River detection algorithm in SAR images based on edge extraction and ridge tracing techniques

Sun, Jinping; Mao, Shiyi

doi:10.1080/01431161003749477

Cited by 16 publications

(8 citation statements)

References 10 publications

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“…Structural methods that look for longitudinally continuous curvilinear dark objects in SAR images have been proposed for a number of detection tasks, including that of rivers. Sun and Mao [22] segment rivers in SAR images based on their structural characteristics following a concept borrowed from fingerprint identification that involves performing edge detection in the wavelet domain followed by ridge tracing to enforce connectivity. Yang et al [23] use Gabor filters to first enhance river cross-sections then followed by a pathopening morphological operation to enforce connectivity of longitudinal segments.…”

Section: Related Workmentioning

confidence: 99%

River Planform Extraction From High-Resolution SAR Images via Generalized Gamma Distribution Superpixel Classification

Pappas

Anantrasirichai

Achim

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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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.

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Section: Related Workmentioning

confidence: 99%

River Planform Extraction From High-Resolution SAR Images via Generalized Gamma Distribution Superpixel Classification

Pappas

Anantrasirichai

Achim

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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“…Its detection is often used as a primitive to achieve higher-level goals in many application systems (Jin, Wang, and Li 2014;Li et al 2014;Sun and Mao 2011;Dias, CribariNeto, and Ospina 2014). Examples are image segmentation, pattern recognition, and computer vision.…”

Section: Introductionmentioning

confidence: 98%

“…Hence, to efficiently prevent false edges, the filter with a low side lobe is also required. According to the filter theory (Simon 2002), we think the PMG is suitable for SAR images. In the following, the impact of the high side lobe on the detection performance is testified by experiments.…”

mentioning

confidence: 99%

An efficient SAR edge detector with a lower false positive rate

Qiao

Feng

2015

International Journal of Remote Sensing

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An efficient ratio-based edge detector (RBED) is proposed for synthetic aperture radar (SAR) images to decrease the false positive detection rate. Low false rate is achieved by taking full advantage of the integrative information associated with multiple adjacent edge-pixels. The RBED introduces a novel 2D separable filter. The 2D filter is represented as the direct product between a 1D vertical filter and a 1D parallel filter. The vertical filter is heuristically designed as the product of a power function and a Gaussian function, simply called the power-modulated-Gaussian function with a weaker tail. Under the non-ideal edge model, this power-modulated-Gaussian vertical filter keeps the false positive rate low and provides a good localization precision. The parallel filter is set as a flat bell-shaped function that is designed by taking a good trade-off between the Gaussian function and the gate function. Such a flat bell-shaped parallel filter with two flexible parameters can be properly adjusted so that the false positive rate can further be decreased. The constant false alarm rate and localization bias of the RBED are analysed theoretically. A set of objective and subjective experiments demonstrate that the RBED can markedly decrease the false positive rate and obtain closed skeleton edges.

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“…Based on the GSS filter, Wei proposed an image edge field accumulation (IEFA) straight-line detection method [9]. Aside from the ratio-based detectors, there exist some other techniques, such as the river detection algorithm based on edge extraction and ridge tracing techniques [10], the statistical edge detector based on the square successive difference of averages [11], the edge detector using Crater-Shaped Window (CSW) [12], and the SAR edge detector based on the Shearlet transform and sparse representation [13].…”

Section: Introductionmentioning

confidence: 99%

SAR-PC: Edge Detection in SAR Images via an Advanced Phase Congruency Model

et al. 2017

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Edge detection in Synthetic Aperture Radar (SAR) images has been a challenging task due to the speckle noise. Ratio-based edge detectors are robust operators for SAR images that provide constant false alarm rates, but they are only optimal for step edges. Edge detectors developed by the phase congruency model provide the identification of different types of edge features, but they suffer from speckle noise. By combining the advantages of the two edge detectors, we propose a SAR phase congruency detector (SAR-PC). Firstly, an improved local energy model for SAR images is obtained by replacing the convolution of raw image and the quadrature filters by the ratio responses. Secondly, a new noise level is estimated for the multiplicative noise. Substituting the SAR local energy and the new noise level into the phase congruency model, SAR-PC is derived. Edge response corresponds to the max moment of SAR-PC. We compare the proposed detector with the ratio-based edge detectors and the phase congruency edge detectors. Receiver Operating Characteristic (ROC) curves and visual effects are used to evaluate the performance. Experimental results of simulated images and real-world images show that the proposed edge detector is robust to speckle noise and it provides a consecutive edge response.

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River detection algorithm in SAR images based on edge extraction and ridge tracing techniques

Cited by 16 publications

References 10 publications

River Planform Extraction From High-Resolution SAR Images via Generalized Gamma Distribution Superpixel Classification

River Planform Extraction From High-Resolution SAR Images via Generalized Gamma Distribution Superpixel Classification

An efficient SAR edge detector with a lower false positive rate

SAR-PC: Edge Detection in SAR Images via an Advanced Phase Congruency Model

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