A neural network based classification of satellite images for change detection applications

Detecting changes on the earth surface are vital to predict and avoid several catastrophes being occurring. In many situations, change detection techniques aids in detecting such changes being taking place. The changes can be noticed from different kinds of low- and high-resolution satellite images of multi-spectral and multi-temporal images. There are different kinds of change detection techniques to observe changes in the images, like principal component analysis method, spectral change vector analysis, post-classification method, kernel method, etc. Machine learning (post-classification) method based change detection provides better accuracy, because these methods are based on pixel comparison in multi-temporal satellite images. The change detection accuracy depends on the classifier used for classification of multi-temporal images. Any misclassification in either images leads to poor detection in change, hence classifier selection is very important in this case. In the recent past, the performance of classification techniques is improved by combining the advantages of some of the classifiers as ensemble methods. In this work, ensemble based classifier is explored for images classification. Different parameters are considered to estimate the performance of ensemble based classifier. Finally, the changed pixels in two temporal images are observed and listed. The images are also shown in zoomed for easy observation of changes in the images.