A Self-Adaptive Thresholding Approach for Automatic Water Extraction Using Sentinel-1 SAR Imagery Based on OTSU Algorithm and Distance Block

As an indispensable material for animals, plants and human beings, obtaining accurate water body information rapidly is of great significance to maintain the balance ecosystems ensure normal production life beings. Due its independence time day weather conditions, synthetic aperture radar (SAR) data have been increasingly applied in extraction bodies. However, there a deal speckle noise SAR images, which seriously affect accuracy water. At present, most processing methods are filtering methods, will cause loss detailed information. Based on characteristic side-looking SAR, this paper proposed self-adaptive thresholding approach automatic based OTSU algorithm distance block. In method, whole images were firstly divided into uniform image blocks through layer was produced by orbit. Then, conducted merging blocks. The used obtain threshold classification Jeffries–Matusita (JM) calculated with result. merge continued until separability reached maximum. Subsequently, we started from next block repeat merger, so all processed. Ten study areas around world local Dongting Lake area test feasibility method. comparison five other global segmentation algorithms such as traditional OTSU, MOMENTS, MEAN, ISODATA MINERROR, method obtains highest overall (OA) kappa coefficient (KC), while also demonstrates greater robustness analysis series. findings offer effective improve detection well reducing influence retaining details image.