River Planform Extraction From High-Resolution SAR Images Via Generalised Gamma Distribution Superpixel Classification

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1109/TGRS.2020.3011209. This is version 1 of this Preprint.


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Odysseas Pappas, Nantheera Anantrasirichai, Alin Achim, Byron Adams


The extraction of river planforms from remotely sensed satellite images is a task of crucial importance to many applications such as land planning, water resource monitoring or flood prediction. In this paper we present a novel framework for the extraction of rivers from Synthetic Aperture Radar (SAR) images, based on superpixel segmentation and subsequent classification. Superpixel segmentation is achieved via a modelling of the image pixels’ amplitudes and spatial coordinates as a finite mixture model, where the Generalised Gamma distribution is used to accurately model a variety of high-resolution SAR scenes. A number of features describing texture and statistics are extracted on a superpixel level, facilitating the identification of river superpixels - planforms are then extracted via unsupervised,
agglomerative clustering thus eliminating the need for labelled training data. We present results of our proposed method on ICEYE-X2 and SENTINEL-1 SAR data demonstrating its ability to produce pixel-accurate river masks.




Computer Engineering, Electrical and Computer Engineering, Engineering, Geology, Geomorphology, Hydrology


Synthetic Aperture Radar Data, Generalised Gamma Distribution, Superpixels, River Detection, Generalised Gamma Distribution, Superpixels, River Detection


Published: 2020-10-21 15:02

Last Updated: 2020-10-21 22:02


CC BY Attribution 4.0 International

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