Lightweight CNN-CEM for Efficient Hyperspectral Target Detection on Resource-Constrained Edge Devices

Efficient target detection in hyperspectral images faces significant deployment challenges on resource-constrained edge platforms due to the large data volume and high computational complexity of detection algorithms. This paper proposes a CEM target detection method based on 1D-CNN feature dimensionality reduction. A lightweight 1D-CNN reduces spectral dimensions from L bands to 16 features, decreasing the core matrix inversion complexity from O(L3) to O(163). Unlike PCA-based dimensionality reduction requiring online eigenvalue decomposition, the proposed approach employs fixed pre-trained weights with simple convolution operations, enabling high parallelizability for FPGA implementation. A Zynq-based PS + PL collaborative acceleration scheme is designed, deploying CNN on the PL side through RTL implementation and CEM on the PS side using double-precision floating-point computation. Experimental validation on multiple hyperspectral datasets demonstrates that the proposed method achieves an AUC of 0.9953 with less than 1% difference compared to traditional CEM, processes 40,000 pixels in approximately 10.8 s, and consumes only 2.067 W, making it suitable for power-sensitive edge applications such as UAV reconnaissance and satellite on-board processing. The system achieves a processing rate of 3704 pixels/s.