Abstract
Remote sensing images with high spatial and temporal resolution (HSHT) for GIS land use monitoring are crucial data sources. When trying to get HSHT resolution images, cloud cover is a typical problem. The effects of cloud cover reduction using the ESTARFM, one of spatiotemporal image fusion technique, is examined in this study. By merging two satellite photos of low-resolution and medium-resolution images, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Method (ESTARFM), predicts the reflectance value of the cloud cover region. ESTARFM, on the other hand, employs both medium and high-resolution satellite pictures in this study. Using Sentinel 2 and Landsat 8, the Peak Signal Noise Ratio (PSNR) statistical methods are then utilized to evaluate the ESTARFM. The PSNR explain ESTARFM cloud removal performance by comparing the level of similarity of the reference image with the reconstructed image. In remote sensing, this hypothesis was established to get high-quality HSHT pictures. Based on this study, Landsat 8 images that have been cloud removed with ESTARFM may be classed as good. The PSNR value of 21.8 to 26 backs this up, and the ESTARFM result seems good on visual examination.
Highlights
An effective way to monitor land-use changes on the earth's surface is remote sensing technology [1]
Cloud removal has become a hot issue in the RS community, as it expands the applications and research that optical RS data may be used in Geographic Information System (GIS)
There can be a systematic bias in surface reflection across various sensor images due to sensor system variations such as orbital parameters, bandwidth, acquisition time, and spectral response function. This condition was the primary purpose of ESTARFM to utilize the correlation of various satellite sensor data
Summary
An effective way to monitor land-use changes on the earth's surface is remote sensing technology [1]. A grave challenge to maximize the usage of small sensing images (RS) is the cloud problem. Satellite sensors during the acquisition process are unable to capture passive radiation energy in cloud-covered areas, resulting in missed information in satellite images [2]. Electromagnetic waves from ground features are blocked from reaching the sensor system by the cloud. It substantially limits knowledge gained from optical RS images, putting a halt to future analysis. Cloud removal has become a hot issue in the RS community, as it expands the applications and research that optical RS data may be used in Geographic Information System (GIS)
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