An Algorithm to More Accurately Classify Land Cover Using Landsat Imagery ~ GIS Lounge
▻https://www.gislounge.com/algorithm-accurately-classify-land-cover-landsat-imagery
Classification of multispectral and hyperspectral data has increasingly become important to detecting land use change. While many algorithms and approaches exist for such classification, improving classification techniques using widely available data such as Landsat #satellite data has largely stalled in recent years.
Recently, Hankui Zhang, from South Dakota State University, has developed a new classification technique that used a large number of images from MODIS, which has 500-meter resolution, and Landsat (30-meter) resolution together. Overall, three years of data were gathered from the Landsat 5, Landsat 7, and #MODIS programs. The research focused on the area covering 20 and 50 degrees north latitude mostly in North America. A future aim is to use the Sentinel 2 series and combine that data to then also obtain a global 30-meter resolution classification. The algorithm can be obtained using an FTP server after obtaining a username and password from Zhang.[1]