dc.description.abstract | With the popularization of multi-sensor applications in remote sensing, computer vision, and many other fields, the fusion of multi-sensor products has become an emerging topic in the community. One of main reasons is the variety of sensors can provide different spatiotemporal images in the same location. Hence, this study aims to compose a panchromatic-sharpened image from heterogenous sensors, and to investigate the performance of the fused image in waterline detection. The workflow is exemplified by Sentinel-2 that has a lower spatial but high temporal resolution, and to merge the data with SPOT-6 that provide much higher spatial resolution in its panchromatic band. We first fuse the panchromatic images of SPOT-6 with the multispectral (NIR-B-G) images of Sentinel-2, by using the Self-similarity Regularized Pansharpening (SimiRegPS) method to fuse the images covering Taoyuan, Taiwan. The self-similarity employed in our design has been extensively examined in natural images as well as in various imaging inverse problems. Following that, the Normalized Difference Water Index Pansharpened (NDWIP) is calculated to identify water pixels. We validate 8 ponds as compared with in situ data from Taoyuan Water Resources Department. The validation includes two scenarios: dry season (scenario 1) and wet season (scenario 2). In scenario 1, the averaged accuracy of waterline in the fused image is between 2.99 m and 8.05 m. In scenario 2, the averaged accuracy of waterline in the fused image is between 2.68 m and 7.52 m. Also, the averaged accuracy of water area in the fused image is 85% and 84%, in contrast to 73% and 72% of the original image in scenario 1 and 2, respectively. To conclude, this research has shown the possibility to effectively extract hydrologic parameters by combining Sentinel-2 with limited SPOT-6 images to obtain the more accurate waterline through SimiRegPS method. | en_US |