Automatic Semantic Segmentation with DeepLab Dilated Learning Network for Change Detection in Remote Sensing Images

Abstract

Automatic change detection is an interesting research area in remote sensing (RS) technology aims to detect the changes in synthetic aperture radar (SAR) and multi-temporal hyperspectral images acquired at different time intervals. This method identifies the differences between the images and accomplishes the classification result into changed and unchanged areas. However, the existing algorithms are degraded due to noises present in the RS images. The main aim of the proposed method is the automatic semantic segmentation based change detection that produces a final change between the two input images. This paper proposes a feature learning method named deep lab dilated convolutional neural network (DL-DCNN) for the detection of changes from the images. The proposed approach consists of three stages: (i) pre-processing, (ii) semantic segmentation based change detection and (iii) accuracy assessment. Initially, preprocessing is performed to correct the errors and to obtain detailed information from the scene. Then, map the changes between the two images with the help of a trained network. The DCNN network performs fine-tuning and determines the relationship between two images as changed and unchanged pixel areas. The experimental analysis conducted on various datasets and compared with several existing algorithms. The experimental analysis is performed in terms of F-score, percentage correct classification, kappa coefficient, and overall error rate measures to show a better performance measure than the other state-of-art approaches.