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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- "ECENT research has demonstrated the potential of SAR image texture in helping to discriminate tree stands on a forested landscape , . SAR image texture consists of two components: backscatter fluctuations, which contain information about the scene, and speckle fluctuations, which are produced by the imaging process. "
ABSTRACT: Spatial fluctuations in microwave backscatter may be an important piece of information in discriminating tree stands. However, the presence of speckle in synthetic aperture radar (SAR) image data is a barrier to the exploitation of image texture. The authors explored a new methodology that combines a recent adaptive speckle reduction algorithm by Lopes et al. (1990) with a generic texture estimation scheme. They investigated the claim that this filter was capable of preserving backscatter texture. To understand if speckle reduction was destroying backscatter texture, they compared the strength of the relationship between forest inventory parameters and image texture as a function of spatial scale for both filtered and unfiltered images. They used Radarsat Fine mode image data: single look resolution is approximately 8.5 m, and pixel spacing is 3 m. Their study area was northern Vancouver Island, B.C., on the west coast of Canada. For the unfiltered data, they found that the ability of image texture to predict the forest parameters decreased as the texture scale increased from 3 to 13 m, suggesting greater information content in the small scale texture. For the filtered data, this relationship was much weaker at small scales and was not a function of distance. Their results suggest that the speckle filter was not retaining small scale texture, which is consistent with the theoretical hypotheses underlying its multiplicative noise model. They also show that there is significant information in small state SAR image texture that may be used as an adjunct to other spatial information for discriminating tree stands in the temperate rain forestIEEE Transactions on Geoscience and Remote Sensing 06/2000; 38(3-38):1160 - 1170. DOI:10.1109/36.843008 · 3.51 Impact Factor
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ABSTRACT: The Pantanal of Brazil, the largest wetland on the planet, is a disturbance-maintained ecosystem: an unusual topography coupled with a seasonal cycle of flooding and drydown creates a collection of landscapes that are environmentally heterogenous in space and time. Dominant land cover types include freshwater and saline lakes, periodically inundated grasslands, and forested corridors and patches. These cover types are highly heterogeneous in spatial arrangement and in response to inundation. Spatio-temporal analysis of land cover dynamics from synthetic aperture radar (SAR) image time series is relatively new research area but one that will expand given the increasing availability of SAR data. The Pantanal is well suited to microwave remote sensing because land cover types can exhibit great contrasts in backscattering. The authors have previously shown the efficacy of using lacunarity analysis with SAR imagery for quantifying land cover dynamics. In this presentation they extend that analysis to a total of seven ERS-1 SAR images from December 1992 to November 1993. This period includes both seasonal inundation followed by a significant climatic drought that transformed the spatial structure of backscattering across the landscape. Lacunarity analysis of the SAR image series captures the spatio-temporal rearranging and illustrates how complex land cover change can be quantified within a predictive frameworkGeoscience and Remote Sensing Symposium, 1996. IGARSS '96. 'Remote Sensing for a Sustainable Future.', International; 06/1996
Conference Paper: Use of SAR image texture in terrain classification[Show abstract] [Hide abstract]
ABSTRACT: Classification is a common first step in the use of SAR data. Intensity of a pixel is generally used as a feature vector. This is complicated by coherent fading that yields multiplicative noise. Consequently, the first statistical moment of intensity (over some local window) is often used as a feature vector instead. In some cases this leads to unacceptably high rates of misclassification. The 2nd statistical moment also can be used to distinguish categories but is dependent on the composite effects of the sensor (N of looks), the mean backscatter (via multiplicative noise) and the true spatial variance in average backscatter relative to SAR resolution. Thus, using variance measures as feature vectors can lead to increased classification accuracy. However, such measures ignore the observation that the variance for many terrain categories is not stationary and indeed may not be isotropic. Further improvement in classification can be realized by quantifying the translational variance in backscatter using scale-dependent geostatistical semi-variance and lacunarity that spatial structure of image intensity. Simulated SAR data are used to understand the effects of system parameters (such as number of looks and spatial resolution) and target conditions (such as probability of occurrence and stationarity) on geostatistical measures of texture. ERS-1 and JERS-1 SAR data demonstrate the use of these techniques in terrain characterization. These statistics also give measures of heterogeneity of interest to ecologistsGeoscience and Remote Sensing, 1997. IGARSS '97. Remote Sensing - A Scientific Vision for Sustainable Development., 1997 IEEE International; 09/1997