ICTACT Journal on Soft Computing

S. Jenicka ¹, A. Suruliandi ¹

Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN

Abstract

Land cover classification is a vital application area in satellite image processing domain. Texture is a useful feature in land cover classification. The classification accuracy obtained always depends on the effectiveness of the texture model, distance measure and classification algorithm used. In this work, texture features are extracted using the proposed multivariate descriptor, MFTM/MVAR that uses Multivariate Fuzzy Texture Model (MFTM) supplemented with Multivariate Variance (MVAR). The K_Nearest Neighbour (KNN) algorithm is used for classification due to its simplicity coupled with efficiency. The distance measures such as log likelihood, Manhattan, Chi squared, Kullback Leibler and Bhattacharyya were used and the experiments were conducted on IRS P6 LISS-IV data. The classified images were evaluated based on error matrix, classification accuracy and Kappa statistics. From the experiments, it is found that log likelihood distance with MFTM/MVAR descriptor and KNN classifier gives 95.29% classification accuracy.

Keywords

Land Cover Classification, Kullback Leibler, Log Likelihood, Chi Squared, Bhattacharyya.

Full Text

     

C. Jenifer Grace Giftlin ¹, S. Jenicka ¹

Affiliations
1 Department of Computer Science and Engineering, Sethu Institute of Technology, IN

Abstract

The content based image retrieval (CBIR) of remotely sensed (RS) images is vital in the era of processing huge numbers of remotely sensed images. The paper implements a method for CBIR using HSV histograms for retrieving closely matching images from the database and a texture based strategy for forest fire detection. In texture based strategy Gray level co-occurrence Matrix (GLCM) has been used in combination with Feed Forward Neural Network to detect forest fire. The results presented in this paper were obtained through conducting experiments on IRS P6 AWiFS satellite images downloaded from Internet.

Keywords

Remote Sensing, Histogram, Feature Extraction, Feature Vector, AWiFS.

Full Text