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Extinction profile (EP) is an effective feature extraction method which can well preserve the geometrical characteristics of a hyperspectral image (HSI) and by extracting the EP from first three independent components (ICs) of an HSI, three correlated and complementary groups of EP features can be constructed. In this paper, an EPs fusion (EPs-F) strategy is proposed for HSI classification by exploring spatial-spectral information within and among three EP features. In general, the EPs-F method includes two stages. In the first stage, within each EP feature, a superpixel-based composite kernel strategy is proposed to adaptively fuse the spatial information of EP and the spectral feature of HSI. Then, the obtained adaptive composite kernel is used to create a classification map for each EP. In the second stage, decision fusion is further applied on different classification maps to create the final classification result. Experiments on two real HSIs verify the effectiveness of the proposed EPs-F algorithm.

As the data acquisition capabilities of Earth observation (EO) satellites have been improved substantially in the past few years, large amount of high-resolution satellite images are downlinked continuously to ground stations. Such amount of data increases rapidly beyond the users' capability to access the images' content in reasonable time. Hence, automatic and fast interpretation of a large data volume is a computationally intensive task. Recently, approximate nearest neigbhour search has been used for content-based image retrieval in sublinear time. Kernelized locality sensitive hashing (KLSH) is a well-known approximate method, which has recently shown promising results for fast remote sensing image retrieval. This paper proposes a novel parallelization of KLSH using Graphical Processing Units (GPU), in order to perform fast parallel image retrieval. The proposed method was tested on high-dimensional feature vectors from two satellite-based image datasets, where an average speedup of 20 times was achieved.