A piecewise continuous learning method based on a spatial-spectral relational neural networks

Mengbin Rao; Ping Tang; Jianjun Ge

doi:10.1117/12.3012215

21 December 2023 A piecewise continuous learning method based on a spatial-spectral relational neural networks

Mengbin Rao, Ping Tang, Jianjun Ge

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129703V (2023) https://doi.org/10.1117/12.3012215
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

In recent years, with the development of hyperspectral sensor technology, how to accurately capture different classes of ground objects from hyperspectral images in a large area is an urgent problem. This paper proposes a piecewise continuous learning method based on a spatial-spectral relational network, a circular classification process, including image cutting, image classification, and result map mosaic. This method can directly fine-tune each sub-region without modifying the network structure and can infer the whole region's figure class information. Taking Houston dataset as the experimental dataset, this paper verifies the effectiveness of the proposed classification process. Further, it reveals the application value of the proposed method in large-area hyperspectral image classification.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mengbin Rao, Ping Tang, and Jianjun Ge "A piecewise continuous learning method based on a spatial-spectral relational neural networks", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129703V (21 December 2023); https://doi.org/10.1117/12.3012215

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