Recognition of lithological units in airborne SAR images using new texture features

He, Dong-Chen; Wang, Li

doi:10.1080/01431169008955179

Cited by 14 publications

(8 citation statements)

References 10 publications

(8 reference statements)

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“…For example, He and Wang [37] demonstrated that, for airborne SAR -band data on lithological units, features derived from texture spectrum fared better than the co-occurrence method [68]. An in-depth comparative study of five texture algorithms [2] reported that no universally best feature set, among the five different texture algorithms chosen, was found when the data from six frequency bands (of a TM image of a mountainous region) were used.…”

Section: B Texture Algorithmsmentioning

confidence: 99%

Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices

Soh

Tsatsoulis

1999

IEEE Trans. Geosci. Remote Sensing

1,058

498

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Abstract-This paper presents a preliminary study for mapping sea ice patterns (texture) with 100-m ERS-1 synthetic aperture radar (SAR) imagery. We used gray-level co-occurrence matrices (GLCM) to quantitatively evaluate textural parameters and representations and to determine which parameter values and representations are best for mapping sea ice texture. We conducted experiments on the quantization levels of the image and the displacement and orientation values of the GLCM by examining the effects textural descriptors such as entropy have in the representation of different sea ice textures. We showed that a complete gray-level representation of the image is not necessary for texture mapping, an eight-level quantization representation is undesirable for textural representation, and the displacement factor in texture measurements is more important than orientation. In addition, we developed three GLCM implementations and evaluated them by a supervised Bayesian classifier on sea ice textural contexts. This experiment concludes that the best GLCM implementation in representing sea ice texture is one that utilizes a range of displacement values such that both microtextures and macrotextures of sea ice can be adequately captured. These findings define the quantization, displacement, and orientation values that are the best for SAR sea ice texture analysis using GLCM.

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Section: B Texture Algorithmsmentioning

confidence: 99%

Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices

Soh

Tsatsoulis

1999

IEEE Trans. Geosci. Remote Sensing

1,058

498

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“…Various ground condition factors, such as roughness, soil moisture, and slope, have an influence on the backscatter coefficients (Engman and Wang 1987, He and Wang 1990, Bayer et al 1991, Evans et al 1992. Therefore, if pyroclastic flows or lahars changed the land surfaces so that their SAR backscatter coefficients increased or decreased considerably, then the difference (subtraction of intensity values) between a pair of SAR backscatter coefficient images taken before and after the event might 1932…”

Section: Backscatter Approachmentioning

confidence: 98%

Detection of traces of pyroclastic flows and lahars with satellite synthetic aperture radars

Terunuma¹,

Nishida

Amada

et al. 2005

International Journal of Remote Sensing

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To assist volcanic hazard mitigation, detection of traces of pyroclastic flows and lahars were attempted by using satellite synthetic aperture radars (SAR). If such information can be obtained rapidly, it can help early warnings of the next pyroclastic flows and lahars because these flows often occur repeatedly at similar locations. Using three analytical approaches, two satellite SAR data, namely, the L-band SAR of the Japanese Earth Resource Satellite (JERS-1) and the C-band SAR of the European Remote Sensing Satellite (ERS-1), were tested. These approaches include subtraction of SAR backscatter coefficients, SAR coherence, and SAR interferometry (InSAR). These approaches were validated in the Unzen Volcano with digital elevation model (DEM) subtraction images created from aerial photographs. As a result, it was found that the coherence approach with JERS-1 SAR was highly capable of detecting the traces of the pyroclastic flows and lahars. The traces appeared as either one of two characteristics on the coherence images: low coherence caused by the new traces formed in between a pair of observations or high coherence caused by the recent traces formed before a pair of observations. In contrast, we could not validate applicability of the backscatter approach or the InSAR approach.

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“…Meanwhile, textural information and textural characteristics obtained from SAR images have also been proved to be useful in the recognition of lithological units [27,34]. The backscatter coefficients VV and VH were chosen for grey level co-occurrence matrix (GLCM) [45] analysis.…”

Section: Sentinel-1 Preprocessingmentioning

confidence: 99%

Lithology Discrimination Using Sentinel-1 Dual-Pol Data and SRTM Data

2021

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Compared to various optical remote sensing data, studies on the performance of dual-pol Synthetic aperture radar (SAR) on lithology discrimination are scarce. This study aimed at using Sentinel-1 data to distinguish dolomite, andesite, limestone, sandstone, and granite rock types. The backscatter coefficients VV and VH, the ratio VV–VH; the decomposition parameters Entropy, Anisotropy, and Alpha were firstly derived and the Kruskal–Wallis rank sum test was then applied to these polarimetric derived matrices to assess the significance of statistical differences among different rocks. Further, the corresponding gray-level co-occurrence matrices (GLCM) features were calculated. To reduce the redundancy and data dimension, the principal component analysis (PCA) was carried out on the GLCM features. Due to the limited rock samples, before the lithology discrimination, the input variables were selected. Several classifiers were then used for lithology discrimination. The discrimination models were evaluated by overall accuracy, confusion matrices, and the area under the curve-receiver operating characteristics (AUC-ROC). Results show that (1) the statistical differences of the polarimetric derived matrices (backscatter coefficients, ratio, and decomposition parameters) among different rocks was insignificant; (2) texture information derived from Sentinel-1 had great potential for lithology discrimination; (3) partial least square discrimination analysis (PLSDA) had the highest overall accuracy (0.444) among the classification models; (4) though the overall accuracy is unsatisfactory, according to the AUC-ROC and confusion matrices, the predictive ability of PLSDA model for limestone is high with an AUC value of 0.8017, followed by dolomite with an AUC value of 0.7204. From the results, we suggest that the dual-pol Sentinel-1 data are able to correctly distinguish specific rocks and has the potential to capture the variation of different rocks.

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Recognition of lithological units in airborne SAR images using new texture features

Cited by 14 publications

References 10 publications

Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices

Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices

Detection of traces of pyroclastic flows and lahars with satellite synthetic aperture radars

Lithology Discrimination Using Sentinel-1 Dual-Pol Data and SRTM Data

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