Hyperspectral Image Classification Using a Superpixel–Pixel–Subpixel Multilevel Network

Tu, Bing; Ren, Qi; Li, Qianming; He, Wangquan; He, Wei

doi:10.1109/tim.2023.3271713

Cited by 18 publications

(1 citation statement)

References 48 publications

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“…Due to the characteristic of "combination of image and spectrum" in hyperspectral images, they contain a much higher degree of ground information. By fully utilizing this feature, accurate classification of ground objects can be achieved [3]. Therefore, hyperspectral remote sensing has been widely applied in urban planning [4], environmental monitoring [5], and precision agriculture [6,7].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Images Weakly Supervised Classification with Noisy Labels

Liu,

Zhao,

2023

Remote Sensing

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The deep network model relies on sufficient training samples to achieve superior processing performance, which limits its application in hyperspectral image (HSI) classification. In order to perform HSI classification with noisy labels, a robust weakly supervised feature learning (WSFL) architecture combined with multi-model attention is proposed. Specifically, the input noisy labeled data are first subjected to multiple groups of residual spectral attention models and multi-granularity residual spatial attention models, enabling WSFL to refine and optimize the extracted spectral and spatial features, with a focus on extracting clean samples information and reducing the model’s dependence on labels. Finally, the fused and optimized spectral-spatial features are mapped to the multilayer perceptron (MLP) classifier to increase the constraint of the model on the noisy samples. The experimental results on public datasets, including Pavia Center, WHU-Hi LongKou, and HangZhou, show that WSFL is better at classifying noise labels than excellent models such as spectral-spatial residual network (SSRN) and dual channel residual network (DCRN). On Hangzhou dataset, the classification accuracy of WSFL is superior to DCRN by 6.02% and SSRN by 7.85%, respectively.

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