Textural Features for Radar Image Analysis

Shanmugan, K. Sam; Narayanan, V.; Frost, Victor S.; Stiles, Josephine Abbot; Holtzman, J. C.

doi:10.1109/tgrs.1981.350344

Cited by 69 publications

(30 citation statements)

References 7 publications

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“…Shanmugan et al (1981), Ulaby et al (1986), Yanasse et al (1993), Baraldi & Parmiggiani (1995), and Kurvonen & Hallikainen (1999), but few examples have focused on the geological applications in tropical environments (Azzibrouck et al, 1997).…”

Section: Methodsmentioning

confidence: 99%

Mapping iron-mineralized laterite environments based on textural attributes from MAPSAR image simulation - SAR-R99B (SIVAM/SIPAM) in the Amazon region

2011

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ABSTRACT. The use of remote sensing is a valuable method for geological mapping as it provides synoptic coverage at relatively low cost. In the Amazon region, radar imagery has a potential for geological applications due the enhanced sensitivity to topography (macrotopography), surface roughness (microtopography), and dielectric properties of materials, independent of weather, sun angle, and illumination conditions. As the roughness is highlighted, SAR textural attributes can be used for mapping iron-ore mineralized laterites in N1 deposit, located in the Carajás Province. For mapping the lateritic cover, the airborne SAR was used from Surveillance of the Amazon System (SIVAM/SIPAM, L-hh, L-hv, L-vv) to simulate orbital Multi-Application Purpose SAR (MAPSAR). The images were analyzed through textural classifications derived from second-order measure (GLCM) with the objective of mapping the mineralized laterites for iron ore. Differences are highlighted when comparing the classified maps and the ground information. Not all classes were separated, but a high performance for textural attributes was presented by the hematite class. This class was sensitive to the sensor and target parameters, especially macrotopography and physics characteristics. The results showed that for mineral exploration, the radar images at L-band can be used as a practical tool for a preliminary mapping, and as a guide for field-based verification.Keywords: Amazônia, SAR, MAPSAR, textural attribute, laterite cover, SIVAM/SIPAM, L-hh, L-hv, L-vv. RESUMO.Imagens de radar sãoúteis em geologia devidoà visão sinóptica e a uma cobertura a um custo relativamente baixo. Particularmente na região amazônica, o uso de imagens de radar em aplicações geológicasé favorecido pela capacidade de realce do relevo (macrotopografia), da rugosidade superficial (microtopografia) e da constante dielétrica, independente da presença de nuvens,ângulo solar e condições de iluminação. A microtopografia do terreno, realçada neste tipo de imagem, permite que a textura das imagens de radar possa ser usada no mapeamento das coberturas lateríticas mineralizadas em ferro do depósito N1, localizado na Província de Carajás.A investigação se baseou em imagens adquiridas por radar aerotransportado (SIVAM/SIPAM, banda L-hh, L-hv e L-vv), cujas faixas de voo foram degradadas visandoà simulação do SAR orbital MAPSAR. Os dados foram analisados através de classificações texturais, derivadas de medidas obtidas por meio de Matriz de Co-ocorrência dos Níveis de Cinza (MCNC) com o objetivo de mapeamento das unidades lateríticas em N1. Muitas diferenças foram encontradas entre as classes mapeadas e o mapa de verdade terrestre. Nem todas as classes foram separadas, mas a hematita apresentou alto desempenho na classificação. Esta classe foi sensível aos parâmetros do alvo e do sensor, especialmente a macrotopografia e as características físicas. Os resultados mostraram que a classificação baseada em MCNC pode ser usada como ferramenta preliminar de mapeamento das lateritas e como um guia p...

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Section: Methodsmentioning

confidence: 99%

Mapping iron-mineralized laterite environments based on textural attributes from MAPSAR image simulation - SAR-R99B (SIVAM/SIPAM) in the Amazon region

2011

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“…Davis et al [21] used generalized GLCM to impose spatial constraints. Shanmugan et al [61] used segments of digitally correlated SEASAT-A SAR imagery in their attempt to classify radar images based on GLCM textural features. Conners et al [19] used texture to segment a high-resolution black and white image of an urban area.…”

Section: A Applicationsmentioning

confidence: 99%

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

Soh

Tsatsoulis

1999

IEEE Trans. Geosci. Remote Sensing

1,059

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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“…It can be applied to SAR images with different resolutions. The GLCM technique was used to process the SEASAT-A SAR images over the Tenessee [25]. A set of texture features is extracted, and used for identifying simple geological formations.…”

Section: Analysis Of the State Of The Artmentioning

confidence: 99%

A Novel Technique Based on the Combination of Labeled Co-Occurrence Matrix and Variogram for the Detection of Built-up Areas in High-Resolution SAR Images

Bruzzone

Chen

et al. 2014

Remote Sensing

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Abstract:Interests in synthetic aperture radar (SAR) data analysis is driven by the constantly increased spatial resolutions of the acquired images, where the geometries of scene objects can be better defined than in lower resolution data. This paper addresses the problem of the built-up areas extraction in high-resolution (HR) SAR images, which can provide a wealth of information to characterize urban environments. Strong backscattering behavior is one of the distinct characteristics of built-up areas in a SAR image. However, in practical applications, only a small portion of pixels characterizing the built-up areas appears bright. Thus, specific texture measures should be considered for identifying these areas. This paper presents a novel texture measure by combining the proposed labeled co-occurrence matrix technique with the specific spatial variability structure of the considered land-cover type in the fuzzy set theory. The spatial variability is analyzed by means of variogram, which reflects the spatial correlation or non-similarity associated with a particular terrain surface. The derived parameters from the variograms are used to establish fuzzy functions to characterize the built-up class and non built-up class, separately. The proposed technique was tested on TerraSAR-X images acquired of Nanjing (China) and Barcelona (Spain), and on a COSMO-SkyMed image acquired of Hangzhou (China). The obtained classification accuracies point out the effectiveness of the proposed technique in identifying and detecting built-up areas. OPEN ACCESSRemote Sens. 2014, 6 3858

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Textural Features for Radar Image Analysis

Cited by 69 publications

References 7 publications

Mapping iron-mineralized laterite environments based on textural attributes from MAPSAR image simulation - SAR-R99B (SIVAM/SIPAM) in the Amazon region

Mapping iron-mineralized laterite environments based on textural attributes from MAPSAR image simulation - SAR-R99B (SIVAM/SIPAM) in the Amazon region

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

A Novel Technique Based on the Combination of Labeled Co-Occurrence Matrix and Variogram for the Detection of Built-up Areas in High-Resolution SAR Images

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