Wavelet Siamese Network for Change Detection in Remote Sensing Images

Change detection is a technique used to identify semantic differences between co-registered images of the same area captured at different times. However, current methods often overlook the fact that the low-frequency and high-frequency components of these images play distinct roles in change detection. Our method decomposes each feature map into its low-frequency and high-frequency components and then uses an attention mechanism to adjust the contribution of each component to handle different types of changes. Low-frequency information can help detect overall changes, and high-frequency information can enhance the integrity of the change boundaries. Experiments on the LEVIR-CD, WHU-CD and CLCD datasets show that our model outperforms the state-of-the-art method and the ablation study demonstrates that this approach improve the accuracy of the change detection.

1. INTRODUCTION

Remote sensing change detection (CD) aims to analyze the changed area between two images taken at different times in the same region. It plays an important role in earth observation and drives many practical applications, including disaster monitoring, urban planning, vegetation change, and environmental monitoring [1].