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.
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ABSTRACT: Lacunarity analysis is a multi-scaled method of determining the texture associated with patterns of spatial dispersion (i.e., habitat types or species locations) for one-, two-, and three-dimensional data. Lacunarity provides a parsimonious analysis of the overall fraction of a map or transect covered by the attribute of interest, the degree of contagion, the presence of self-similarity, the presence and scale of randomness, and the existence of hierarchical structure. For self-similar patterns, it can be used to determine the fractal dimension. The method is easily implemented on the computer and provides readily interpretable graphic results. Differences in pattern can be detected even among very sparsely occupied maps.01/1993; 8:201-211.
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ABSTRACT: An explicit framework can provide a better understanding of remote sensing models and their interrelationships. This framework distinguishes between the scene, which is real and exists on the ground, and the image, which is a collection of spatially arranged measurements drawn from the scene. The scene model generalizes and parameterizes the essential qualities of the scene. Scene models may be discrete, in which the scene model consists of discrete elements with boundaries, or continuous, in which matter and energy flows are taken to be continuous and there are no clear or sharp boundaries in the scene. In the discrete case, there are two possibilities for models: H- and . In the case, the resolution cells of the image are smaller than the elements, and thus the elements may be individually resolved. In the case, the resolution cells are larger than the elements and cannot be resolved. Most canopy models are , deterministic, and noninvertible in nature; image processing models, however, tend to be , empirical, and invertible. This taxonomy helps add insight to the development of remote sensing theory and point the way to new, productive areas of research.Remote Sensing of Environment. 11/1986;
Article: Computer And Robot Vision Volume II