Multi-Scale Feature Based Land Cover Change Detection in Mountainous Terrain Using Multi-Temporal and Multi-Sensor Remote Sensing Images

Abstract

Land use and land cover (LULC) change is frequent in mountainous terrain of southern China. Although remote sensing technology has become an important tool for gathering and monitoring LULC dynamics, image pairs can occur scale changes, noises, geometrical distortions, and illuminated variations if these are acquired from different types of sensors (e.g., satellites). Meanwhile, how to design an efficient land cover change detection algorithm that ensures a high detection rate remains a critical and challenging step. To address these problems, we propose a robust multi-temporal change detection framework for land cover change in mountainous terrain which contains the following contributions. i) To transform multi-temporal remote sensing image pairs acquired by different type of sensors into the same coordinate system by image registration, a multi-scale feature description is generated using layers formed via a pretrained VGG network. ii) A gradually increasing selection of inliers is defined for improving the robustness of feature points registration, and ${L_{2}}$ -minimizing estimate ( ${L_{2}E}$ )-based energy optimization is formulated to calculate a reasonable position in a reproducing kernel Hilbert space. iii) Fuzzy C-Means classifier is adopted to generate a similarity matrix between image pair of geometric correction, and a robust and contractive change map is built through feature similarity analysis. Extensive experiments on multi-temporal image pairs taken by different type of satellites (e.g., Chinese GF and Landsat) or small unmanned aerial vehicles are conducted. Experimental results show that our method provides better performances in most cases after comparing with the five state-of-the-art image registration methods and the four state-of-the-art change detection methods.