Hyperspectral remote sensing technology has significant application value in fields such as forestry ecology and precision agriculture, while also putting forward higher requirements for fine ground object classification. However, although hyperspectral images are rich in spectral information and can improve recognition accuracy, they tend to cause prominent feature redundancy due to their numerous bands, high dimensionality, and spectral mixing characteristics. To address this, this study used hyperspectral images from the ZY1F satellite as a data source and selected Yugan County, Shangrao City, Jiangxi Province as the research area to perform ground object classification research. A classification framework named CWSSNet was proposed, which integrates 3D spectral-spatial features and wavelet convolution. This framework integrates multimodal information us-ing a multiscale convolutional attention module and breaks through the classification performance bottleneck of traditional methods by introducing multi-band decomposition and convolution operations in the wavelet domain. The experiments showed that CWSSNet achieved 74.50\%, 82.73\%, and 84.94\% in mean Intersection over Union (mIoU), mean Accuracy (mAcc), and mean F1-score (mF1) respectively in Yugan County. It also obtained the highest Intersection over Union (IoU) in the classifica-tion of water bodies, vegetation, and bare land, demonstrating good robustness. Additionally, when the training set proportion was 70\%, the increase in training time was limited, and the classification effect was close to the optimal level, indicating that the model maintains reliable performance under small-sample training conditions.
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