中国福彩快三网站 Due to the limitations of the sensors, the remote sensing satellite images feature a tradeoff between the spatial, temporal, and spectral resolutions. However, most of the existing methods (multiview fusion, spatio-temporal fusion, and spatio-spectral fusion, etc) have been developed independently, and few studies have been dedicated to studying the relationships between them, thus the fusion frameworks lack versatility. In addition, they are designed to merge the complementary information from one or two sensors, and thus they cannot take full advantage of the useful complementary spatial, temporal, and spectral features information from more sensor observations. In this paper, we propose an integrated framework for the spatio-temporal-spectral fusion of remote sensing images. The proposed framework is based on the maximum a posterior theory, and the integrated spatio-temporal-spectral relationship model is constructed by studying the relations among multisource observations. Finally, the conjugate gradient algorithm is applied for the solution of the fused image. There are two main advantages to the proposed integrated fusion framework: 1) it can accomplish different kinds of fusion tasks, such as multi-view spatial fusion, spatio-spectral fusion, and spatio-temporal fusion, based on a single unified model; and 2) it can achieve the integrated fusion of multi-source observations to obtain high spatio-temporal-spectral resolution images, without limitations on the number of remote sensing sensors. The proposed integrated fusion framework was comprehensively tested and verified in a variety of image fusion experiments. The experimental results confirm the effectiveness of the proposed method. The experimental design and results are shown as follows.
Read More>> H. Shen, X. Meng, and L. Zhang, “An Integrated Framework for the Spatio-Temporal-Spectral Fusion of Remote Sensing Images,” IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(12): 7135.