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

Li, Na; Bruzzone, Lorenzo; Chen, Zengping; Liu, Fang

doi:10.3390/rs6053857

Cited by 10 publications

(7 citation statements)

References 32 publications

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“…Due to their broad coverage, coarse resolution images such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Medium Resolution Imaging Spectrometer (MERIS) have been widely used for the global and regional mapping of built-up areas in global ecological, environmental, and climatological research [5]. With the development of imaging techniques, satellite images of a medium spatial resolution, such as the Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+), are now often used for this task [6,7], as are China's Beijing-1 [8] optical and synthetic aperture radar (SAR) images [14,[31][32][33]. One of the most famous methods is the "Pantex" procedure [14], which proposes a texture-based built-up area presence index.…”

Section: Introductionmentioning

confidence: 99%

Representation of Block-Based Image Features in a Multi-Scale Framework for Built-Up Area Detection

Zhang

et al. 2016

Remote Sensing

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Abstract:The accurate extraction and mapping of built-up areas play an important role in many social, economic, and environmental studies. In this paper, we propose a novel approach for built-up area detection from high spatial resolution remote sensing images, using a block-based multi-scale feature representation framework. First, an image is divided into small blocks, in which the spectral, textural, and structural features are extracted and represented using a multi-scale framework; a set of refined Harris corner points is then used to select blocks as training samples; finally, a built-up index image is obtained by minimizing the normalized spectral, textural, and structural distances to the training samples, and a built-up area map is obtained by thresholding the index image. Experiments confirm that the proposed approach is effective for high-resolution optical and synthetic aperture radar images, with different scenes and different spatial resolutions.

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Section: Introductionmentioning

confidence: 99%

Representation of Block-Based Image Features in a Multi-Scale Framework for Built-Up Area Detection

Zhang

et al. 2016

Remote Sensing

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“…From the recorded S1-SAR imagery, several urban features can be extracted that accurately classifies urban areas. These features include; (1) textural information describing spatial variations of the urban neighbourhoods, (2) polarimetric information used for retrieval of geometry and dielectric information for urban compositions and (3) morphological profiles of urban scenes that describe physical and socio-economic characteristics of local neighbourhoods [10], [22]. In recent studies, the use of coherence information from dual-polarimetry has led to improved estimation of polarimetric features form S1-SAR imagery [16].…”

Section: S1-sarmentioning

confidence: 99%

“…In essence, since textural patterns in the imagery have different sizes and intensity levels, MLPH captures the pattern size distributions by a varying intensity-based windows, with similar intensities forming a local structure. The pixel-wise moving window is computationally intensive, therefore, [22] computed featurespecific semi-variogram to obtained variograms of distinct shapes and parameters in advance. The variograms once combined with GLCM matrices in a fuzzy set theory, not only describes the spatial characteristics but also to improves the fuzzy belongings for low backscatter imagery [31].…”

Section: Ijsermentioning

confidence: 99%

Reviewing the Pertinence of Sentinel-1 SAR for Urban Land Use Land Cover Classification

Abudu¹,

Parvin²,

Andogah³

2020

IJSER

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Conventional approaches for urban land use land cover classification and quantification of land use changes have often relied on the ground surveys and urban censuses of urban surface properties. Advent of Remote Sensing technology supporting metric to centimetric spatial resolutions with simultaneous wide coverage, significantly reduced huge operational costs previously encountered using ground surveys. Weather, sensor’s spatial resolution and the complex compositions of urban areas comprising concrete, metallic, water, bare- and vegetation-covers, limits Remote Sensing ability to accurately discriminate urban features. The launch of Sentinel-1 Synthetic Aperture Radar, which operates at metric resolution and microwave frequencies evades the weather limitations and has been reported to accurately quantify urban compositions. This paper assessed the feasibility of Sentinel-1 SAR data for urban land use land cover classification by reviewing research papers that utilised these data. The review found that since 2014, 11 studies have specifically utilised the datasets.

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“…Further, spatial feature is needed to reduce the classification uncertainty when only spectral feature is used. The popular gray level co-occurrence matrix (GLCM) is a spatial feature extraction approach, which is widely used for remote sensing image information extraction [41,42]. The widely used strategy is to get the GLCM from the first principal component of principal component analysis, which lost a lot of useful information.…”

Section: Feature Extractionmentioning

confidence: 99%

Improving the Impervious Surface Estimation from Hyperspectral Images Using a Spectral-Spatial Feature Sparse Representation and Post-Processing Approach

Liu¹,

Gu²

2017

Remote Sensing

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Impervious surfaces have been widely recognized as an indicator for urbanization and environment monitoring. Plenty of methods have been proposed to extract impervious surfaces using remote sensing images. However, accurately extracting impervious surface is still a challenging task due to the confusion between impervious surface and bare soil. Thus, this paper presents a hybrid approach consisting of spectral-spatial feature sparse representation (SS-SR) and post-processing to extract urban impervious surface from hyperspectral images. We first extracted spectral and spatial features from hyperspectral images. Then, the spectral and spatial information of a pixel is represented by the vector stacking strategy. Each pixel vector can be represented by a linear combination of a few atoms from a learned dictionary, which is more suitable for impervious surface estimation. The sparse coefficients were automatically learned and then used for extracting impervious surface. The proposed impervious surface extraction method was evaluated with four hyperspectral datasets. We compared our algorithms with the state-of-the-art per-pixel based impervious surface extraction methods. The encouraging experimental results demonstrate the SS-SR algorithm generally outperforms the classic support vector machines and random forest. The improvement is more significant when combining SS-SR with post-classification approach.

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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

Cited by 10 publications

References 32 publications

Representation of Block-Based Image Features in a Multi-Scale Framework for Built-Up Area Detection

Representation of Block-Based Image Features in a Multi-Scale Framework for Built-Up Area Detection

Reviewing the Pertinence of Sentinel-1 SAR for Urban Land Use Land Cover Classification

Improving the Impervious Surface Estimation from Hyperspectral Images Using a Spectral-Spatial Feature Sparse Representation and Post-Processing Approach

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