Enhancing Remote Sensing Image Change Detection and Security With Stacked U-Net

Abstract

Change detection (CD) and security plays a crucial role in remote sensing applications. The proposed change detection approach focuses on detecting the changes in synthetic aperture radar (SAR) images. The SAR images suffer from speckle noise which affects the classification accuracy. The proposed approach focuses on improving the model's accuracy by removing speckle noise with k-means clustering and an improved threshold approach based on curvelet transform and designing a stacked U-Net model. The stacked U-Net is designed with the help of a 2-dimensional convolutional neural network (2D-CNN). The proposed change detection strategy is evaluated via performing extensive experiments on three SAR datasets. The obtained results reveal that the proposed approach achieves better results than the several state-of-art works in terms of percentage of correct classification (PCC), overall error (OE), and kappa coefficient (KC).