Image classification is one of the most significant applications for remote sensing imagery which is used in a wide range of applications from military to farm development by the government and private agencies. The proposed work focuses on the land use / land cover classification using advanced supervised classification techniques, Error Correcting Output Code (ECOC) multiclass model classifier and Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier. The samples of different classes such as, vegetation, quarry, water, wasteland and urban area were collected from different locations, refined, and trained on RGB, Gray and HSV color spaces based on the features mean, standard deviation, energy, contrast, entropy, and homogeneity. In ANFIS classifier when the number of inputs is more, the construction of FIS structure causes excessive propagation of number of rules which leads to memory overhead. Owing to this limitation, the number of features was restricted to mean and standard deviation in HSV and RGB color spaces. Based on the performance measures overall accuracy and kappa coefficient, it has been observed that the ECOC classifier produces better results in RGB color space and hence it has been applied on different locations of Tamil Nadu in Google Maps. From the results it has been proved that the ECOC classifier performs well when the ground cover nature is heterogeneous in nature.

Error Correcting Output Code (ECOC) multiclass model, True Color Composite Filter, Statistical Features, Textural Features, Google Maps’ Images, Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier

[1] S. Pravada, Bharatkar, Rahila Patel, “Assessment of Various Block Truncation Coding Based Remote Sensing Image Classification Techniques,” International Journal of Remote Sensing and GIS, Vol. 2, Issue 1, pp. 52-60, 2013.
[2] S.V.J.S.S.Rajesh, B.S.P. Rao, K. Niranjan, ‘Remote Sensing Study on Interlinking of Rivers from Pennar to Cauvery,’ International Journal of Computer Sciences and Engineering, Vol. 5, Issue-2, pp. 100-111, 2017.
[3] Mohamed H Almeer, ‘Vegetation extraction from free Google Earth images of Deserts using a robust BPNN approach in HSV space,’ International Journal of Advanced Research in Computer and Communication Engineering, Vol. 1, Issue 3, pp 134-140, 2012
[4] Iman Khosravi, Majid Mohammad-Beigi, ‘Multiple classifier systems for hyperspectral remote sensing data classification,’ Indian Society of Remote Sensing Vol. 42, Issue 2, pp. 423-428, 2014.
[5] Pal.M, Mather.P.M., ‘Support vector machines for classification in remote sensing,’ International Journal of Remote Sensing, Vol 26, Issue 5, pp. 1007-1011, 2005.
[6] Sergio Escalera, Oriol Pujol, Petia Radeva, ‘Error-Correcting Output Codes Library,’ Journal of Machine Learning Research, Vol 11, pp. 661-664, 2010
[7] Zhigang YAN, Yuanxuan YANG, ‘Application of ECOC SVMs in remote sensing image classification’, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XL-2, pp.191-196, 2014.
[8] Mohammad ali Bagheri, Gholam Ali Montazer, Sergio Escalera, ‘Error Correcting Output Codes for multiclass classification: Application to two image vision problems,’ The 16th CSI international symposium on Artificial Intelligence and Signal processing (AIS 2012), pp. 508-513, 2012.
[9] Madhubala.M, Mohan Rao.S.K, Ravindra Babu.G, ‘Classification of IRS LISS-III images by using Artificial Neural Networks,’ Recent Trends in Image Processing and Pattern Recognition, pp. 152-157, 2010.
[10] Sassan Mohammady, ‘Optimization of adaptive neuro fuzzy inference system based urban growth model,’ Mohammady City Territory and Architecture, Vol 3, Issue 10, pp. 1-15,2016.
[11] Mojedifar.S, Ranjbar.H, Nezamabadi-pour.H, ‘Adaptive neuro-fuzzy inference system application for hydrothermal alteration mapping using ASTER data,’ Journal of Mining & Environment, Vol 4. No. 2, pp. 83-96, 2013.
[12] Bernabe, S., and Plaza, A., ‘A New System to Perform Unsupervised and Supervised Classification of Satellite Images from Google Maps,’ Proceedings of SPIE, Vol. 7810, pp.781010-1-10, 2010.
[13] Agustin Garcia-Flores, Abel Paz-Gallardo, Antonio Plaza and Jun Li, ‘A new tool for supervised classification of satellite images available on web servers: Google Maps as a case of study,’ Proc. of SPIE Vol. 10007, pp.1000702-1-12, 2016.
[14] Lu, D., Weng, Q., ‘A survey of image classification methods and techniques for improving classification performance,’ International Journal of Remote Sensing, Vol. 28, No. 5, pp. , 823–870, 2007.
[15] Kusum, Nisha, "Comparative Analysis And Classification of Multispectral Remote Sensing Data", International Journal of Computer Sciences and Engineering, Vol.2, Issue.6, pp.18-21, 2014.
[16] Navneet Walia, Harsukhpreet Singh, Anurag Sharma, ‘ANFIS: Adaptive Neuro-Fuzzy Inference System- A Surve,’ International Journal of Computer Applications Volume 123 – No.13, pp. 0975 – 8887, 2015.
[17] A. Ray, B. Kartikeyan, S. Garg, "Towards Deriving an Optimal Approach for Denoising of RISAT-1 SAR Data Using Wavelet Transform", International Journal of Computer Sciences and Engineering, Vol.4, Issue.10, pp.33-46, 2016.