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The Lee sigma filter was developed in 1983 based on the simple concept of two-sigma probability, and it was reasonably effective in speckle filtering. However, deficiencies were discovered in producing biased estimation and in blurring and depressing strong reflected targets. The advancement of synthetic aperture radar (SAR) technology with high-resolution data of large dimensions demands better and efficient speckle filtering algorithms. In this paper, we extend and improve the Lee sigma filter by eliminating these deficiencies. The bias problem is solved by redefining the sigma range based on the speckle probability density functions. To mitigate the problems of blurring and depressing strong reflective scatterers, a target signature preservation technique is developed. In addition, we incorporate the minimum-mean-square-error estimator for adaptive speckle reduction. Simulated SAR data are used to quantitatively evaluate the characteristics of this improved sigma filter and to validate its effectiveness. The proposed algorithm is applied to spaceborne and airborne SAR data to demonstrate its overall speckle filtering characteristics as compared with other algorithms. This improved sigma filter remains simple in concept and is computationally efficient but without the deficiencies of the original Lee sigma filter. Index Terms---Sigma filter, speckle, speckle filtering, synthetic aperture radar (SAR).

In this paper, we study the bistatic scattering using an advanced integral equation model (AIEM). By keeping all the surface current terms in the Kirchhoff surface fields, the bistatic scattering coefficients are obtained. For simplification, the complete Kirchhoff field did not cast into the derivation of the complementary field. We compare varied updated versions of IEM-based models with the small perturbation model, geometrical optics model, and Kirchhoff approximation standard models at respective regions of validity. The results indicate that the new AIEM provides much more accurate predictions for bistatic scattering. Index Terms--Bistatic scattering, geometrical optics model (GOM), integral equation model (IEM), Kirchhoff approximation (KA), rough surface, small perturbation model (SPM).

Four radiative transfer equations for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) bands 11, 12, 13, and 14 are built involving six unknowns (average atmospheric temperature, land surface temperature, and four band emissivities), which is a typical ill-posed problem. The extra equations call be built by using linear or nonlinear relationship between neighbor band emissivities because the emissivity of every land surface type is almost constant for bands 11, 12, 13, and 14. The neural network (NN) can make full use of potential information between band emissivities through training data because the NN simultaneously owns function approximation, classification, optimization computation, and self-study ability. The training database can be built through simulation by MODTRAN4 or can be obtained from the reliable measured data. The average accuracy of the land surface temperature is about 0.24 K, and the average accuracy of emissivity in bands 11, 12, 13, and 14 is under 0.1105 for test data. The retrieval result by the NN is, on average, higher by about 0.7 K than the ASTER standard product (AST08), and the application and comparison indicated that the retrieval result is better than the ASTER standard data product. To further evaluate self-study of the NN, the ASTER standard products are assumed as measured data. After using AST09, AST08, and AST05 (ASTER Standard Data Product) as the compensating training data, the average relative error of the land surface temperature is under 0.1 K relative to the AST08 product, and the average relative error of the emissivity in bands 11, 12, 13, and 14 is under 0.001 relative to AST05, which indicates that the NN owns a powerful self-study ability and is capable of suiting more conditions if more reliable and high-accuracy ASTER standard products can be compensated. Index Terms--Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, emissivity, land surface temperature (LST).

In this paper, a novel technique is proposed for a supervised classification of multisource images for the purpose of landslide hazard assessment. The method, known as the generalized positive Boolean function (GPBF), is developed for land cover classification based on the fusion of remotely sensed images of the same scene collected from multiple sources. It presents a framework for data fusion of multisource remotely sensed images, which consists of two approaches, referred to as the band generation process (BGP) and the positive Boolean function (PBF) classifier. The PBF classifier developed from a stack filter has been successfully applied in hyperspectral image classification. For the PBF to be effective for multispectral images, a multiple adaptation BGP is introduced to create a new set of additional bands especially accommodated to landslide classes. These bands include nonlinear normalized difference vegetation index data and morphological information in the form of digital elevation model (DEM)-derived slope values that originate from multiple sources. The performance of the proposed method is evaluated by fusing Systeme Pour l'Observation de ht Terre images and DEM information for land cover classification during the post 921 Earthquake period in Taiwan. Experimental results demonstrate the proposed GPBF multiclassification approach is suitable for land cover classification in Earth remote sensing and improves the precision of image classification compared to conventional classifiers. Index Terms--Band generation process (BGP), data fusion, digital elevation model (DEM), generalized positive Boolean function (GPBF), normalized difference vegetation index (NDVI), positive Boolean function (PBF), stack filter.

A novel method, which employs both a matrix doubling algorithm and the first-order solution of a radiative transfer (RT) equation for modeling microwave backscattering from forest, is presented in the paper. The method is based on the assumption that a forest canopy can be divided into a number of distinct horizontal vegetation layers over a dielectric half-space rough surface. The scattering phase matrix of each layer is calculated by either matrix doubling to account for the multiple-scattering effect or first-order solution of an RT equation, depending on the scattering characteristics of the layer. The first-order solution of the RT equation is used for the trunk layer while the matrix doubling technique is applied to both the crown layer and understory. The advanced integral equation model and reflectivity matrix are used to calculate the noncoherent and coherent surface boundary conditions. Comparisons between model predictions and field measurements on radar backscattering coefficients for a walnut orchard showed a good agreement at both L-band and X-band and for all three polarizations. Comparative analyses of model predictions for backscattering from a forest medium calculated using the combined model, first-order RT model, and the standard matrix doubling model were also presented. Understory effects, that can significantly change the weight of each scattering mechanism, were also evaluated by using the combined method. Index Terms--Combined model, forest, matrix doubling.

This study develops a parameterized bare surface emission model for the applications in analyses of the passive microwave satellite measurements from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). We first evaluated the capability of the advanced integral equation model (AIEM) in simulating wide-band and high-incidence surface emission signals in comparison with INRA's field experimental data obtained in 1993. The evaluation results showed a very good agreement. With the confirmed confidence, we generated a bare surface emission database for a wide range of surface dielectric and roughness properties under AMSR-E sensor configurations using the AIEM model. Through the evaluations of the commonly used semiempirical models with both the AIEM simulated and the field experimental data, we developed a parameterized multifrequency-polarization surface emission model--the Qp model. This model relates the effects of the surface roughness on the emission signals through the roughness variable Qp at the polarization p. The Qp can be simply described as a single-surface roughness property--the ratio of the surface rms height and the correlation length. The comparison of the emissivity simulations by the Qp and AIEM models indicated that the absolute error is extremely small at the magnitude of [10.sup.-3]. The newly developed surface emission model should be very useful in modeling, improving our understanding, analyses, and predictions of the AMSR-E measurements. Index Terms--Microwave, modeling, surface emissivity, roughness.

An integral equation method (IEM) surface scattering model was examined in terms of its applicability to laboratory measurement and numerical simulations. New expressions for both single scattering and multiple scattering were obtained by rederiving the scattering coefficient to keep all the phase terms in the spectral representation of the Green's function. After quite intricate mathematical manipulations, a fairly compact form is obtained for the scattering coefficients. In addition, the Fresnel reflection coefficients used in the model were replaced by a transition function that takes surface roughness and permittivity into account. The results of comparisons with both the numerical simulations and measurements for the backscattering case indicate that the IEM is improved, becoming more accurate and practical to use. Index Terms--Backscattering, integral equation method (IEM) model, rough surface.

This paper presents a method, based on a fuzzy neural network, that uses fully polarimetric information for terrain and land-use classification of synthetic aperture radar (SAR) image. The proposed approach makes use of statistical properties of polarimetric data, and takes advantage of a fuzzy neural network. A distance measure, based on a complex Wishart distribution, is applied using the fuzzy c-means clustering algorithm, and the clustering result is then incorporated into the neural network. Instead of preselecting the polarization channels to form a feature vector, all elements of the polarimetric covariance matrix serve as the target feature vector as inputs to the neural network. It is thus expected that the neural network will include fully polarimetric backscattering information for image classification. With the generalization, adaptation, and other capabilities of the neural network, information contained in the covariance matrix, such as the amplitude, the phase difference, the degree of polarization, etc., can be fully explored. A test image, acquired by the .let Propulsion Laboratory Airborne SAR (AIRSAR) system, is used to demonstrate the advantages of the proposed method. It is shown that the proposed approach can greatly enhance the adaptability and the flexibility giving fully polarimetric SAR for terrain cover classification. The integration of fuzzy c-means (FCM) and fast generalization dynamic learning neural network (DLNN) capabilities makes the proposed algorithm an attractive and alternative method for polarimetric SAR classification. Index Terms--Fuzzy c-means, neural classification, polarimetric synthetic aperture radar, speckle filtering.