Deep Spectral-Spatial Feature Extraction Based on DCGAN for Hyperspectral Image Retrieval

The hyperspectral images are represented as an image cube, which image the same ground object with several tens to hundreds of spectral bands from ultraviolet to the microwave range. Content-Based Image Retrieval (CBIR) for hyperspectral images has been explored in recent years. However, it is still a concern and challenging task to extract a highly descriptive feature to contribute to improve the performance of hyperspectral image retrieval. At present, deep learning is a new research area of machine learning, which can extract more effective deep features by using a cascade of many layers of nonlinear processing units. In this paper, a deep spectral-spatial feature extraction method is proposed based on Deep Convolutional Generative Adversarial Networks (DCGAN) for hyperspectral image retrieval. Firstly, spectral vector is extracted by selecting manually pure pixels from hyperspectral image. Then spatial vector is extracted by selecting manually the neighbor pixels of pure pixels from principal bands after reducing the dimensionality of hyperspectral images with one-bit (1BT) transform. Spectral-spatial vector as training samples is obtained by combining spectral vector with spatial vector by using Vector Stacking (VS) approach. After training DCGAN model, deep spectral-spatial feature is extracted to further apply into hyperspectral retrieval. Experiments are conducted among our method and the three other state-of-the-art methods including endmember extraction using improved Automatic Pixel Purity Index (APPI), spectral and spatial features extraction, and endmember signatures extraction using Endmember Induction Algorithm (EIA). Experimental results on AVIRIS data show that our method can achieve a higher accuracy for hyperspectral image retrieval and further prove our extracted deep spectral-spatial feature has stronger descriptive ability.