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 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
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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. DOI:10.1080/01431160310001632675 · 1.36 Impact Factor
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ABSTRACT: It is widely recognized that there is no single scale at which a landscape or ecosystem should be studied, and forest planning and management require the definition of homogeneous units whose characteristics depend strongly on the scale at which their spatial arrangement occurs. Such "characteristic scales" range from fine to coarse, especially when planning efforts must be developed for broad spatial extensions, like forest areas. In such cases, the same analytical scale of analysis may be appropiate for some landscapes, while in others fine-scale features may be overlooked, or the scale may provide too much detail, depending on the characteristics of the spatial pattern. In this study, we aimed to develop a straightforward methodology to identify and discriminate among scale-divergent areas in landscapes represented by categorical maps. For this purpose, artificial landscapes were generated by use of a Modified Random Clusters method, and the Shannon-Wiener index was then applied at different scales by use of a moving-window approach. The results were analysed by contrasting the generation parameters with the statistical characteristics of the spatial pattern at each scale. This enabled us to relate the characteristic patterns to scale behaviour, and to define the minimum extension needed for a satisfactorily description of a landscape. The information obtained was then compared with real landscapes in order to validate the method, which we believe will facilitate the identification of homogeneous management units (in terms of spatial heterogeneity) and definition of the minimum area required for inclusion of essential descriptive elements.Forest Ecology and Management 01/2010; 258:2490-2500. DOI:10.1016/j.foreco.2009.09.005 · 2.67 Impact Factor