Texture-based characterization of urban environments on satellite SAR images

Dell’Acqua, Fabio; Gamba, Paolo

doi:10.1109/tgrs.2002.807754

Cited by 158 publications

(53 citation statements)

References 9 publications

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“…They correspond to the mean and standard deviations (STD) of backscatter intensities calculated in a local neighborhood of four different sizes: 3 × 3, 5 × 5, 7 × 7, and 9 × 9 pixels matching typical building structures. Textural features are expected to enhance the capacity of the SML in delineating built-up structures as demonstrated in multiple studies dealing with SAR images in urban environments (Corbane, Faure, Baghdadi, Villeneuve, & Petit, 2008;Dell'Acqua & Gamba, 2003;Pesaresi & Gerhardinger, 2011). …”

Section: Sml Classification Of Sentinel-1mentioning

confidence: 99%

Big earth data analytics on Sentinel-1 and Landsat imagery in support to global human settlements mapping

Corbane

Pesaresi

Politis³

et al. 2017

Big Earth Data

110

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Continuous global-scale mapping of human settlements in the service of international agreements calls for massive volume of multi-source, multi-temporal, and multi-scale earth observation data. In this paper, the latest developments in terms of processing big earth observation data for the purpose of improving the Global Human Settlement Layer (GHSL) data are presented. Two experiments with Sentinel-1 and Landsat data collections were run leveraging on the Joint Research Centre Earth Observation Data and Processing Platform. A comparative analysis of the results of built-up areas extraction from different remote sensing data and processing workflows shows how the information production supported by data-intensive computing infrastructure for optimization and multiple testing can improve the output information reliability and consistency within the GHSL scope. The paper presents the processing workflows and the results of the two main experiments, giving insights into the enhanced mapping capabilities gained by analyzing Sentinel-1 and Landsat data-sets, and the lessons learnt in terms of handling and processing big earth observation data.

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Section: Sml Classification Of Sentinel-1mentioning

confidence: 99%

Big earth data analytics on Sentinel-1 and Landsat imagery in support to global human settlements mapping

Corbane

Pesaresi

Politis³

et al. 2017

Big Earth Data

110

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“…Previous research has shown that texture measures provide vital information from radar imagery [20,34]. Among several statistical texture methods previously proposed, the gray-level co-occurrence matrix (GLCM) is one of the most powerful for land cover monitoring; thus, the GLCM is used in this study.…”

Section: Spatial Texture Analysismentioning

confidence: 99%

“…SAR sensors can extract object characteristics from backscattering echo, independent of weather conditions and time [15][16][17]. Currently, this technology, with dual-or full-polarization (HH, HV, VV and VH), is used widely to monitor urban areas and map land cover since different polarizations have different sensitivities and scattering coefficients for the same target [3,[18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Texture Analysis and Land Cover Classification of Tehran Using Polarimetric Synthetic Aperture Radar Imagery

Zakeri¹,

Yamazaki²,

Li³

2017

Applied Sciences

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Abstract:Land cover classification of built-up and bare land areas in arid or semi-arid regions from multi-spectral optical images is not simple, due to the similarity of the spectral characteristics of the ground and building materials. However, synthetic aperture radar (SAR) images could overcome this issue because of the backscattering dependency on the material and the geometry of different surface objects. Therefore, in this paper, dual-polarized data from ALOS-2 PALSAR-2 (HH, HV) and Sentinel-1 C-SAR (VV, VH) were used to classify the land cover of Tehran city, Iran, which has grown rapidly in recent years. In addition, texture analysis was adopted to improve the land cover classification accuracy. In total, eight texture measures were calculated from SAR data. Then, principal component analysis was applied, and the first three components were selected for combination with the backscattering polarized images. Additionally, two supervised classification algorithms, support vector machine and maximum likelihood, were used to detect bare land, vegetation, and three different built-up classes. The results indicate that land cover classification obtained from backscatter values has better performance than that obtained from optical images. Furthermore, the layer stacking of texture features and backscatter values significantly increases the overall accuracy.

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“…The potential usefulness of the GLCM technique for characterizing SAR images has been confirmed. Dell'Acqua et al [26,27] introduce the GLCM to discriminate the urban areas from the background in SAR images with medium, as well as high, resolutions. When the GLCM is used, many important parameters need to be considered.…”

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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Texture-based characterization of urban environments on satellite SAR images

Cited by 158 publications

References 9 publications

Big earth data analytics on Sentinel-1 and Landsat imagery in support to global human settlements mapping

Big earth data analytics on Sentinel-1 and Landsat imagery in support to global human settlements mapping

Texture Analysis and Land Cover Classification of Tehran Using Polarimetric Synthetic Aperture Radar Imagery

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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