Fabio Dell'Acqua

Exploiting spectral and spatial information in hyperspectral urban data with high resolution

Very high resolution hyperspectral data should be very useful to provide detailed maps of urban land cover. In order to provide such maps, both accurate and precise classification tools need, however, to be developed. In this letter, new methods for classification of hyperspectral remote sensing data are investigated, with the primary focus on multiple classifications and spatial analysis to improve mapping accuracy in urban areas. In particular, we compare spatial reclassification and mathematical morphology approaches. We show results for classification of DAIS data over the town of Pavia, in northern Italy. Classification maps of two test areas are given, and the overall and individual class accuracies are analyzed with respect to the parameters of the proposed classification procedures.

Texture-based characterization of urban environments on satellite SAR images

We investigate the use of co-occurrence 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.

Detection of urban structures in SAR images by robust fuzzy clustering algorithms: the example of street tracking

The authors present a fuzzy approach to the analysis of airborne synthetic aperture radar (SAR) images of urban environments. In particular, they 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, they 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.

Improving urban road extraction in high-resolution images exploiting directional filtering, perceptual grouping, and simple topological concepts

In this letter, the problem of detecting urban road networks from high-resolution optical/synthetic aperture radar (SAR) images is addressed. To this end, this letter exploits a priori knowledge about road direction distribution in urban areas. In particular, this letter presents an adaptive filtering procedure able to capture the predominant directions of these roads and enhance the extraction results. After road element extraction, to both discard redundant segments and avoid gaps, a special perceptual grouping algorithm is devised, exploiting colinearity as well as proximity concepts. Finally, the road network topology is considered, checking for road intersections and regularizing the overall patterns using these focal points. The proposed procedure was tested on a pair of very high resolution images, one from an optical sensor and one from a SAR sensor. The experiments show an increase in both the completeness and the quality indexes for the extracted road network

Change Detection of Multitemporal SAR Data in Urban Areas Combining Feature-Based and Pixel-Based Techniques

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 sensors 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 Bam, Iran, stricken in 2003 by a serious earthquake

Improvements to urban area characterization using multitemporal and multiangle SAR images

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.

Remote Sensing and Earthquake Damage Assessment: Experiences, Limits, and Perspectives

In this paper, a survey of the techniques and data sets used to evaluate earthquake damages using remote sensing data is presented. After a few preliminary definitions about earthquake damage, their evaluation scale, and the difference between identification of damage “extent” and identification of damage “level,” the advantages and limits of different remote sensing data sets are presented. Furthermore, a survey of proposed algorithms for data interpretation and earthquake damage extraction is presented, and two examples of these algorithms and their results are discussed. According to the outcome of this survey, some open issues are finally presented and discussed, identifying possible research lines as well as working solutions.

Increased accuracy multiband urban classification using a neuro-fuzzy classifier

This letter presents an improvement of an already proposed neural classifier, designed to exploit multiband data over urban environments. The original classifier, based on an Adaptive Resonance Theory (ART) network followed by a fuzzy clustering step, is here improved by directly using a neuro-fuzzy approach, the fuzzy ARTMAP neural network. We show that significant advantages in the classifications could be obtained by tuning the fuzzy ARTMAP learning parameters. Overall accuracy has increased on the same dataset of aerial and Synthetic Aperture Radar (SAR) images of the original work. Moreover, the proposed change in the original classifier structure reduces the implementation complexity and increases its capability to adapt to new inputs. To demonstrate the robustness of this new approach, we offer results on a multiband AIRSAR dataset (C-, P- and L-band images) over the urban area of Broni, northern Italy.

Multitemporal/multiband SAR classification of urban areas using spatial analysis: statistical versus neural kernel-based approach

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.

Semi-automatic choice of scale-dependent features for satellite SAR image classification

In this work we compare two different approaches to the use of multiple scales in the classification process of satellite SAR images. These are (I) the multi-scale co-occurrence texture analysis and (II) the semivariogram approach. Moreover, we propose a scheme for optimizing the co-occurrence window size and the semivariogram lag distances in terms of classification accuracy performance. To improve the results even further, we introduce a methodology to compute the co-occurrence features with a window consistent with the local scale, provided by the semivariogram analysis. Examples of satellite SAR image segmentation for urban area characterization are shown to validate the procedure.