Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 785-790, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-785-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
13 Sep 2017
COMPRESSIVE SENSING IMAGE FUSION BASED ON PARTICLE SWARM OPTIMIZATION ALGORITHM
X. Li1, J. Lv1, S. Jiang1, and H. Zhou2 1School of geomatics and urban spatial information, Beijing University of Civil Engineering and Architecture, 102616 Beijing, China
2School of medicine, Shanghai Jiao Tong University, 200240 Shanghai, China
Keywords: Particle swarm optimization, Compressive sensing, HIS-CS image fusion, Fusion coefficient, self-adaptability Abstract. In order to solve the problem that the spatial matching is difficult and the spectral distortion is large in traditional pixel-level image fusion algorithm. We propose a new method of image fusion that utilizes HIS transformation and the recently developed theory of compressive sensing that is called HIS-CS image fusion. In this algorithm, the particle swarm optimization algorithm is used to select the fusion coefficient ω. In the iterative process, the image fusion coefficient ω is taken as particle, and the optimal value is obtained by combining the optimal objective function. Then we use the compression-aware weighted fusion algorithm for remote sensing image fusion, taking the coefficient ω as the weight value. The algorithm ensures the optimal selection of fusion effect with a certain degree of self-adaptability. To evaluate the fused images, this paper uses five kinds of index parameters such as Entropy, Standard Deviation, Average Gradient, Degree of Distortion and Peak Signal-to-Noise Ratio. The experimental results show that the image fusion effect of the algorithm in this paper is better than that of traditional methods.
Conference paper (PDF, 1771 KB)


Citation: Li, X., Lv, J., Jiang, S., and Zhou, H.: COMPRESSIVE SENSING IMAGE FUSION BASED ON PARTICLE SWARM OPTIMIZATION ALGORITHM, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 785-790, https://doi.org/10.5194/isprs-archives-XLII-2-W7-785-2017, 2017.

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