Research on Extraction and Subdivision Type Identification of Coastal Mudflat Based on Planet Remote Sensing Data
Based on the daily overpass times of high temporal resolution Planet remote sensing satellite,this paper selects imagery cor-responding to the highest and lowest tides over the past three years for five study areas.The U-Net deep learning algorithm is em-ployed to identify the land-water boundary at high and low tide and extract the mudflat extent in each study area.Coastal mudflat sub-division types are identified through visual interpretation.The results from the five study areas demonstrate that high temporal resolu-tion Planet remote sensing data can be used to rapidly extract coastal mudflat extents by the land-water boundaries at the highest and lowest tides and identify subdivision types of coastal mudflat natural resources.This approach addresses current challenges in subdivi-ding coastal mudflat natural resource units for registration and provides new technical support for physical measurement of coastal mud-flats and natural resource registration in the inventory of natural resource assets.
NETL
NSTL
万方数据
