Blending Ultra Spectral Images of Multi-Source Remote Sensors

Images and procedures from remote sensing are effective tools for studying the earth's surface. Data quality is essential for improving remote sensing applications and producing crisp, noise-free images. Due to the different gathering methods, obtaining a free set of data is quite challenging in most cases. So, picture or information fusion is crucial in far-flung sensing applications. Spatiotemporal fusion (STF) is a method for fusing pix with the proper temporal and spatial decision by integrating (temporally dense) coarse-resolution pictures with (temporally sparse) fine-resolution pictures. This paper makes a specialty of enforcing spatiotemporal fusion of multi-supply remote sensing pictures. In this paper, STF of multi-source remote sensing images, specifically Landsat and Sentinel sensors images is performed using the ESTARFM fusion method. In total 2 experiments were conducted with Landsat 7, Landsat 8, and Sentinel 2 data. In experiment 1 Landsat 7, and Sentinel 2 images are considered fine and coarse resolution images respectively, and in experiment 2 Landsat 8, and Sentinel 2 as fine and coarse resolution images. The metrics suggest that by applying the STF method, the similarity between the fused image and the original image at the prediction time of experiment 2 is more when compared to the corresponding image results of experiment 1.