PyramidMamba: An Effective Hyperspectral Remote Sensing Image Target Detection Network

The lack of prior knowledge is a challenging issue in target detection tasks for hyperspectral remote sensing images. In this paper, we propose an effective network for object detection in hyperspectral remote sensing images. First, through spectral data augmentation methods, all surrounding pixels within a data block are encoded as the transformed spectral signature of the central pixel, thereby constructing a sufficient number of training sample pairs. Subsequently, a backbone network (PyramidMamba) was designed to establish long-term dependencies across the frequency domain and multi-scale dimensions using the Mamba residual module and pyramid wavelet transform module. A residual self-attention module is further developed, integrating self-attention with convolutional operations to enhance feature extraction while improving the network's depth and stability. A backbone network was employed to extract representative vectors from augmented sample pairs, which were then optimized through a spectral contrast head to enhance the distinction between target and background features. Experimental results demonstrate that compared to mainstream algorithms, the proposed algorithm achieves higher detection accuracy and computational efficiency. It successfully learns deep nonlinear feature representations with stronger discriminative power, enabling effective separation of targets from background and delivering state-of-the-art performance.