Evaluation of Multi-Source High-Resolution Remote Sensing Image Fusion in Aquaculture Areas

文献类型： 外文期刊

第一作者： Zhou, Weifeng

作者： Zhou, Weifeng;Wang, Xi;Tang, Fenghua;Li, Jiasheng;Wang, Fei;Li, Jiasheng;Wang, Xi

作者机构：

关键词： aquaculture area; high-resolution satellite images; image fusion; quantitative evaluation; CQ(max)

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.838； 五年影响因子：2.921 ）

ISSN：

年卷期： 2022 年 12 卷 3 期

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收录情况： SCI

摘要： Image fusion of satellite sensors can generate a high-resolution multi-spectral image from inputs of a high spatial resolution panchromatic image and a low spatial resolution multi-spectral image for feature extraction and target recognition, such as enclosure seines and floating rafts. However, there is currently no clear and definite method of image fusion for different aquaculture areas distribution extraction from high-resolution satellite images. This study uses three types of high-resolution remote sensing images, GF-1 (Gaofen-1), GF-2 (Gaofen-2), and WV-2 (WorldView-2), covering the raft and enclosure seines aquacultures in the Xiangshan Bay, China, to evaluate panchromatic and multispectral image fusion techniques to determine which is the best. This study applied PCA (principal component analysis), GS (Gram-Schmidt), and NNDiffuse (nearest neighbor diffusion) algorithms to panchromatic and multispectral images fusion of GF-1, GF-2, and WV-2. Two quantitative methods are used to evaluate the fusion effect. The first used seven statistical parameters, including gray mean value, standard deviation, information entropy, average gradient, correlation coefficient, deviation index, and spectral distortion. The second is the CQ(max) index. Comparing the evaluation results by these seven common statistical indicators with the results of the image fusion evaluation by index CQ(max), the results prove that the CQ(max) index can be applied to the evaluation of image fusion effects in different aquaculture areas. For the floating raft cultured area, the conclusion is consentaneous; NNDiffuse was also optimal for GF-1 and GF-2 data, and PCA was optimal for WV-2 data. For the enclosure seines culture area, the conclusion of quantitative evaluations is not consistent and it shows that there is no definite good method that can be applied to all areas; therefore, careful evaluation and selection of the best applicable image fusion method are required according to the study area and sensor images.

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