Robust Extraction of Urban Area Extents in HR and VHR SAR Images

Gamba, Paolo; Aldrighi, M.; Stasolla, Mattia

doi:10.1109/jstars.2010.2052023

Cited by 83 publications

(39 citation statements)

References 20 publications

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“…All SAR images are converted from linear float-type backscattering values to 8-bit gray-scale images with linear stretching, by setting 2% low values to 0 and 2% high values to 255, which can enhance the image contrast [34]. In this step, the float-type pixel depth is reduced to 8-bit to significantly reduce image memory; this process usually does not affect classification accuracy [31].…”

Section: Preprocessingmentioning

confidence: 99%

Operational Built-Up Areas Extraction for Cities in China Using Sentinel-1 SAR Data

et al. 2018

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Abstract:To obtain accurate information in a timely manner on built-up areas (BAs) is essential for urban planning and natural hazard (e.g., earthquakes) response strategies. In this paper, a new method for BAs extraction using the Sentinel-1 SAR is proposed, which includes two steps: (1) Candidate BAs are first selected as seeds from images that show high backscattering and obvious textural patterns, as characterized by image intensity, Getis-Ord index, and the variogram texture features; (2) region growing is iteratively implemented from these seed pixels to extract the BAs. Sentinel-1 data, with 5 × 20 m 2 resolution, are selected over eight cities with various environmental settings around China, to validate the robustness of the proposed method. The results show that the proposed method achieves higher detection accuracy and fewer commission errors compared with the intensity-based region growing and thresholding methods. An averaged accuracy of 96.5% in validation points of eight cities was achieved, which outperforms the GlobCover urban product in both urban and rural area, while fewer commission errors were achieved compared to Landsat data-based methods. Moreover, two polarizations (VV/VH) and the averaged channel are compared for BAs extraction in areas with various environments. It turns out that improved results can be achieved using the averaged image of two polarizations in north China, while the VV image is better suited for BAs extraction in south. These findings indicate that operational BAs mapping over China, and even globally, is possible, since the Sentinel-1 data can provide images with global coverage.

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Section: Preprocessingmentioning

confidence: 99%

Operational Built-Up Areas Extraction for Cities in China Using Sentinel-1 SAR Data

et al. 2018

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“…Dekker [18] investigates several texture measures, such as histogram measures, wavelet energy measures, fractal dimension, and lacunarity, to update built-up maps of regions in the Netherlands. Gamba et al [19] propose a procedure for the extraction of urban areas from HR SAR images based on the combination of Local Indicators of Spatial Association (LISA) [20] and textural features derived from grey level co-occurrence matrices (GLCM) [21]. This technique succeeds in detecting the sparse and large settlements; however, it fails to detect the settlements patterns with low backscattering.…”

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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“…Optical remote sensing data with high resolution, such as data extracted from remote sensing image indices and other high-quality land cover data products, are the major datasets used for urban information extraction [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34], and such data are far superior to those of DMSP-OLS in terms of image resolution. However, most of these data products have limited temporal coverage and present limited usefulness for a dynamic analysis at large scales.…”

Section: Introductionmentioning

confidence: 99%

Optimized Sample Selection in SVM Classification by Combining with DMSP-OLS, Landsat NDVI and GlobeLand30 Products for Extracting Urban Built-Up Areas

Tong

Luo

et al. 2017

Remote Sensing

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Abstract:The accuracy of training samples used for data classification methods, such as support vector machines (SVMs), has had a considerable positive impact on the results of urban area extractions. To improve the accuracy of urban built-up area extractions, this paper presents a sample-optimized approach for classifying urban area data using a combination of the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) for nighttime light data, Landsat images, and GlobeLand30, which is a 30-m global land cover data product. The proposed approach consists of three main components: (1) initial sample generation and data classification into built-up and non-urban built-up areas based on the maximum and minimum intervals of digital numbers from the DMSP-OLS data, respectively; (2) refined sample selection and optimization by the probability threshold of each pixel based on vegetation-cover, using the Landsat-derived normalized differential vegetation index (NDVI) and artificial surfaces extracted from the GlobeLand30 product as the constraints; (3) iterative classification and urban built-up area data extraction using the relationship between these three aspects of data collection together with the training sets. Experiments were conducted for several cities in western China using this proposed approach for the extraction of built-up areas, which were classified using urban construction statistical yearbooks and Landsat images and were compared with data obtained from traditional data collection methods, such as the threshold dichotomy method and the improved neighborhood focal statistics method. An analysis of the empirical results indicated that (1) the sample training process was improved using the proposed method, and the overall accuracy (OA) increased from 89% to 96% for both the optimized and non-optimized sample selection; (2) the proposed method had a relative error of less than 10%, as calculated by an accuracy assessment; (3) the overall and individual class accuracy were higher for artificial surfaces in GlobeLand30; and (4) the average OA obviously improved and the Kappa coefficient in the case of Chengdu increased from 0.54 to 0.80. Therefore, the experimental results demonstrated that our proposed approach is a reliable solution for extracting urban built-up areas with a high degree of accuracy.

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Robust Extraction of Urban Area Extents in HR and VHR SAR Images

Cited by 83 publications

References 20 publications

Operational Built-Up Areas Extraction for Cities in China Using Sentinel-1 SAR Data

Operational Built-Up Areas Extraction for Cities in China Using Sentinel-1 SAR Data

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

Optimized Sample Selection in SVM Classification by Combining with DMSP-OLS, Landsat NDVI and GlobeLand30 Products for Extracting Urban Built-Up Areas

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