Search

In January 2006, the Data Fusion Committee of the IEEE Geoscience and Remote Sensing Society launched a public contest for pansharpening algorithms, which aimed to identify the ones that perform best. Seven research groups worldwide participated in the contest, testing eight algorithms following different philosophies [component substitution, multiresolution analysis (MRA), detail injection, etc.]. Several complete data sets from two different sensors, namely, QuickBird and simulated Pleiades, were delivered to all participants. The fusion results were collected and evaluated, both visually and objectively. Quantitative results of pansharpening were possible owing to the availability of reference originals obtained either by simulating the data collected from the satellite sensor by means of higher resolution data from an airborne platform, in the case of the Pleiades data, or by first degrading all the available data to a coarser resolution and saving the original as the reference, in the case of the QuickBird data. The evaluation results were presented during the special session on Data Fusion at the 2006 International Geoscience and Remote Sensing Symposium in Denver, and these are discussed in further detail in this paper. Two algorithms outperform all the others, the visual analysis being confirmed by the quantitative evaluation. These two methods share the same philosophy: they basically rely on MRA and employ adaptive models for the injection of high-pass details. Index Terms--Image fusion, multispectral (MS) imagery, pansharpening, quality assessment, QuickBird (QB), simulated Pleiades data.

In this paper, a mapping procedure exploiting object boundaries in very high-resolution (VHR) images is proposed. After discrimination between boundary and nonboundary pixel sets, each of the two sets is separately classified. The former are labeled using a neural network (NN), and the shape of the pixel set is finely tuned by enforcing a few geometrical constraints, while the latter are classified using an adaptive Markov random field (MRF) model. The two mapping outputs are finally combined through a decision fusion process. Experimental results on hyperspectral and satellite VHR imagery show the superior performance of this method over conventional NN and MRF classifiers. Index Terms--Land cover mapping, spatially adaptive classifier, urban remote sensing, very high-resolution (VHR) sensors.

In this paper, the problem of rapid earthquake damage detection in urban areas using multitemporal synthetic aperture radar data is addressed. It is shown that the combination of intensity and phase features enhances the damage pattern extracted from the data temporal stack using a spatially aware classifier. Moreover, the use of ancillary data, easily available for urban areas, further improves the accuracy by discarding uninteresting parts of the scene and forcing homogeneous classification within city blocks to avoid 'class-blurring' effects consequential to the window-based computation of relevant measures. The procedure is validated based on results for the town of Bam, Iran, and compared with ground-based survey maps. Index Terms--Ancillary data, earthquake damage detection, multitemporal SAR.

A general processing framework for urban road network extraction in high-resolution synthetic aperture radar images is proposed. It is based on novel multiscale detection of street candidates, followed by optimization using a Markov random field description of the road network. The latter step, in the path of recent technical literature, is enriched by the inclusion of a priori knowledge about road junctions and the automatic choice of most of the involved parameters. Advantages over existing and previous extraction and optimization procedures are proved by comparison using data from different sensors and locations. Index Terms--High-resolution synthetic aperture radar (SAR), Markov random fields (MRFs), road network, urban remote sensing.

In this paper, the problem of change detection from synthetic aperture radar (SAR) images is addressed. Feature-level change-detection algorithms are still in their preliminary design stage. Indeed, while pixel-based approaches are already implemented into existing, commercial software, this is not the case for feature comparison approaches. Here, the authors propose a joint use of both approaches. The approach is based on the extraction and comparison of linear features from multiple SAR images, to confirm pixel-based changes. Though simple, the methodology proves to be effective, irrespectively of misregistration errors due to reprojection problems or difference in the sensor's viewing geometry, which are common in multitemporal SAR images. The procedure is validated through synthetic examples, but also two real change-detection situations, using airborne and satellite SAR data over the area of the Getty Museum, Los Angeles, as well as over an area around the city of Barn, Iran, stricken in 2003 by a serious earthquake. Index Terms--Change detection, linear element extraction, synthetic aperture radar (SAR).

In this paper, we derive two techniques for the classification of multifrequency/multitemporal polarimetric SAR images, based respectively on a statistical and on a neural approach. Both techniques are especially designed to exploit the spatial structure of the observed scene, thus allowing more stable classification results. Such techniques are useful when looking at medium- to large-scale features, like the boundaries between urban and nonurban areas. They are applied to a set of SIR-C images of a urban area, to test their effectiveness in the identification of the different classes that compose the observed scene. A lower and an upper bound to the classification performance are introduced to characterize their limits. They correspond respectively to pixel-by-pixel classification and to the joint classification of the pixels belonging to the different classes identified in the ground truth. The results achieved with the two approaches are quantitatively analyzed by comparing them to the ground truth. Moreover, a hybrid approach is presented, where the homogeneous regions identified through statistical segmentation are classified using a neurofuzzy technique. Finally, a quantitative analysis of the results achieved with all the proposed techniques is carried out, showing that their classification performance is much higher than the lower bound and reasonably close to the upper bound. This is a consequence of their effectiveness in the exploitation of the spatial information. Index Terms--Image processing, synthetic aperture radar (SAR), spatial analysis, urban areas.

In this paper, we present some improvements to urban area characterization by means of synthetic aperture radar (SAR) images using multitemporal and multiangle datasets. The first aim of this research is to show that a temporal sequence of satellite SAR data may improve the classification accuracy and the discriminability of land cover classes in an urban area. Similarly, a second point worth discussing is to what extent multiangle SAR data allows extracting complementary urban features, exploiting different acquisition geometries. To these aims, in this paper, we show results on the same urban test site (Pavia, northern Italy), referring to a sequence of European Remote Sensing Satellite 1/2 (ERS-1/2) C-band images and to a set of simulated X-band data with a finer spatial resolution and different viewing angles. In particular, the multitemporal data is analyzed by means of a novel procedure based on a neuro-fuzzy classifier whose input is a subset of the ERS sequence chosen using the histogram distance index. Instead, the multiangle dataset is used to provide a better characterization of the road network in the area, overcoming effects due to the orientation of the SAR sensor. Index Terms--Road extraction, synthetic aperture radar (SAR) image analysis, urban remote sensing.

In this paper, we present how we used neural networks (NNs) and a pyramidal approach to model the data obtained by a weather radar and to short-range forecast the rainfall behavior. Very short-range forecast is useful, for instance, for estimating the path attenuation in terrestrial point-to-point communications. Radial basis function NNs are used both to approximate the rain field and to forecast the parameters of this approximation in order to anticipate the movements and changes in geometric characteristics of significant meteorological structures. The procedure is validated by applying it to actual weather radar data and comparing the outcome with a linear forecasting method, the steady-state method, and the persistence method. The same approach is probably useful also for predicting the behavior of other meteorological phenomena like clusters of clouds observed from satellites. Index Terms--Neural forecasting, rain pattern analysis, weather radar.

In this letter, we investigate the use of cooccrrence texture measures to provide information on different building densities inside a town structure. We try to improve the pixel-by-pixel classification of an urban area by considering texture measures as a means for block analysis and classification. We find some interesting hints concerning the optimal dimension of the window to be considered for texture measures, as well as the most useful measures. Moreover, we show that it is possible to use medium-resolution readily available satellite synthetic aperture radar images for a more refined urban analysis than previously shown. Index Terms--Pattern recognition, synthetic aperture radar, terrain mapping, urban areas.

In this paper, we describe a method to obtain a reliable set of elevation data suitable for data validation on the Shuttle Radar Topography Mission (SRTM), starting from laser scanning measurements on an urban test site: Pavia, Northern Italy. The elevation dataset is obtained through extraction of digital terrain models. The source digital surface model is first filtered by means of a lowpass or morphological kernel. Then, buildings are suppressed through analysis of the height histogram. Finally, a lowpass filter suppresses the surviving elevation artifacts. We show that, starting from a digital surface model at 1-m ground resolution, we end up with a digital terrain model that can be used as a ground truth for SRTM topographic analysis of an urban area. Index Terms--Image processing, laser scanning, SRTM, urban remote sensing.

In this paper, the neural network approach is applied to the detection of cylindric objects as well as their geometric and electrical characteristics inside a given investigation domain. The electric field values scattered by the object and available at a small number of locations are fed into the network, whose output is the dielectric permittivity, and the location and radius of the cylinder. The results are evaluated using different sets of testing data, and the dependence of the various output parameters to the input are considered. The algorithm performance shows that the approach is able to solve the inverse scattering problem quickly. This may be useful for real-time remote-sensing applications. Index Terms - Buried object detection, inverse problems, neural networks.

In this work, we present a fuzzy approach to the analysis of airborne synthetic aperture radar (SAR) images of urban environments. In particular, we want to show how to implement structure extraction algorithms based on fuzzy clustering unsupervised approaches. To this aim, the idea is to segment first the sensed data and recognize very basic urban classes (vegetation, roads, and built areas). Then, from these classes, we extract structures and infrastructures of interest. The initial clustering step is obtained by means of fuzzy logic concepts and the successive analyses are able to exploit the corresponding fuzzy partition. As a possible complete procedure for urban SAR images, in this paper, we focus on the street tracking and extraction problem. Three road extraction algorithms available in literature (namely, the connectivity weighted Hough transform (CWHT), the rotation Hough transform, and the shortest path extraction) have been modified to be consistent with the previously computed fuzzy clustering results. Their different capabilities are applied for the characterization of streets with different width and shape. The whole approach is validated by the analysis of AIRSAR images of Los Angeles, CA. Index Terms--Fuzzy clustering, street extraction, urban remote sensing.

In this paper, we work on meteorological satellite image archives and provide a novel and useful query-by-shape tool. To this aim, we first present the point diffusion technique (PDT), a fast and efficient method for shape similarity evaluation. Thanks to its very structure, this approach is suitable to handle objects whose shape is not well defined and can be represented by a set of sparse points. PDT is thus suitable for application to similarity-based retrieval from remotely sensed image archives, where shapes are hardly defined but are still among the major features of interest. Moreover, we prove here that PDT is almost as effective as more standard procedures for shape-based database queries, although significantly faster. In other words, it manages to combine retrieval speed and precision, the features of greatest importance for a first remote sensing data prescreening in many applications. Archives of meteorological satellite images are typical examples of very large-sized, remote sensing-based databases with a special attention for shape features. Each meteorological satellite produces terabytes of data every day, a large part of which is not immediately analyzed and ends being stored in archives. In this paper, the application of PDT to such a database is presented and discussed, and a comparison with a standard method developed for meteorological shape analysis is provided. Index Terms--Database searching, image shape analysis, meteorology.

In this paper, the task of extracting significant built structure in digital surface models (DSM) is analyzed. The original data are obtained by means of interferometric SAR or LIDAR techniques and have different resolution and noise characteristics. This work aims to make a comparison of what (and how precisely) it is possible to detect and extract starting from these models, taking into account their differences but applying to them the same planar approximation approach. To this aim, data over Los Angeles and Denver is considered and evaluated. The results show that LIDAR data provide a better shape characterization of each building, and not simply because of their higher resolution. Indeed, less accurate results obtained starting from radar data are mainly due to shadowing/layover effects, which can be only partially corrected by means of the segmentation procedures. However, better results than those already presented in the literature could be achieved by using the IFSAR data correlation map.

In this paper, a processing chain for the spatial analysis of the data recorded by a ground penetrating radar (GPR) is presented. In particular, the detection and localization of pipes is implemented by exploiting the a priori knowledge that a buried cylinder gives rise to a hyperbolic signature in GPR images. The image interpretation is performed by a suitably trained simple neural detector after some preprocessing steps aiming toward the enhancement of the buried objects' signatures. The algorithm has been tested on actual GPR images and compared with the information extracted by a trained human operator; and the agreement is extremely satisfying. Moreover, the possibilities and advantages to exploiting some sort of 'spatial diversity' by combining the analysis of data simultaneously recorded by different antennas are presented and discussed. Index Terms--GPR, image analysis, neural networks.

In this paper, we present a complete procedure for the extraction and characterization of building structures starting from the three-dimensional (3-D) terrain elevation data provided by interferometric SAR measurements. Each building is detected and isolated from the surroundings by means of a suitably modified machine vision approach, originally developed for range image segmentation. The procedure is based on a local approximation of the 3-D data by means of best-fitting planes. In this way, a building footprint, height and position, as well as its description with a simple 3-D model, are recovered by a self consistent partitioning of the topographic surface reconstructed from interferometric radar data. Index Terms--SAR urban analysis, 3-D building extraction.

Images from meteorological satellites or weather radars must be often interpreted at a higher level than simple pixel classification, and shape analysis with reliable and fast methods may be necessary. For instance, it is useful to analyze the temporal evolution of a rain event by means of a reliable tracking of the rain patterns at different scales. However, this task of tracking objects continuously changing their shapes is challenging. In this paper, we show how the application of a recently introduced numerical technique, called boundary integral-resonant mode expansion (BI-RME), to weather radar and meteorological satellite data could be used to achieve more information about the evolution in time of rain patterns or other meteorological structures of interest. In order to demonstrate the efficiency and robustness of our approach, several examples of image processing are considered in this paper. Applications to both operational tracking of clouds to produce wind fields and hurricane tracking are presented. Index Terms - Contour tracking, remote-sensing image processing, satellite and radar meteorology.