Exploring a Hybrid Convolutional Framework for Camouflage Target Classification in Land-Based Hyperspectral Images

In recent years, camouflage technology has evolved from single-spectral-band applications to multifunctional and multispectral implementations. Hyperspectral imaging has emerged as a powerful technique for target identification due to its capacity to capture both spectral and spatial information. The advancement of imaging spectroscopy technology has significantly enhanced reconnaissance capabilities, offering substantial advantages in camouflaged target classification and detection. However, the increasing spectral similarity between camouflaged targets and their backgrounds has significantly compromised detection performance in specific scenarios. Conventional feature extraction methods are often limited to single, shallow spectral or spatial features, failing to extract deep features and consequently yielding suboptimal classification accuracy. To address these limitations, this study proposes an innovative 3D-2D convolutional neural networks architecture incorporating depthwise separable convolution (DSC) and attention mechanisms (AM). The framework first applies dimensionality reduction to hyperspectral images and extracts preliminary spectral-spatial features. It then employs an alternating combination of 3D and 2D convolutions for deep feature extraction. For target classification, the LogSoftmax function is implemented. The integration of depthwise separable convolution not only enhances classification accuracy but also substantially reduces model parameters. Furthermore, the attention mechanisms significantly improve the network's ability to represent multidimensional features. Extensive experiments were conducted on a custom land-based hyperspectral image dataset. The results demonstrate remarkable classification accuracy: 98.74% for grassland camouflage, 99.13% for dead leaf camouflage and 98.94% for wild grass camouflage. Comparative analysis shows that the proposed framework is outstanding in terms of classification accuracy and robustness for camouflage target classification.