LAUSR

Given two multitemporal aerial images, semantic change detection (SCD) aims to locate the land-cover variations and identify their change types with pixelwise boundaries. This problem is vital in many earth vision-related tasks, such as precise urban planning and natural resource management. Existing state-of-the-art algorithms mainly identify the changed pixels by applying homogeneous operations on each input image and comparing the extracted features. However, in changed regions, totally different land-cover distributions often require heterogeneous feature extraction procedures for images acquired at different times. In this article, we present an asymmetric Siamese network (ASN) to locate and identify semantic changes through feature pairs obtained from modules of widely different structures, which involves areas of various sizes and applies different quantities of parameters to factor in the discrepancy across land-cover distributions during different times. To better train and evaluate our model, we create a large-scale well-annotated SEmantic Change detectiON Dataset (SECOND), while an adaptive threshold learning (ATL) module and a separated kappa (SeK) coefficient are proposed to alleviate the influences of label imbalance in model training and evaluation. The experimental results demonstrate that the proposed model can stably outperform the state-of-the-art algorithms with different encoder backbones.