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- W3131451942 abstract "High spectral resolution of hyperspectral images allows the detection and classification of materials in the observed images. However, existing research on hyperspectral detection mainly focuses on pixel-level study, partially due to the low spatial resolution in typical earth observation applications. With the development of imaging techniques, high-spatial-resolution hyperspectral data can be obtained and object-level detection is necessary for many applications. In this work, the object-based hyperspectral detection problem is formulated, and a convolutional neural network is then designed based on the specific characteristics of this problem. Moreover, a hyperspectral dataset with over 400 high-quality images for object-level target detection is created. Experimental results validate the proposed framework and show its superior performance." @default.
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- W3131451942 date "2021-01-01" @default.
- W3131451942 modified "2023-10-13" @default.
- W3131451942 title "Object Detection in Hyperspectral Images" @default.
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- W3131451942 doi "https://doi.org/10.1109/lsp.2021.3059204" @default.
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