The remote sensing image data is so vast that it requires compression by low-complexity algorithm on space-borne equipment. Binary tree coding with adaptive scanning order (BTCA) is an effective algorithm for the mission. However, for large-scale remote sensing images, BTCA requires a lot of memory, and does not provide random access property. In this paper, we propose a new coding method based on BTCA and optimize truncation. The wavelet image is first divided into several blocks which are encoded individually by BTCA. According the property of BTCA, we select the valid truncation points for each block carefully to optimize the ratio of rate-distortion, so that a higher compression ratio, lower memory requirement and random access property are attained. Without any entropy coding, the proposed method is simple and fast, which is very suitable for space-borne equipment. Experiments are conducted on three remote sensing image sets, and the results show that it can significantly improve PSNR, SSIM and VIF, as well as subjective visual experience.

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Particle swarm optimization (PSO) algorithm has the ability of global optimization , but it often suffers from premature convergence problem, especially in high-dimensional multimodal functions. In order to overcome the premature property and improve the global optimization performance of PSO algorithm, this paper proposes an improved particle swarm optimization algorithm , called IPSO. The simulation results of eight unimodal/multimodal benchmark functions demonstrate that IPSO is superior in enhancing the global convergence performance and avoiding the premature convergence problem to SPSO no matter on unimodal or multimodal high-dimensional (100 real-valued variables) functions.

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