Siamese Network Ensembles for Hyperspectral Target Detection with Pseudo Data Generation

Target detection in hyperspectral images (HSIs) aims to distinguish target pixels from the background using knowledge gleaned from prior spectra. Most traditional methods are based on certain assumptions and utilize handcrafted classifiers. These simple models and assumptions’ failure restrict the detection performance under complicated background interference. Recently, based on the convolutional networks, many supervised deep learning detectors have outperformed the traditional methods. However, these methods suffer from unstable detection, heavy computation burden, and optimization difficulty. This paper proposes a Siamese fully connected based target detector (SFCTD) that comprises nonlinear feature extraction modules (NFEMs) and cosine distance classifiers. Two NFEMs, which extract discriminative spectral features of input spectra-pairs, are based on fully connected layers for efficient computing and share the parameters to ease the optimization. To solve the few samples problem, we propose a pseudo data generation method based on the linear mixed model and the assumption that background pixels are dominant in HSIs. For mitigating the impact of stochastic suboptimal initialization, we parallelly optimize several Siamese detectors with small computation burdens and aggregate them as ensembles in the inference time. The network ensembles outperform every detector in terms of stability and achieve an outstanding balance between background suppression and detection rate. Experiments on multiple data sets demonstrate that the proposed detector is superior to the state-of-the-art detectors.