Extraction of River Planforms from Synthetic Aperture Radar Imagery using Generalised Gamma Superpixel Classification

Abstract:

Algorithms for the detection and extraction of river planforms from remotely sensed images are of great interest to numerous applications including land planning, water resource monitoring, and flood prediction. Synthetic Aperture Radar (SAR) is a very promising modality for river monitoring and analysis as it can provide high resolution imagery regardless of weather conditions and the day/night cycle.

In this work we present an algorithm for the detection and segmentation of rivers in SAR images, with emphasis on accurate riverbank extraction. The algorithm utilises a novel superpixel segmentation algorithm that segments the image into perceptually uniform clusters of pixels based on a mixture modelling of the SAR image, wherein pixel intensities are modelled using the Generalised Gamma distribution (GGD) – a state-of-the-art statistical model able of capturing the varying heavy-tailed nature of SAR data.

The generated superpixels adhere to the edges of objects in the image (such as riverbanks) with great accuracy. Superpixels are then characterised according to several features that describe their statistical and textural properties as well as the presence of longitudinal structures within, which in turn allows for the discrimination between river- and land-cover superpixels. Such measures include information entropy, the sample median as well as the output response of filter-driven methods like the Multiscale Singularity Index. The river-forming superpixels are then grouped together using unsupervised agglomerative clustering to produce river planform masks.

We demonstrate our proposed method on high resolution SAR images from the SENTINEL-1 and ICEYE platforms. Future work will focus on incorporating more complex heuristics for the identification of false positives and to circumvent apparent river discontinuities (e.g. bridges), as well as on the release of a toolbox providing open access to the geosciences community.