As remote sensing (RS) images increase dramatically, the demand for remote sensing image retrieval (RSIR) is growing, and has received more and more attention. The characteristics of RS images, e.g., large volume, diversity and high complexity, make RSIR more challenging in terms of speed and accuracy. To reduce the retrieval complexity of RSIR, a hashing technique has been widely used for RSIR, mapping high-dimensional data into a low-dimensional Hamming space while preserving the similarity structure of data. In order to improve hashing performance, we propose a new hash learning method, named low-rank hypergraph hashing (LHH), to accomplish for the large-scale RSIR task. First, LHH employs a l2-1 norm to constrain the projection matrix to reduce the noise and redundancy among features. In addition, low-rankness is also imposed on the projection matrix to exploit its global structure. Second, LHH uses hypergraphs to capture the high-order relationship among data, and is very suitable to explore the complex structure of RS images. Finally, an iterative algorithm is developed to generate high-quality hash codes and efficiently solve the proposed optimization problem with a theoretical convergence guarantee. Extensive experiments are conducted on three RS image datasets and one natural image dataset that are publicly available. The experimental results demonstrate that the proposed LHH outperforms the existing hashing learning in RSIR tasks.

This paper suggests a thin cloud removing approach of color remote sensing image based on support vector machine in HSI color model. The intensity component is decomposed in multi-scale by using strong edge capture ability of support vector machine, and the coefficients in different scales are obtained. Then abundant high frequency information is obtained by combining with directional filter bank. Reconstructed image is obtained through enhancing coefficients of high frequency and suppressing coefficients of low frequency. The saturation component is processed by exponential expand method, while the hue component is invariant.Experiments show that thin cloud can be removed efficiently by using the method introduced in this paper.

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