Decision Fusion of Textural Features Derived From Polarimetric Data for Levee Assessment

Cui, Minshan; Prasad, Saurabh; Mahrooghy, Majid; Aanstoos, James V.; Lee, M. A.; Bruce, Lori

doi:10.1109/jstars.2012.2195713

Cited by 17 publications

(19 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The texture features extracted from the VV polarization were used in addition to the use of VV polarization in order to extend the radar feature space. Gray level co-occurrence (GLCM) matrix features are among the most widely used methods for textural extraction in remotely sensed images [50][51][52][53]. GLCM analysis is a second-order statistical tool generally adopted to describe texture in imagery.…”

Section: Feature Extractionmentioning

confidence: 99%

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

et al. 2018

View full text Add to dashboard Cite

This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried out at five different sites with dominant vegetation cover located in Kenya. In the initial stage of the process, different features are extracted from single polarization mode (VV polarization) SAR and optical data. Subsequently, proper selection of the relevant features is conducted on the extracted features. An advanced state-of-the-art machine learning regression approach, the support vector regression (SVR) technique, is used to retrieve soil moisture. This paper takes a new look at soil moisture retrieval in vegetated areas considering the needs of practical applications. In this context, we tried to work at the object level instead of the pixel level. Accordingly, a group of pixels (an image object) represents the reality of the land cover at the plot scale. Three approaches, a pixel-based approach, an object-based approach, and a combination of pixel-and object-based approaches, were used to estimate soil moisture. The results show that the combined approach outperforms the other approaches in terms of estimation accuracy (4.94% and 0.89 compared to 6.41% and 0.62 in terms of root mean square error (RMSE) and R 2 ), flexibility on retrieving the level of soil moisture, and better quality of visual representation of the SMC map.

show abstract

Section: Feature Extractionmentioning

confidence: 99%

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Cui et al [30] investigated landslides in earthen levees by means of a multi-classifier decision framework for textural features (grey level co-occurrence matrix) derived from multi-polarized SAR imagery.…”

Section: Introductionmentioning

confidence: 99%

“…Except for [30,31], all aforementioned landslide detection procedures are based on change detection approaches of pre-and post-event VHR SAR imagery, requiring identical imaging geometries Remote Sens. 2016, 8, 307 3 of 20 of both acquisitions.…”

Section: Introductionmentioning

confidence: 99%

Landslide Mapping in Vegetated Areas Using Change Detection Based on Optical and Polarimetric SAR Data

2016

View full text Add to dashboard Cite

Abstract:Mapping of landslides, quickly providing information about the extent of the affected area and type and grade of damage, is crucial to enable fast crisis response, i.e., to support rescue and humanitarian operations. Most synthetic aperture radar (SAR) data-based landslide detection approaches reported in the literature use change detection techniques, requiring very high resolution (VHR) SAR imagery acquired shortly before the landslide event, which is commonly not available. Modern VHR SAR missions, e.g., Radarsat-2, TerraSAR-X, or COSMO-SkyMed, do not systematically cover the entire world, due to limitations in onboard disk space and downlink transmission rates. Here, we present a fast and transferable procedure for mapping of landslides, based on change detection between pre-event optical imagery and the polarimetric entropy derived from post-event VHR polarimetric SAR data. Pre-event information is derived from high resolution optical imagery of Landsat-8 or Sentinel-2, which are freely available and systematically acquired over the entire Earth's landmass. The landslide mapping is refined by slope information from a digital elevation model generated from bi-static TanDEM-X imagery. The methodology was successfully applied to two landslide events of different characteristics: A rotational slide near Charleston, West Virginia, USA and a mining waste earthflow near Bolshaya Talda, Russia.

show abstract

“…Cui et al [22] have implemented a multi-classifier decision fusion framework for levee health monitoring using texture features derived from the grey level co-occurrence matrix. Levee slump slide detection was performed by Omni-directional GLCM texture analysis which has been conducted on the re-sampled images using Rubber Band Straightening Transform …”

Section: Sar Data -Machine Learningmentioning

confidence: 99%

Earthen levee slide detection via automated analysis of synthetic aperture radar imagery

2015

Self Cite

View full text Add to dashboard Cite

show abstract

Decision Fusion of Textural Features Derived From Polarimetric Data for Levee Assessment

Cited by 17 publications

References 19 publications

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

Landslide Mapping in Vegetated Areas Using Change Detection Based on Optical and Polarimetric SAR Data

Earthen levee slide detection via automated analysis of synthetic aperture radar imagery

Contact Info

Product

Resources

About