Insights into Segmentation Methods Applied to Remote Sensing SAR Images for Wet Snow Detection

Abstract

Monitoring variations in the extent of wet snow over space and time is essential for many applications, such as hydrology, mountain ecosystems, meteorology and avalanche forecasting. The Synthetic Aperture Radar (SAR) measurements from the Sentinel-1 satellite help detect wet snow in almost all weather conditions. Most detection methods use a fixed threshold to a winter image ratio with one or two reference images (with no snow or dry snow). This study aimed to explore the potential of image segmentation methods from different families applied to Sentinel-1 SAR images to improve the detection of wet snow over the French Alps. Several segmentation methods were selected and tested on a large alpine area of 100 × 100 km2. The segmentation methods were evaluated over one season using total snow masks from Sentinel-2 optical measurements and outputs from forecasters’ bulletins combining model and in-situ observations. Different metrics were used (such as snow probability, correlations, Hamming distance, and structure similarity scores). The standard scores illustrated that filtering globally improved the segmentation results. Using a probabilistic score as a function of altitude highlights the interest in some segmentation methods, and we show that these scores could be relevant to calibrate the parameters of these methods better.

Characterizing Snow Dynamics in Semi-Arid Mountain Regions with Multitemporal Sentinel-1 Imagery: A Case Study in the Sierra Nevada, Spain

“…Indeed, in these areas, the main characteristics are generally quicker snowpack changes, several melting cycles throughout the year, and perhaps the loss of some of the three theoretical melting phases due to this quick response [3,60]. This higher Mediterranean frequency of accumulation-melting events will have a different characterization compared to those previously analyzed in other temperate regions, such as the Alps [35,43,61]. Therefore, there is still a gap when analyzing wet-snow dynamics in semi-arid regions.…”

Integrating Sigmoid Calibration Function into Entropy Thresholding Segmentation for Enhanced Recognition of Potholes Imaged Using a UAV Multispectral Sensor

“…When imaged using UAV systems, image segmentation becomes the basis for characterizing potholes. Although there are several remote sensing image segmentation methods capable of detecting and characterizing potholes such as spatial-based image segmentation [4], hybrid-technologybased image segmentation [5] and semantic-based image segmentation [6], spectral-based image segmentation has been, and is still, of great interest to the remote sensing community [7]. This method segments an image by analyzing the pixel values [8], through either feature space clustering [9] or thresholding [10].…”