In article      
 
[52]  Kowsari, K., et al. Hdltex: Hierarchical deep learning for text classification. in 2017 16th IEEE international conference on machine learning and applications (ICMLA). 2017. IEEE.
In article      View Article
 
[53]  Punia, M., P.K. Joshi, and M. Porwal, Decision tree classification of land use land cover for Delhi, India using IRS-P6 AWiFS data. Expert systems with Applications, 2011. 38(5): p. 5577-5583.
In article      View Article
 
[54]  Kumar, G.D. and G.D. Kumar, Machine Learning Techniques for Improved Business Analytics. 2018: IGI Global.
In article      
 
[55]  Choy, M. and P. Flom. Building decision trees from decision stumps. in SAS Global Forum. 2010.
In article      
 
[56]  Ramezankhani, A., et al., Decision tree-based modelling for identification of potential interactions between type 2 diabetes risk factors: a decade follow-up in a Middle East prospective cohort study. BMJ open, 2016. 6(12): p. e013336.
In article      View Article  PubMed
 

Published with license by Science and Education Publishing, Copyright © 2020 Rahel Hamad

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Cite this article:

Normal Style
Rahel Hamad. An Assessment of Artificial Neural Networks, Support Vector Machines and Decision Trees for Land Cover Classification Using Sentinel-2A Data. Applied Ecology and Environmental Sciences. Vol. 8, No. 6, 2020, pp 459-464. http://pubs.sciepub.com/aees/8/6/18
MLA Style
Hamad, Rahel. "An Assessment of Artificial Neural Networks, Support Vector Machines and Decision Trees for Land Cover Classification Using Sentinel-2A Data." Applied Ecology and Environmental Sciences 8.6 (2020): 459-464.
APA Style
Hamad, R. (2020). An Assessment of Artificial Neural Networks, Support Vector Machines and Decision Trees for Land Cover Classification Using Sentinel-2A Data. Applied Ecology and Environmental Sciences, 8(6), 459-464.
Chicago Style
Hamad, Rahel. "An Assessment of Artificial Neural Networks, Support Vector Machines and Decision Trees for Land Cover Classification Using Sentinel-2A Data." Applied Ecology and Environmental Sciences 8, no. 6 (2020): 459-464.
Share
  • Figure 1. Location map of Soran district in the Kurdistan Region of Iraq, after [27], showing the ANN result map of this specific work
  • Figure 5. Standard natural colour composites and land cover classifications via ANN, SVMs, and DTs of the acquired Sentinel-2A imagery
[1]  Huang, C., L. Davis, and J. Townshend, An assessment of support vector machines for land cover classification. International Journal of remote sensing, 2002. 23(4): p. 725-749.
In article      View Article
 
[2]  Li, J. and D.P. Roy, A global analysis of Sentinel-2A, Sentinel-2B and Landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sensing, 2017. 9(9): p. 902.
In article      View Article
 
[3]  Lu, D., S. Hetrick, and E. Moran, Impervious surface mapping with Quickbird imagery. International journal of remote sensing, 2011. 32(9): p. 2519-2533.
In article      View Article  PubMed
 
[4]  Lu, D. and Q. Weng, A survey of image classification methods and techniques for improving classification performance. International journal of Remote sensing, 2007. 28(5): p. 823-870.
In article      View Article
 
[5]  Anderson, J.R., A land use and land cover classification system for use with remote sensor data. Vol. 964. 1976: US Government Printing Office.
In article      View Article
 
[6]  Thai, L., T. Hai, and N.T. Thuy, Image classification using support vector machine and artificial neural network. International Journal of Information Technology and Computer Science (IJITCS), 2012. 4(5): p. 32-38.
In article      View Article
 
[7]  Foody, G., Supervised image classification by MLP and RBF neural networks with and without an exhaustively defined set of classes. International Journal of Remote Sensing, 2004. 25(15): p. 3091-3104.
In article      View Article
 
[8]  Verbeke, L., F. Vancoillie, and R. De Wulf, Reusing back-propagation artificial neural networks for land cover classification in tropical savannahs. International Journal of Remote Sensing, 2004. 25(14): p. 2747-2771.
In article      View Article
 
[9]  Foody, G.M. and A. Mathur, Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification. Remote Sensing of Environment, 2004. 93(1-2): p. 107-117.
In article      View Article
 
[10]  Mitra, P., B.U. Shankar, and S.K. Pal, Segmentation of multispectral remote sensing images using active support vector machines. Pattern recognition letters, 2004. 25(9): p. 1067-1074.
In article      View Article
 
[11]  Lawrence, R., et al., Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis. Remote sensing of environment, 2004. 90(3): p. 331-336.
In article      View Article
 
[12]  DeFries, R. and J.C.-W. Chan, Multiple criteria for evaluating machine learning algorithms for land cover classification from satellite data. Remote Sensing of Environment, 2000. 74(3): p. 503-515.
In article      View Article
 
[13]  Sohn, Y., E. Morán, and F. Gurri, Deforestation in North-Central Yucatan (1985-1995)- Mapping secondary succession of forest and agricultural land use in Sotuta using the cosine of the angle concept. Photogrammetric engineering and remote sensing, 1999. 65: p. 947-958.
In article      
 
[14]  Sohn, Y. and N.S. Rebello, Supervised and unsupervised spectral angle classifiers. Photogrammetric engineering and remote sensing, 2002. 68(12): p. 1271-1282.
In article      
 
[15]  Dymova, L., P. Sevastianov, and P. Bartosiewicz, A new approach to the rule-base evidential reasoning: Stock trading expert system application. Expert Systems with Applications, 2010. 37(8): p. 5564-5576.
In article      View Article
 
[16]  Yang, J.-B., et al., Belief rule-base inference methodology using the evidential reasoning approach-RIMER. IEEE Transactions on systems, Man, and Cybernetics-part A: Systems and Humans, 2006. 36(2): p. 266-285.
In article      View Article
 
[17]  Franklin, S. and M. Wulder, Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas. Progress in Physical Geography, 2002. 26(2): p. 173-205.
In article      View Article
 
[18]  Congalton, R.G. and L. Plourde, Quality assurance and accuracy assessment of information derived from remotely sensed data. Manual of geospatial science and technology, 2002: p. 349-361.
In article      View Article
 
[19]  Foody, G.M., Status of land cover classification accuracy assessment. Remote sensing of environment, 2002. 80(1): p. 185-201.
In article      View Article
 
[20]  Szantoi, Z., et al., Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features. Environmental monitoring and assessment, 2015. 187(5): p. 262.
In article      View Article  PubMed
 
[21]  Chen, J., A comparison of four data mining models: bayes, neural network. SVM and decision trees in identifying syndromes in coronary heart disease, 2007. 4491: p. 2007.
In article      View Article
 
[22]  Khatami, R., G. Mountrakis, and S.V. Stehman, A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research. Remote Sensing of Environment, 2016. 177: p. 89-100.
In article      View Article
 
[23]  Otukei, J.R. and T. Blaschke, Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 2010. 12: p. S27-S31.
In article      View Article
 
[24]  Mohamed, A.E., Comparative study of four supervised machine learning techniques for classification. International Journal of Applied, 2017. 7(2).
In article      
 
[25]  Song, Y.-Y. and L. Ying, Decision tree methods: applications for classification and prediction. Shanghai archives of psychiatry, 2015. 27(2): p. 130.
In article      
 
[26]  Friedl, M.A. and C.E. Brodley, Decision tree classification of land cover from remotely sensed data. Remote sensing of environment, 1997. 61(3): p. 399-409.
In article      View Article
 
[27]  Hamad, R., A remote sensing and GIS-based analysis of urban sprawl in Soran District, Iraqi Kurdistan. SN Applied Sciences, 2020. 2(1): p. 24.
In article      View Article
 
[28]  Atkinson, P.M. and A.R. Tatnall, Introduction neural networks in remote sensing. International Journal of remote sensing, 1997. 18(4): p. 699-709.
In article      View Article
 
[29]  Liu, C., et al., Comparison of neural networks and statistical methods in classification of ecological habitats using FIA data. Forest Science, 2003. 49(4): p. 619-631.
In article      
 
[30]  Kesikoglu, M.H., et al., Performance of ANN, SVM and MLH techniques for land use/cover change detection at Sultan Marshes wetland, Turkey. Water Science and Technology, 2019. 80(3): p. 466-477.
In article      View Article  PubMed
 
[31]  Bhatta, B., Remote sensing and GIS. 2008: Oxford University Press, USA.
In article      
 
[32]  Civco, D.L., Artificial neural networks for land-cover classification and mapping. International journal of geographical information science, 1993. 7(2): p. 173-186.
In article      View Article
 
[33]  Kanellopoulos, I. and G.G. Wilkinson, Strategies and best practice for neural network image classification. International Journal of Remote Sensing, 1997. 18(4): p. 711-725.
In article      View Article
 
[34]  Todorov, T.I., R.R. Stewart, and D.P. Hampson, Porosity prediction using attributes from 3C–3D seismic data. 1998, CREWES research report, 10, Chapt-52.
In article      View Article
 
[35]  Lillesand, T., R.W. Kiefer, and J. Chipman, Remote sensing and image interpretation. 2014: John Wiley & Sons.
In article      
 
[36]  Wang, Q., Y. Tian, and D. Liu, Adaptive FH-SVM for Imbalanced Classification. IEEE Access, 2019. 7: p. 130410-130422.
In article      View Article
 
[37]  Rudrapal, D. and M. Subhedar, Land cover classification using support vector machine. International Journal of Engineering Research & Technology (IJERT), 2015. 4(09).
In article      View Article
 
[38]  Kranjčić, N., et al., Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns. Remote Sensing, 2019. 11(6): p. 655.
In article      View Article
 
[39]  Awad, M. and R. Khanna, Support vector machines for classification, in Efficient Learning Machines. 2015, Springer. p. 39-66.
In article      View Article
 
[40]  Shi, D. and X. Yang, Support vector machines for land cover mapping from remote sensor imagery, in Monitoring and Modeling of Global Changes: A Geomatics Perspective. 2015, Springer. p. 265-279.
In article      View Article  PubMed
 
[41]  Guo, G., S.Z. Li, and K.L. Chan, Support vector machines for face recognition. Image and Vision computing, 2001. 19(9-10): p. 631-638.
In article      View Article
 
[42]  Xie, Z., et al., Classification of land cover, forest, and tree species classes with ZiYuan-3 multispectral and stereo data. Remote Sensing, 2019. 11(2): p. 164.
In article      View Article
 
[43]  Osei-Bryson, K.-M., Post-pruning in decision tree induction using multiple performance measures. Computers & operations research, 2007. 34(11): p. 3331-3345.
In article      View Article
 
[44]  Sahoo, S. and P. Raj, EVALUATION OF DIFFERENT TECHNIQUES TO DETECT LAND USE/LAND COVER OVER AN AREA. Journal of Spatial Hydrology, 2019. 14(2).
In article      
 
[45]  Sharma, R., A. Ghosh, and P. Joshi, Decision tree approach for classification of remotely sensed satellite data using open source support. Journal of Earth System Science, 2013. 122(5): p. 1237-1247.
In article      View Article
 
[46]  Michalski, R.S., J.G. Carbonell, and T.M. Mitchell, Machine Learning: An Artificial Intelligence Approach, vol. 1. Tioga, Palo Alto, California, 1983.
In article      View Article  PubMed
 
[47]  Pantazi, X.-E., D. Moshou, and D. Bochtis, Intelligent Data Mining and Fusion Systems in Agriculture. 2019: Academic Press.
In article      View Article
 
[48]  Coleman, J. and J.S. Coleman, Introducing speech and language processing. 2005: Cambridge university press.
In article      
 
[49]  Korkobi, T., M. Djemel, and M. Chtourou, Stability analysis of neural networks-based system identification. Modelling and Simulation in Engineering, 2008. 2008.
In article      View Article
 
[50]  He, H. and Y. Ma, Imbalanced learning: foundations, algorithms, and applications. 2013: John Wiley & Sons.
In article      View Article
 
[51]  Veropoulos, Campell C., and Cristanini N., “Controlling the sensitivity of support vector machines,” in Proceeding of the International Joint Conference on Artificial Intelligence (Stockholm, Sweden). 1999: p. pp. 55-60.
In article      
 
[52]  Kowsari, K., et al. Hdltex: Hierarchical deep learning for text classification. in 2017 16th IEEE international conference on machine learning and applications (ICMLA). 2017. IEEE.
In article      View Article
 
[53]  Punia, M., P.K. Joshi, and M. Porwal, Decision tree classification of land use land cover for Delhi, India using IRS-P6 AWiFS data. Expert systems with Applications, 2011. 38(5): p. 5577-5583.
In article      View Article
 
[54]  Kumar, G.D. and G.D. Kumar, Machine Learning Techniques for Improved Business Analytics. 2018: IGI Global.
In article      
 
[55]  Choy, M. and P. Flom. Building decision trees from decision stumps. in SAS Global Forum. 2010.
In article      
 
[56]  Ramezankhani, A., et al., Decision tree-based modelling for identification of potential interactions between type 2 diabetes risk factors: a decade follow-up in a Middle East prospective cohort study. BMJ open, 2016. 6(12): p. e013336.
In article      View Article  PubMed