Multi-scale texture in SAR imagery: Landscape dynamics of the Pantanal, Brazil
ABSTRACT The potential for environmental monitoring of natural landscapes using radar remote sensing is great. However, to realize this potential, new tools for radar image analysis are needed. Landscapes that exhibit complex spatio-temporal variability in terms of backscattering cannot be well characterized using standard approaches to texture. The authors assess three methods for multi-scale texture measurement: lacunarity, diversity, and GLCM measures. They used three ERS-1 SAR images of the Nhecolandia region of the Brazilian Pantanal from December 1992 to May 1993. Lacunarity and diversity measures captured the seasonal transformation of the landscape due to rising floodwaters; GLCM measures did not, however, exhibit much sensitivity to either anisotropies or temporal differences in images. In addition, lacunarity analysis was able to distinguish between speckle-generated texture from texture produced by scene object backscattering.
Conference Paper: Texture analysis of urban areas in ERS SAR imagery for map updating[Show abstract] [Hide abstract]
ABSTRACT: In single-band and single-polarized SAR image classification, textural information is important, both for pixel and segment based classification schemes. To study the map updating capabilities of such sensors in urban areas, several texture measures were studied. Among them are statistical measures, wavelet energy, fractal dimension, lacunarity, and semivariogram. The latter was chosen as an alternative for the well known gray-level co-occurrence family of features. Two urban areas were studied using ERS1/2 data, one of which is reported: the conurbation around Rotterdam and The Hague in The Netherlands. The area can be characterized as a well-planned dispersed urban area with residential areas, industry, greenhouses, pasture, arable land, and some forest. The digital map is a 1:250,000 vector map (VMapl). The texture measures that gave the best landcover separability for this area are: mean intensity, variance, skew, weighted-rank fill ratio, semivariograms (or alternatively wavelet energy measures), and lacunarity. The latter is preferred to be included in case more than one man-made landcover class is involved, which is often the case in urban environmentsRemote Sensing and Data Fusion over Urban Areas, IEEE/ISPRS Joint Workshop 2001; 02/2001
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ABSTRACT: The paper presents results for spectral and textural analysis of the rock units in Landsat Thematic Mapper (TM) images, dual-band (L and C) and dual-polarization (HH and HV) Shuttle Imaging Radar (SIR)-C images, and C-band HH polarization Standard Beam 4 and Extended High Incidence Beam 3 Radarsat images from a study area between California and Arizona, USA. Fractal dimension, lacunarity and grey-level co-occurrence matrix (GLCM) textural feature images were created from the SIR-C and Radarsat images. Fractal dimensions were calculated using a differential box counting method and lacunarity measures were obtained using a new grey-scale lacunarity estimation method for 36 sample images extracted from the SIR-C and Radarsat images. The fractal dimension and lacunarity curves and class signature separability analysis show that, for rock unit discrimination using image textural features in the study area, the SIR-C L-HH image is more suitable than other SIR-C images and Radarsat images, and that co-polarization (HH) generally provides more textural information than cross-polarization (HV) in the study area. The study also shows that lacunarity measures can reveal the scaling properties of radar image textures for rock units. The combination of spectral information from Landsat TM images and textural information from radar images improves the image classification accuracy of rock units in the study area.International Journal of Remote Sensing 09/2004; 25(18):3745-3768. · 1.36 Impact Factor
Conference Paper: Texture analysis and classification of SAR images of urban areas[Show abstract] [Hide abstract]
ABSTRACT: In SAR image classification texture holds useful information. In a study after the ability of texture to discriminate urban land-cover, a set of measures was investigated. Among them were histogram measures, wavelet energy, fractal dimension, lacunarity and semivariograms. The latter were chosen as an alternative for the well known gray-level cooccurrence family of features. The study was done on the basis of non-parametric separability measures and classification techniques applied to ERS-1 SAR data. The conclusion is that texture improves the classification accuracy. The measures that performed best were mean intensity (actually no texture), variance, weighted-rank fill ratio and semivariogram, but the accuracies vary for different classes. Despite the improvement, the overall classification accuracy indicated that the land-cover information content of ERS-1 leaves to be desired.Remote Sensing and Data Fusion over Urban Areas, 2003. 2nd GRSS/ISPRS Joint Workshop on; 06/2003