Zhijie, C.; Yu, C.; Yuan, W.; Xiaoyan, W.; Xinsheng, W.; Zhouru, X. DCFF-Net: Deep Context Feature Fusion Network for High-Precision Classification of Hyperspectral Image. Preprints2024, 2024070199. https://doi.org/10.20944/preprints202407.0199.v1
APA Style
Zhijie, C., Yu, C., Yuan, W., Xiaoyan, W., Xinsheng, W., & Zhouru, X. (2024). DCFF-Net: Deep Context Feature Fusion Network for High-Precision Classification of Hyperspectral Image. Preprints. https://doi.org/10.20944/preprints202407.0199.v1
Chicago/Turabian Style
Zhijie, C., Wang Xinsheng and Xiang Zhouru. 2024 "DCFF-Net: Deep Context Feature Fusion Network for High-Precision Classification of Hyperspectral Image" Preprints. https://doi.org/10.20944/preprints202407.0199.v1
Abstract
Hyperspectral images (HSI) contain abundant spectral information. Efficient extraction and utilization of this information for image classification remain prominent research topics. Previously, hyperspectral classification techniques primarily relied on statistical attributes and mathematical models of spectral data. Deep learning classification techniques have recently been extensively utilized for hyperspectral data classification, yielding promising outcomes. This study proposes a deep learning approach that uses polarization feature maps for classification. Initially, the polar coordinate transformation method is employed to convert the spectral information of all pixels in the image into spectral feature maps. Subsequently, the proposed Deep Context Feature Fusion Network (DCFF-NET) is utilized to classify these feature maps. The model is validated using three open-source hyperspectral datasets: Indian Pines, Pavia University, and Salinas. The experimental results indicated that DCFF-NET achieves excellent classification performance. Experimental results on three public HSI datasets demonstrate that the proposed method accurately recognizes different objects with an overall accuracy (OA) of 86.68%, 94.73%, and 95.14% based on the pixel method, and 98.15%, 99.86%, and 99.98% based on the pixel-patch method.
Keywords
deep learning; hyperspectral images; classification; hyperspectral feature map
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
