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Change Detection in Vegetation Cover Using Deep Learning

Because of man-made occasions and regular causes, numerous areas on the land are experiencing quick and wide-running changes in vegetation spread. Vegetation is the significant piece of land spread and its progressions have a significant effect on the vitality and mass biochemical cycles. It is also a key marker of territorial natural condition change. Change discovery alludes to the way toward recognizing contrasts in the condition of a state by watching it at various occasions. The Multi Spectral Remote Sensing pictures are proficient for acquiring a superior comprehension of the earth condition and its changes. A Convolution Neural Network (CNN) plays a prominent role in classifying the images more accurately, compared to other Machine Learning algorithms. This paper concentrates on identifying the vegetation change in Bapatla region of Andhra Pradesh state, India between the years 2015 to 2017 using multi spectral Landsat images and deep learning techniques and the experimental results shown a decrease in vegetation land in approximately 240 square kilo meters.