Unsupervised stepwise extraction of offshore aquaculture ponds using super-resolution hyperspectral images

Abstract

Despite offshore aquaculture brings great economic benefits, it also has destructive effects on the ecological environment of coastal regions. Therefore, the accurate monitoring of offshore aquaculture areas is vital. Existing methods for extracting aquaculture ponds are still limited by the lack of high-resolution hyperspectral imagery and inadequate feature extraction. In this paper, we proposed an unsupervised aquaculture ponds extraction method based on hyperspectral imagery super-resolution, feature fusion and stepwise extraction strategy. First, the resolution of the original hyperspectral imagery is enhanced via a deep learning-based super-resolution method. Then we introduce a feature fusion method by combining multi-dimensional features including elevation, reflectance, biochemistry, principal component analysis, and fast-Fourier transform features to enhance the feature sensitivity to the aquaculture ponds. The aquaculture ponds are finally extracted via a step-wise extraction strategy. Experiments on ZH-1 and GF-7 imageries are conducted, and the experimental results show that our method achieves an OA of 97.9% on the aquaculture pond extraction and that the method can be generalized for the object extraction on multispectral datasets. Ablation experiments show that all three of our innovative modules can effectively improve the extraction accuracy and confirm the generalization of the method.