Statistical analysis and modeling of TerraSAR-X images for CFAR based target detection

Ni, Weijian; Yan, Weidong; Wu, Junzheng; Zheng, Gang; Lü, Ying

doi:10.1109/igarss.2013.6723197

Cited by 1 publication

(2 citation statements)

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“…The frontal line extraction algorithm consists of three steps: SO (Smallest of)-CFAR for binary classification [26][27][28][29], morphological image processing [30,31], and maximal cumulative based frontal point extraction [32]. CFAR detectors are adaptable threshold detectors that use various statistical models to detect target returns from the ice shelf against the background clutter, such as sea ice and ocean.…”

Section: Methodsmentioning

confidence: 99%

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Analysis of Temporal and Spatial Variability of Fronts on the Amery Ice Shelf Automatically Detected Using Sentinel-1 SAR Data

2021

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The Amery Ice Shelf (AIS) dynamics and mass balance caused by iceberg calving and basal melting are significant in the ocean climate system. Using satellite imagery from Sentinel-1 SAR, we monitored the temporal and spatial variability of the frontal positions on the Amery Ice Shelf, Antarctica, from 2015 to 2021. In this paper, we propose an automatic algorithm based on the SO-CFAR strategy and a profile cumulative method for frontal line extraction. To improve the accuracy of the extracted frontal lines, we developed a framework combining the Constant False Alarm Rate (CFAR) and morphological image-processing strategies. A visual comparison between the proposed algorithm and state-of-the-art algorithm shows that our algorithm is effective in these cases including rifts, icebergs, and crevasses as well as ice-shelf surface structures. We present a detailed analysis of the temporal and spatial variability of fronts on AIS that we find, an advance of the AIS frontal line before the D28 calving event, and a continuous advance after the event. The study reveals that the AIS extent has been advanced at the rate of 1015 m/year. Studies have shown that the frontal location of AIS has continuously expanded. From March 2015 to May 2021, the frontal location of AIS expanded by 6.5 km; while the length of the AIS frontal line is relatively different after the D28 event, the length of the frontal line increased by about 7.5% during 2015 and 2021 (255.03 km increased to 273.5 km). We found a substantial increase in summer advance rates and a decrease in winter advance rates with the seasonal characteristics. We found this variability of the AIS frontal line to be in good agreement with the ice flow velocity.

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Section: Methodsmentioning

confidence: 99%

“…The ice-shelf areas in Sentinel-1 SAR imagery can be detected using the CFAR method [26]. For CFAR modeling, we first model the distribution of the SAR backscatter using Weibull distribution since it is suitable for ocean and ice clutter modeling [27].…”

Section: Ice Shelf Detection Using Cfar Methodsmentioning

confidence: 99%