Fully Automated Generation of Cloud-free Imagery Using Landsat-8

Deep Learning Model Analysis of Drone Images for Unauthorized Occupancy Detection of River Site

“…In a previous study, Kim and Choi (2018) proposed a GLCM (Gray Level Co-occurrence Matrix) -based approach to classify farmland in the reservoir area. They used texture information along with NDWI and NDVI as additional features in the classification process.…”

“…(2023) established the viability of detecting illegal logging sites in river basins using YOLOv5 and DeepLabv3+. Other studies have proven the effectiveness of using techniques such as GLCMbased supervised classification (Kim and Choi, 2018) and highresolution drone images (Lee et al, 2018) for recognizing and exploring unauthorized facilities occupying public lands. The use of deep learning, including YOLOv5, is also widespread in object classification for land use status analysis (Kim, 2022) and has recently been used to discern coastal fixed waste (Park et al, 2020) and marine litter issues (Wang et al, 2021).…”

Determination of aquaculture distribution by using remote sensing technology in Thanh Phu district, Ben Tre province, Vietnam

“…Thin cloud was masked using bands 1, 9 and 10 using only the low values in both bands 9 and 10. Cloud is also normally brighter than the other objects, especially in the blue band, which is given a result in high pixel values on band 1 [8]. The cloudy values were used to create cloud mask in each image; cloud pixels were subsequently deleted by the cloud mask and filled values by multi-time images.…”