Adaptive context aggregation network for H2 remote sensing imagery classification

doi:10.11947/j.AGCS.2023.20220237

Abstract

Abstract: High spectral and spatial resolution (H²) remote sensing imagery can achieve more comprehensive and precise attribute recognition of ground objects. However, the details of ground objects are gradually revealed with the significant improvement of the spatial resolution, which makes the H² images show extremely high spectral variability and spatial heterogeneity, and then the phenomenon of the same class with different spectrums occurs in large numbers; the intraclass variance increases significantly. As a result, an adaptive aggregation context network was proposed for H² image classification, which uses a full convolution network with encoder-decoder architecture to achieve global spectrum-spatial fusion. A local-to-global long-distance context module was designed to alleviate the intraclass variance in the encoder module. Then an adaptive context aggregation module was constructed in the decoder module for the adaptive aggregation of local and global context information. ACANet has achieved excellent performance in the WHU-Hi benchmark dataset, and the experiments show that it can sufficiently alleviate the impact of the spatial-spectrum heterogeneity of the H² image in the precise classification.

Key words: hyperspectral image with high spatial resolution, precise classification, deep learning, context information

CLC Number:

P237

HU Xin, WANG Xinyu, ZHONG Yanfei. Adaptive context aggregation network for H² remote sensing imagery classification[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(7): 1175-1186.

References

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Adaptive context aggregation network for H² remote sensing imagery classification

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