Sidnei J. S. Sant'Anna

A model for extremely heterogeneous clutter

A new class of distributions, G distributions, arising from the multiplicative model is presented, along with their main properties and relations. Their densities are derived for complex and multilook intensity and amplitude data. Classical distributions, such as K, are particular cases of this new class. A special case of this class called G/sup 0/, that has as many parameters as K distributions, is shown able to model extremely heterogeneous clutter, such as that of urban areas, that cannot be properly modeled with K distributions. One of the parameters of this special case is related to the degree of homogeneity, and a limiting case is that of a scaled speckle. The advantage of the G/sup 0/ distribution becomes evident through the analysis of a variety of areas (urban, primary forest and deforested) from two sensors.

Exploratory study of the relationship between tropical forest regeneration stages and SIR-C L and C data☆

Abstract In this article, the relationship between secondary forest regrowth stage and SIR-C SAR data is assessed, for an area located near to the Tapajos National Forest, south of Para State, in the Amazon region. These regeneration stages are mapped by making use of a consecutive annual sequence of Landsat TM (Thematic Mapper) images. Using this map as a mask over the radar images, the tonal means (expressed in dB) and coefficient of variation (CV) for several second-growth succession stages classes are calculated. It is shown that the discrimination between regeneration stages is difficult when individual ″small areas are used, but this discrimination might be possible when L-band means over a “large area are computed. In particular, the LHV band seems to carry more information. The maximum difference of means among classes occurred in this band and it is of about 5 dB. The CV appeared to be less well suited than the mean value for regeneration stage discrimination, although some discrimination among early stages of regeneration may be possible using this measure at L-band.

Speckle reduction in polarimetric SAR imagery with stochastic distances and nonlocal means

This paper presents a technique for reducing speckle in Polarimetric Synthetic Aperture Radar (PolSAR) imagery using Nonlocal Means and a statistical test based on stochastic divergences. The main objective is to select homogeneous pixels in the filtering area through statistical tests between distributions. This proposal uses the complex Wishart model to describe PolSAR data, but the technique can be extended to other models. The weights of the location-variant linear filter are function of the p-values of tests which verify the hypothesis that two samples come from the same distribution and, therefore, can be used to compute a local mean. The test stems from the family of (h-phi) divergences which originated in Information Theory. This novel technique was compared with the Boxcar, Refined Lee and IDAN filters. Image quality assessment methods on simulated and real data are employed to validate the performance of this approach. We show that the proposed filter also enhances the polarimetric entropy and preserves the scattering information of the targets.

Classification of Segments in PolSAR Imagery by Minimum Stochastic Distances Between Wishart Distributions

A new classifier for Polarimetric SAR (PolSAR) images is proposed and assessed in this paper. Its input consists of segments, and each one is assigned the class which minimizes a stochastic distance. Assuming the complex Wishart model, several stochastic distances are obtained from the h - φ family of divergences, and they are employed to derive hypothesis test statistics that are also used in the classification process. This article also presents, as a novelty, analytic expressions for the test statistics based on the following stochastic distances between complex Wishart models: Kullback-Leibler, Bhattacharyya, Hellinger, Rényi, and Chi-Square; also, the test statistic based on the Bhattacharyya distance between multivariate Gaussian distributions is presented. The classifier performance is evaluated using simulated and real PolSAR data. The simulated data are based on the complex Wishart model, aiming at the analysis of the proposal with controlled data. The real data refer to a complex L-band image, acquired during the 1994 SIR-C mission. The results of the proposed classifier are compared with those obtained by a Wishart per-pixel/contextual classifier, and we show the better performance of the region-based classification. The influence of the statistical modeling is assessed by comparing the results using the Bhattacharyya distance between multivariate Gaussian distributions for amplitude data. The results with simulated data indicate that the proposed classification method has very good performance when the data follow the Wishart model. The proposed classifier also performs better than the per-pixel/contextual classifier and the Bhattacharyya Gaussian distance using SIR-C PolSAR data.

Land Use and Land Cover Mapping in the Brazilian Amazon Using Polarimetric Airborne P-Band SAR Data

In September 2000, an airborne synthetic aperture radar (SAR) mission acquired unprecedented full polarimetric P-band data over the Tapajos National Forest (Para State), which is an area in the Brazilian Amazon which has been continuously monitored in the last three decades. Eight land use/cover classes were identified, namely, primary forest, regeneration older than 25 years, regeneration between 12 and 25 years, regeneration between 6 and 12 years, regeneration younger than six years, crops/pasture, bare soil, and floodplain (FP). The objective of this paper is to analyze the potential of full polarimetric P-band data in distinguishing different land use/cover classes with a minimum established Kappa value of 75%, using the latest development on SAR statistical characterization. The iterated conditional mode (ICM) contextual classifier was applied to amplitude, intensity images, biomass index, and some polarimetric parameters (entropy, alpha angle, and anisotropy) extracted from the polarimetric P-band data. As the accuracy obtained for eight classes was not acceptable, another two sets, with five and four classes, were formed by the combination of the previous ones. They were defined by confusion matrix analysis and by the graphical analysis of average backscatter values, entropy, [alpha] angle, and anisotropy images and by the H/alpha plans of the land use samples. The classification accuracy with four classes (three levels of biomass plus FP) was then considered acceptable with a Kappa value of 76.81%, using the ICM classification with the adequate bivariate distribution for the HV and VV channels.

Land use/cover classification in the Brazilian Amazon using satellite images

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

Alternative distributions for the multiplicative model in SAR images

Another class of distributions is proposed to model the terrain backscatter, namely the class of the generalized inverse Gaussian distributions. Besides allowing the explicit calculation of the density of the random variable that models the radar return, these distributions have the remarkable property of having the gamma and other distributions as particular cases. The resulting distributions for complex, multilook intensity and multilook amplitude data are derived assuming the multiplicative model. These new densities yield to a more general model-than the classical one, which is given by the class of K distributions. Several plots are presented, showing the flexibility of these new distributions and its possible use as a model for SAR data.

The use of airborne P-band radar data for land use and land cover mapping in Brazilian Amazonia

The aim of this work was to analyze the potentiality of polarimetric P-band data for land use and land cover mapping in a site of the Brazilian Amazonia. These data are the first P-band image set gathered in the Brazilian Amazonia, so they represent a unique opportunity of analyzing the potentiality of this frequency for classification purposes. The stratification of land use/land cover classes was performed using a classification system specially developed for polarimetric data. Results showed that P-band data were able to discriminate forest and regeneration areas from crop, pasture and bare soil areas. Moreover, regeneration areas (older than 12 years) were successfully distinguished from primary forest and other regeneration stages.

Secondary forest age mapping in Amazonia using multi-temporal Landsat/TM imagery

Satellite imagery can be a powerful tool for identification of deforested areas, often providing reliable classification of the Earth surface. The aim of this paper is to identify and to characterize the age of tropical secondary forests in a test site near Tapajos National Forest, located on the south of Santarem city, Para State, Brazil, using a time series of Landsat/TM images. The Landsat/TM images from 1986 to 1992 were co-registered, segmented and classified into several classes of land cover. These thematic images were superposed in a GIS and, using logical (Boolean) operations, a stages of regeneration map was generated. The stages of regeneration classes were defined as areas with recent activities, areas with different stages of secondary vegetation (varying from one to six years of regeneration), old secondary forest (more than six years old), and primary forest. It was observed that there is a functional relationship between the stages of the classified secondary vegetation and the normalized difference vegetation index (NDVI) using the bands 3 and 4 of the Landsat/TM images. It was concluded that the use of time series of optical images can be a valuable tool for determining the evolution of secondary tropical forests, providing that image acquisition is possible at least once a year.

Closed form expressions for scattering matrix of simple targets in multilayer structures

Scattering matrix of a simple target embedded in multilayer planar structure is derived. The structure is excited by an elliptically polarized plane wave incident at an oblique angle. The calculation of the electromagnetic fields is performed in spectral domain. The scattering matrix of a planar electric dipole printed on upper interface and surrounding by free-space is evaluated. The obtained results agree with those existing on the literature for normal incidence case.