A Lightweight Convolutional Neural Network Based on Joint Correlation Distance Constraints and Density Peak Clustering for Hyperspectral Target Detection

Deep learning can fully exploit the potential information of data, which facilitates effective target-background separation for hyperspectral target detection (HTD). The deep learning model usually requires a large number of labeled samples yet the available prior target spectra in the hyperspectral image (HSI) are extremely limited. In addition, network training also requires reliable input samples to ensure that the trained model has stronger data discrimination, but current detection methods often suffer from the problem that the background samples are impure. To address the above problems, we propose a lightweight convolutional neural network based on joint correlation distance constraints and density peak clustering (LCNN-CD) for HTD. First, the correlation distance between the prior target and HSI is calculated, and the pixels with high correlation are retained for expanding the target sample, which effectively handles the problem of insufficient target samples. Second, the density peak clustering algorithm is used to extract the main pure background samples, which effectively solves the problem that the background samples are sufficiently impure. Finally, a lightweight convolutional neural network model is designed, which is fed by the training samples (both target and background samples) to obtain the final detection results with low computational efficiency. The proposed method is compared with the classical and recently popular HTD methods on three real HSI datasets. Numerous experiments show that the proposed LCNN-CD method has better target detection performance and effectiveness.