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This is a Preprint and has not been peer reviewed.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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Authors

Odysseas Pappas, Nantheera Anantrasirichai, Alin Achim, Byron Adams

Abstract

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.

DOI

https://doi.org/10.31223/X53K53

Subjects

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

Keywords

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

Dates

Published: 2020-10-21 13:02

Last Updated: 2020-10-21 20:02

License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
None

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