Deep Residual Learning for Hyperspectral Imaging Camouflage Detection with SPXY-Optimized Feature Fusion Framework

Camouflage detection in hyperspectral imaging is hindered by the spectral similarity between artificial materials and natural vegetation. This study proposes a non-destructive classification framework integrating optimized sample partitioning, spectral preprocessing, and residual deep learning to address this challenge. Hyperspectral data of camouflage fabrics and natural grass (389.06–1005.10 nm) were acquired and preprocessed using principal component analysis, standard normal variate (SNV) transformation, Savitzky–Golay (SG) filtering, and derivative-based enhancement. The Sample set Partitioning based on joint X–Y distance (SPXY) algorithm was applied to improve representativeness of training subsets, and several classifiers were constructed, including support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN), convolutional neural network (CNN), and residual network (ResNet). Comparative evaluation demonstrated that the SPXY-ResNet model achieved the best performance, with 99.17% accuracy, 98.89% precision, and 98.82% recall, while maintaining low training time. Statistical analysis using Kullback–Leibler divergence and similarity measures confirmed that SPXY improved distributional consistency between training and testing sets, thereby enhancing generalization. The confusion matrix and convergence curves further validated stable learning with minimal misclassifications and no overfitting. These findings indicate that the proposed SPXY-ResNet framework provides a robust, efficient, and accurate solution for hyperspectral camouflage detection, with promising applicability to defense, ecological monitoring, and agricultural inspection.