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Given two multi-temporal aerial images, semantic change detection aims tolocate the land-cover variations and identify their change types withpixel-wise boundaries. This problem is vital in many earth vision relatedtasks, such as precise urban planning and natural resource management. Existingstate-of-the-art algorithms mainly identify the changed pixels by applyinghomogeneous operations on each input image and comparing the extractedfeatures. However, in changed regions, totally different land-coverdistributions often require heterogeneous features extraction procedures w.r.teach input. In this paper, we present an asymmetric siamese network (ASN) tolocate and identify semantic changes through feature pairs obtained frommodules of widely different structures, which involve areas of various sizesand apply different quantities of parameters to factor in the discrepancyacross different land-cover distributions. To better train and evaluate ourmodel, we create a large-scale well-annotated SEmantic Change detectiON Dataset(SECOND), while an Adaptive Threshold Learning (ATL) module and a SeparatedKappa (SeK) coefficient are proposed to alleviate the influences of labelimbalance in model training and evaluation. The experimental resultsdemonstrate that the proposed model can stably outperform the state-of-the-artalgorithms with different encoder backbones.

Semantic Change Detection with Asymmetric Siamese Networks