Search

This paper investigates the effect of apodization on the statistical properties of synthetic aperture radar (SAR) images and its impact on the capability of extracting information from homogeneous regions of apodized SAR images. The statistical model for the pixel complex amplitude of the apodized image is derived in terms of both probability density function and statistical moments. Knowledge of the statistical properties is then used to develop appropriate schemes for parameter estimation and supervised classification of homogeneous regions with different radar cross sections in apodized SAR images. The performance analysis shows that the new techniques (properly derived for the apodized case) provide information extraction capabilities only slightly worse than those provided by the conventional techniques applied to the nonapodized case. This allows us to conclude that the use of nonlinear apodization yields sidelobe level reduction and main lobe resolution preservation that can be traded with the small losses above. A full characterization of the estimation and classification performance of these new techniques shows that non-linear apodization globally introduces a performance degradation comparable to a reduction of the number of looks of a factor of 1.455 for a homogeneous region. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of supervised classification of remote sensing images in the presence of incomplete (nonexhaustive) training sets. The problem is analyzed according to two different perspectives: 1) description and recognition of a specific land-cover class by using single-class classifiers and 2) solution of multiclass problems with single-class classification techniques. In this framework, we analyze different one-class classifiers and introduce in the remote sensing community the support vector domain description method (SVDD). The SVDD is a kernel-based method that exhibits intrinsic regularization ability and robustness versus low numbers of high-dimensional samples. The SVDD technique is compared with other standard single-class methods both in problems focused on the recognition of a single specific land-cover class and in multiclass problems. For the latter, we properly define an easily scalable multiclass architecture capable to deal with incomplete training data. Experimental results, obtained on different kinds of data (synthetic, hyperspectral, and multisensor images), point out the effectiveness of the SVDD technique and provide important indications for driving the choice of the classification technique and architecture in the presence of incomplete training data. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to change detection in multitemporal synthetic aperture radar (SAR) images. The proposed approach exploits a wavelet-based multiscale decomposition of the log-ratio image (obtained by a comparison of the original multitemporal data) aimed at achieving different scales (levels) of representation of the change signal. Each scale is characterized by a different tradeoff between speckle reduction and preservation of geometrical details. For each pixel, a subset of reliable scales is identified on the basis of a local statistic measure applied to scale-dependent log-ratio images. The final change- detection result is obtained according to an adaptive scale-driven fusion algorithm. Experimental results obtained on multitemporal SAR images acquired by the ERS-1 satellite confirm the effective- ness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a system to integrate digital elevation model (DEM) enhancement and Earth Observation (EO) image analysis for realistic three-dimensional (3-D) rendering applications. There is an increasing interest in interferometric synthetic aperture radar (InSAR) data, principally due to the availability of the nearly global Shuttle Radar Topography Mission coverage. To remove artifacts and noise from an InSAR DEM, a nonstationary Bayesian filtering is applied that preserves structural information. Land-cover or man-made structures are easily recognized in an optical image. The corresponding geometry encapsulated in the DEM differs from our implicit perception and generally leads to unrealistic 3-D rendering. To improve this, DEM regularization is achieved using only the visualization dataset (optical image and DEM). It consists of extracting relevant information from the optical image and integrate them in the filtered DEM. To gather image information, an object-based description of large optical EO images is obtained in two stages 1) an image is segmented to create a partition of regions and 2) a novel dynamical algorithm is proposed to extract the regions and encode them in a tree structure. Regions are modeled by objects primitives stored in a database. Spatial relationships between regions are reflected by the presented tree of regions. Using the object-based description generation, structures to be integrated into the DEM are interactively selected and classified among a set of user-thematic. Each thematic is associated with a corresponding elevation modeling and enables to estimate the region's 3-D structure. The proposed object line processing provides more realistic 3-D visualizations. [ABSTRACT FROM AUTHOR]

A novel system for the classification of multitemporal synthetic aperture radar (SAR) images is presented. It has been developed by integrating an analysis of the multitemporal SAR signal physics with a pattern recognition approach. The system is made up of a feature-extraction module and a neural-network classifier, as well as a set of standard preprocessing procedures. The feature-extraction module derives a set of features from a series of multitemporal SAR images. These features are based on the concepts of long-term coherence and backscattering temporal variability and have been defined according to an analysis of the multitemporal SAR signal behavior in the presence of different land-cover classes. The neural-network classifier (which is based on a radial basis function neural architecture) properly exploits the multitemporal features for producing accurate land-cover maps. Thanks to the effectiveness of the extracted features, the number of measures that can be provided as input to the classifier is significantly smaller than the number of available multitemporal images. This reduces the complexity of the neural architecture (and consequently increases the generalization capabilities of the classifier) and relaxes the requirements relating to the number of training patterns to be used for classifier learning. Experimental results (obtained on a multitemporal series of European Remote Sensing 1 satellite SAR images) confirm the effectiveness of the proposed system, which exhibits both high classification accuracy and good stability versus parameter settings. These results also point out that properly integrating a pattern recognition procedure (based on machine learning) with an accurate feature extraction phase (based on the SAR sensor physics understanding) represents an effective approach to SAR data analysis. [ABSTRACT FROM AUTHOR]

A hyperspectral imaging sensor can reveal and uncover targets with very narrow diagnostic wavelengths. However, it comes at a price that it can also extract many unknown signal sources such as background and natural signatures as well as unwanted man-made objects, which cannot be identified visually or a priori. These unknown signal sources can be referred to as interferers, which generally play a more dominant role than noise in hyperspectral image analysis. Separating such interferers from signals and annihilating them subsequently prior to detection may be a more realistic approach. In many applications, the signals of interest can be further divided into desired signals for which we want to extract and undesired signals for which we want to eliminate to enhance signal detectability. This paper presents a signal-decomposed and interference-annihilated (SDIA) approach in applications of hyperspectral target detection. It treats interferers and undesired signals as separate signal sources that can be eliminated prior to target detection. In doing so, a signal-decomposed interference/noise (SDIN) model is suggested in this paper. With the proposed SDIN model, the orthogonal subspace projection-based model and the signal/background/noise model can be included as its special cases. As shown in the experiments, the SDIN model-based SDIA approach generally can improve the performance of the commonly used generalized-likelihood ratio test and constrained energy minimization approach on target detection and classification. [ABSTRACT FROM AUTHOR]