Robust and Realistic Classification of Massive Gray Level Thresholding in Remote Sensing Images - Balaji, T (International Journal of Computer Sciences and Engineering, 2014)
Thresholding is an important technique for remote sensing image classification that tries to identify and extract a target from its background on the basis of the distribution of gray levels. Most thresholding techniques are based on the statistics of the one-dimensional histogram of gray levels and the two-dimensional co-occurrence matrix of an image. The remote sensing image classification refers to the process of separating an image into the multi gray level classes or features. The main goal of classification is to simply change the representation of an image into something that is more meaningful and easier to analyze. We present a new algorithm based on robust and realistic classification of massive gray level thresholding representation of remote sensing image. This algorithm allows the distribute the number of gray levels for an image in a fine to grainy fashion, starting with the original gray levels present in the image and all the way down to two gray levels that simply create a binary based version of the original image. This algorithm can also be used to find different gray level threshold image in a natural way without forcing a specific number ahead of time. The accuracy of this algorithm can be most demanding part of a computer vision application. In this work, gray level histogram thresholding is proposed in order to help the classification step to found to be robust way regardless of the classification approach. A proposed method over a remote sensing images have shown that offers very good classification results with a low computation time. Our experimental results show that the thresholding method based on gray level optimization is more efficient than the other classical thresholding methods.