Potential of nonlocally filtered pursuit monostatic TanDEM-X data for coastline detection

Schmitt, Michael; Baier, Gerald; Zhu, Xiao Xiang

doi:10.1016/j.isprsjprs.2018.12.007

Cited by 13 publications

(15 citation statements)

References 35 publications

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“…Accordingly, a smaller patch window and a larger search window must be set. This non-local filtering approach was found to provide remarkable speckle reduction capabilities while keeping fine edges, which is highly desired for coastline extraction purposes [15]. Hence, it is worth expecting that non-local filtering improves the accuracy of the extracted coastlines over areas calling for a large variability of NRCS.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, speckle filtering based on non-local paradigms is suggested as a way to optimize noise reduction and edge preservation [15]. Non-local filters exploit the significant level of redundancy within a SAR image, i. e., similar patches characterized by the same intensity/scattering features are widely present in the same SAR image.…”

Section: Methodsmentioning

confidence: 99%

“…7 where 0 V V is used as the reference background image. Note that for both local filters a 9⇥9 window size is used, while for the non-local filter the search and patch windows' size has been set to 21⇥21 and 7⇥7, respectively, as suggested in [15]. Fig.…”

Section: Methodsmentioning

confidence: 99%

“…In recent years, non-local filters have been exploited to reduce speckle noise in SAR imagery while preserving fine spatial details [13], [14]. To the best of authors' knowledge, there is only one study that explicitly addressed the use of non-local speckle filters for coastline extraction purposes [15], where single-polarization X-band TanDEM-X pursuit monostatic SAR measurements were considered. Experiments, undertaken on a sandy/rocky coast along the English Channel and a skerry coast over Stockholm (Sweden), suggest that the use of non-local speckle filters can improve the extraction accuracy, (evaluated with respect to manually traced reference coastline), from 1 pixel up to 4 pixels, depending on the complexity of the coastal environment.…”

Section: Introductionmentioning

confidence: 99%

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On the Use of Multipolarization Satellite SAR Data for Coastline Extraction in Harsh Coastal Environments: The Case of Solway Firth

Ferrentino

Buono

Nunziata

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This study deals with coastline extraction using multi-polarization spaceborne Synthetic Aperture Radar (SAR) imagery acquired over coastal intertidal areas. The latter are very challenging environments where mud flats lead to a large variability of normalized radar cross section (NRCS), which may trigger a significant number of false edges during the extraction process. The performance of SAR-based coastline extraction methods that rely on a joint combination of multi-polarization information (either single-or dual-polarization metrics) and speckle filtering (either local and non-local approaches) are analyzed using Global Pointing System (GPS) samples and collocated SAR imagery collected under different incidence angles. Our test site is an intertidal zone with a wetland (i. e., salt marsh) in the Solway Firth, southwest along the Scottish-English border. Experimental results, obtained processing a pair of RadarSAT-2 full-polarimetric and a pair of Sentinel-1 dual-polarimetric SAR imagery augmented by co-located GPS samples, show that: a) the multi-polarization information outperforms the singlepolarization counterpart in terms of extraction accuracy; b) among the single-polarization channels, the cross-polarized one performs best; c) both single-and dual-polarization methods perform better when non-local speckle filtering is applied; d) the joint combination of non-local speckle filter and dual-polarization information provides the best accuracy; e) the incidence angle plays a role in the extraction accuracy with larger incidence angles resulting in the best performance when dual-polarization metric is used.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Use of Multipolarization Satellite SAR Data for Coastline Extraction in Harsh Coastal Environments: The Case of Solway Firth

Ferrentino

Buono

Nunziata

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In addition, pairs of subsequent acquisitions were used to compute coherence estimates, again using NL-SAR for the estimation. The benefit of nonlocal methods is twofold: despeckling improves the robustness for subsequent classification, and they provide a less biased coherence estimate [40] compared to estimators with a smaller window. Finally, all products were geocoded using the 3 s SRTM DEM.…”

Section: Sar Datasetsmentioning

confidence: 99%

Multi-Source Data Fusion Based on Ensemble Learning for Rapid Building Damage Mapping during the 2018 Sulawesi Earthquake and Tsunami in Palu, Indonesia

et al. 2019

Self Cite

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This work presents a detailed analysis of building damage recognition, employing multi-source data fusion and ensemble learning algorithms for rapid damage mapping tasks. A damage classification framework is introduced and tested to categorize the building damage following the recent 2018 Sulawesi earthquake and tsunami. Three robust ensemble learning classifiers were investigated for recognizing building damage from Synthetic Aperture Radar (SAR) and optical remote sensing datasets and their derived features. The contribution of each feature dataset was also explored, considering different combinations of sensors as well as their temporal information. SAR scenes acquired by the ALOS-2 PALSAR-2 and Sentinel-1 sensors were used. The optical Sentinel-2 and PlanetScope sensors were also included in this study. A non-local filter in the preprocessing phase was used to enhance the SAR features. Our results demonstrated that the canonical correlation forests classifier performs better in comparison to the other classifiers. In the data fusion analysis, Digital Elevation Model (DEM)- and SAR-derived features contributed the most in the overall damage classification. Our proposed mapping framework successfully classifies four levels of building damage (with overall accuracy >90%, average accuracy >67%). The proposed framework learned the damage patterns from a limited available human-interpreted building damage annotation and expands this information to map a larger affected area. This process including pre- and post-processing phases were completed in about 3 h after acquiring all raw datasets.

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Review of Segmentation Methods for Coastline Detection in SAR Images

Ciecholewski

2023

Arch Computat Methods Eng

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Synthetic aperture radar (SAR) images acquired by airborne sensors or remote sensing satellites contain the necessary information that can be used to investigate various objects of interest on the surface of the Earth, including coastlines. The coastal zone is of great economic importance and is also very densely populated. The intensive and increasing use of coasts and changes of coastlines motivate researchers to try to assess the pace of these changes. As remote sensing develops, coastlines are detected using various image processing and analysis methods, including segmentation methods. Segmentation is to allow separating water and land areas in order to obtain contours representing the shorelines of coasts. Its result has direct impact on the accuracy of the obtained contours and is one of the most important steps in image processing. This article presents an overview of state-of-the-art segmentation methods used for detecting and extracting coastlines from SAR images, taking into account the evaluation metrics used in them. Segmentation methods can be divided into three main groups: thresholding methods, active contours, and machine learning approaches. This article presents the theoretical and practical properties of individual groups of segmentation methods, their advantages and disadvantages, and also promising research directions. This article is intended to give researchers insight into existing approaches and to help them propose new, better solutions.

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Potential of nonlocally filtered pursuit monostatic TanDEM-X data for coastline detection

Cited by 13 publications

References 35 publications

On the Use of Multipolarization Satellite SAR Data for Coastline Extraction in Harsh Coastal Environments: The Case of Solway Firth

On the Use of Multipolarization Satellite SAR Data for Coastline Extraction in Harsh Coastal Environments: The Case of Solway Firth

Multi-Source Data Fusion Based on Ensemble Learning for Rapid Building Damage Mapping during the 2018 Sulawesi Earthquake and Tsunami in Palu, Indonesia

Review of Segmentation Methods for Coastline Detection in SAR Images

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