LWCDNet: A Lightweight Fully Convolution Network for Change Detection in Optical Remote Sensing Imagery

Abstract

Change detection (CD) is an important task in remote sensing image processing. The main research goal is to identify whether the target area has changed. Recently, the rise of deep learning has provided many novel methods for change detection, and some excellent models have been proposed. However, most of the available methods have a large number of parameters, and the traditional loss function does not perform well when tackling the problem of unbalanced sample number. In order to solve the above problems, we propose a lightweight fully convolution network for change detection, namely LWCDNet. Specifically, LWCDNet is a typical encoder-decoder structure, and it realizes more detailed information transmission and feature extraction by using the artificial padding convolution (APC) module as the convolution unit of the encoder. In addition, a convolutional block attention module (CBAM) is added between encoder and decoder to boost the model’s performance even more by emphasizing critical information. To deal with the sample imbalance in the change detection task, we propose Lov-wce loss. The experimental results on two actual remote sensing datasets show the effectiveness of LWCDNet.