Abstract
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 CQmax index. Comparing the evaluation results by these seven common statistical indicators with the results of the image fusion evaluation by index CQmax, the results prove that the CQmax 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.
Highlights
Recent developments in remote sensing technology offer a high spatial, spectral, and temporal resolution
Fusion results using GF-1, GF-2, and WV-2 data from the seine culture area exhibited improved spatial resolution, compared with the original multispectral data, as seine nets are more visible in the images
For the GF-1, PCA and NNDiffuse results are slightly brighter than the original multispectral images
Summary
Recent developments in remote sensing technology offer a high spatial, spectral, and temporal resolution. Most of the operating Earth observation satellites provide both high-resolution panchromatic (Pan) images and low-resolution multi-spectral (MS) images. It is difficult to acquire high-quality spatial and hyperspectral remote sensing data simultaneously [1]. The image fusion of combining multispectral and panchromatic images to produce an enhanced multispectral image of high spatial resolution can improve the efficiency and robustness of information extraction, thereby improving data. Research on image fusion techniques in remote sensing started as early as the late 1980s and early 1990s [3,4,5,6]. Many fusion techniques have been developed [7,8], such as PCA, GS, and NNDiffuse
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