ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Classification Of Color Satellite Images Using Computational Intelligence


Affiliations
1 National Institute of Electronics and Information Technology, India
2 Department of Computer Science and Applications, Panjab University, India
     

   Subscribe/Renew Journal


The classification of color satellite images is presented using Multilayer Perceptron Neural Network and Support Vector Machine. Multilayer Perceptron is used for non-linear classification with 10 hidden layers using different number of epochs. A multiclass SVM is chosen for classification using radial basis function (RBF) kernel. Before performing classification, the image enhancement and feature extraction steps are carried out. The image enhancement is done using contrast stretching. The color features are extracted by using Principal Components Analysis (PCA). Classification results are obtained and testing is done by varying the number of images in the training and test datasets, the number of features and different classifiers. 100 images each obtained from Landsat satellite of NASA, US and Bhuvan geoportal of NRSC, Hyderabad are used in classification. Seven class categories, residential land, commercial land, grasslands, evergreen forest, mixed forest, sediments and clear water are identified. The results are analyzed and it is observed that SVM provides better results as compared to Multilayer Perceptron (MLP). Performance analysis is carried out with respect to classification accuracy and time.

Keywords

Image Classification, Multilayer Perceptron Neural Network, Support Vector Machine, Landsat, Bhuvan.
Subscription Login to verify subscription
User
Notifications
Font Size

  • S. Chib and M.S. Devi, “Performance Analysis of Enhancement Techniques for Satellite Images”, International Journal of Computer Sciences and Engineering, Vol. 4, No. 12, pp. 113-119, 2016.

  • H. Svatonova, “Analysis of Visual Interpretation of Satellite Data”, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 41, No. 4, pp. 675-681, 2016.

  • Y. Zhong and L. Zhang, “An Adaptive Artificial Immune Network for Supervised Classification of Multi-Hyperspectral Remote Sensing Imagery”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 50, No. 3, pp. 894-909, 2012.

  • K. Vani, “Satellite Image Processing”, Proceedings of 4th International Conference on Signal Processing, Communication and Networking, pp. 53-59, 2017.

  • M. Kumar, “Digital Image Processing of Remotely Sensed Satellite Images for Information Extraction”, Proceedings of Conference on Advances in Communication and Control Systems, pp. 406-410, 2013.

  • M.S. Devi and S. Chib, “Classification of Satellite Images using Perceptron Neural Network”, International Journal of Computational Intelligence Research, Vol. 15, No. 1, pp. 1-10, 2019.

  • E. Ojaghi and S. Hamid, “Using Artificial Neural Network for Classification of High Resolution Remotely Sensed Images and Assessment of Its Performance Compared with Statistical Methods”, American Journal of Engineering, Technology and Society, Vol. 2, No. 1, pp. 1-8, 2015.

  • A.B. Laxmi Narayana Eeti and Krishna Mohan Buddhiraju, “A Single Classifier using Principal Components Vs Multi-Classifier System: In Landuse-Landcover Classification of Worldview-2 Sensor Data”, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 2, No. 8, pp. 9-12, 2014.

  • M. Shahbaz, A. Guergachi, A. Noreen and M. Shaheen, “Classification by Object Recognition in Satellite Images by using Data Mining”, Proceedings of World Congress on Engineering, pp. 406-414, 2012.

  • T. Kavzoglu and P.M. Mather, “The use of Backpropagating Artificial Neural Networks in Land Cover Classification”, International Journal of Remote Sensing, Vol. 15, No. 23, pp. 4907-k4938, 2003.

  • G. Mountrakis, J. Im, and C. Ogole, “Support Vector Machines in Remote Sensing: A Review”, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 66, No. 3, pp. 247-259, 2011.

  • B.M.A. Nurul Iman Saiful Bahari, “Application of Support Vector Machine for Classification of Multispectral Data”, Proceedings of 7th IGRSM International Remote Sensing and GIS Conference and Exhibition, pp. 1-8, 2014.

  • K. Tangthaikwan and A.S. Image, “Multiclass Support Vector Machine for Classification Spatial Data from Satellite Image”, Proceedings of the IEEE, Vol. 75, pp. 111-115, 2017.

  • M. Kelly, J.E. Estes and K.A. Knight, “Image Interpretation Keys for Validation of Global Data Sets”, Photogrammetric Engineering and Remote Sensing, Vol. 65, No. 9, pp. 1041-1049, 1999.

  • C.C. Chang and C.J. Lin, “LIBSVM: A Library for Support Vector Machines”, ACM Transactions on Intelligent Systems and Technology, Vol. 2, No. 3, pp. 1-39, 2011.

  • O.S. Soliman, A.S. Mahmoud and S.M. Hassan, “Remote Sensing Satellite Images Classification using Support Vector Machine and Particle Swarm Optimization”, Proceedings of 3rd International Conference on Innovations in Bio-Inspired Computing and Applications, pp. 280-285, 2012.

  • C.L. Xin Zhang, Jintian Cui and Weisheng Wang, “A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm”, Sensors, Vol. 17, No. 7, pp. 1-13, 2017.

  • W. Da Silva, M. Habermann, E.H. Shiguemori, L.D.L. Andrade and R.M. De Castro, “Multispectral Image Classification using Multilayer Perceptron and Principal Components Analysis”, Proceedings of BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence, pp. 557–562, 2013.

  • Earth Explorer, Available at https://earthexplorer.usgs.gov/, Accessed at 2021.

  • Bhuvan, Available at https://bhuvan-app3.nrsc.gov.in/data/download/index.php? c=s&s=L3, Accessed at 2021.


Abstract Views: 317

PDF Views: 1




  • Classification Of Color Satellite Images Using Computational Intelligence

Abstract Views: 317  |  PDF Views: 1

Authors

Sunita Chib
National Institute of Electronics and Information Technology, India
M. Syamala Devi
Department of Computer Science and Applications, Panjab University, India

Abstract


The classification of color satellite images is presented using Multilayer Perceptron Neural Network and Support Vector Machine. Multilayer Perceptron is used for non-linear classification with 10 hidden layers using different number of epochs. A multiclass SVM is chosen for classification using radial basis function (RBF) kernel. Before performing classification, the image enhancement and feature extraction steps are carried out. The image enhancement is done using contrast stretching. The color features are extracted by using Principal Components Analysis (PCA). Classification results are obtained and testing is done by varying the number of images in the training and test datasets, the number of features and different classifiers. 100 images each obtained from Landsat satellite of NASA, US and Bhuvan geoportal of NRSC, Hyderabad are used in classification. Seven class categories, residential land, commercial land, grasslands, evergreen forest, mixed forest, sediments and clear water are identified. The results are analyzed and it is observed that SVM provides better results as compared to Multilayer Perceptron (MLP). Performance analysis is carried out with respect to classification accuracy and time.

Keywords


Image Classification, Multilayer Perceptron Neural Network, Support Vector Machine, Landsat, Bhuvan.

References