Land Cover Change Detection Based on Satellite Images Using Deep Stacking Networks

摘要

Remote sensing provides timely and reliable information about urban areas and their changing patterns. Detection of urban change is an important factor that affects the supply of food supply, water supply, etc. Detection of land use and land change is essential in order to understand the current situation and plan for the future to avoid scarcity. In this work, we proposed two new framework called Tensor - Deep Stacking Network (TDSN) with back propagation and Deep Stacking Convolution Neural Networks (DS - CNN) for land cover change detection. Changes are detected between the year 2005 and 2015 in the month of September over the Hutong area, Beijing, China. The work has been validated by using Sentinel 2 (4 bands cirrus, water vapor, coastal aerosol, red edge band) and Landsat 8(11 bands) satellite image. All the bands from Landsat −8 image are pan-sharpen to 15m resolution to get more accurate classification result. Bands in the Sentinel −2 satellite image are used to remove cloud present in the raw satellite image. The classification accuracy of proposed work are compared with the traditional classification algorithm Support Vector Machine (SVM). The accuracy of proposed work is 12% is improved in compare to traditional algorithm.