Two-Stream Convolutional Networks for Hyperspectral Target Detection

In this article, a two-stream convolutional network-based target detector (denoted as TSCNTD) for hyperspectral images is proposed. The TSCNTD utilizes the two-stream convolutional networks to extract abundant spectral information in hyperspectral images. For the background samples, the TSCNTD finds enough typical background pixels via a hybrid sparse representation and classification-based pixel selection strategy in the entire image. To tackle the problem under limited target samples, a novel synthesis method is proposed to generate sufficient target samples with a target priori and some typical background pixels. Once the target and background samples are obtained, then the designed two-stream convolutional networks were trained with a target priori, target samples, and background samples. During training, a target priori and a target sample, which construct a positive training sample, are considered as two inputs of the two-stream convolutional networks, while a target priori and a background sample construct a negative training sample. During testing, the test samples, which are constructed by a target priori and the detected pixels, are classified by the well-trained network. The outputs of the network constitute the final detection result of the TSCNTD. Extensive experiments were made on four benchmark hyperspectral images. The experimental results indicate that the TSCNTD can achieve superior performances in target detection.