University of Colorado, Boulder (CO), USA

Parametric and nonparametric approaches to evaluate land-cover change detection using very high resolution (VHR) satellite imagery are applied to the analysis of the demolition of the Rocky Flats nuclear weapons facility located near Denver, CO. Both maximum-likelihood and neural network classifiers are used to validate a new parallel architecture which improves the accuracy when applied to VHR satellite imagery for the study of land-cover change between sequential satellite acquisitions. An enhancement of about 14% was found between the single-step classification and the new parallel architecture, confirming the advantage and the robust improvement obtained with this architecture regardless of the classification algorithm used. In this paper, we demonstrate and document the demolition and removal of hundreds of buildings taken down to bare soil between 2003 and 2005 at the Rocky Flats site.

In this paper, we investigate the performance of pulse-coupled neural networks (PCNNs) to detect the damage caused by an earthquake. PCNN is an unsupervised model in the sense that it does not need to be trained, which makes it an operational tool during crisis events when it is crucial to produce damage maps as soon as the post-event images are available. The damage map resulting from PCNN was validated at a block scale of 120x120m using ground truth obtained by a combination of ground survey and visual inspection of the before- and after-event images. The comparison showed agreement between the change measured by PCNN on block scale and the damage occurred.

The successful launch of panchromatic WorldView-1 and the planned launch of WorldView-2 will make a major contribution towards the advancement of the commercial remote sensing industry by providing improved capabilities, more frequent revisits and greater imaging flexibility with respect to the precursor QuickBird satellite. Remote sensing data from panchromatic systems have a potential for more detailed and accurate mapping of the urban environment with details of sub-meter ground resolution, but at the same time, they present additional complexities for information mining. In this study, very high-resolution panchromatic images from QuickBird and WorldView-1 have been used to accurately classify the land-use of four different urban environments: Las Vegas (U.S.A.), Rome (Italy), Washington D.C. (U.S.A.) and San Francisco (U.S.A.). The proposed method is based on the analysis of firstand second-order multi-scale textural features extracted from panchromatic data. For this purpose, textural parameters have been systematically investigated by computing the features over five different window sizes, three different directions and two different cell shifts for a total of 191 input features. Neural Network Pruning and saliency measurements made it possible to determine the most important textural features for sub-metric spatial resolution imagery of urban scenes. The results show that with a multi-scale approach it is possible to discriminate different asphalt surfaces, such as roads, highways and parking lots due to the different textural information content. This approach also makes it possible to differentiate building architectures, sizes and heights, such as residential houses, apartment blocks and towers with classification accuracies above 0.90 in terms of Kappa coefficient computed over more than a million independent validation pixels.

Remote sensing sensors for detecting urban damage and other surface changes due to earthquakes is gaining increasing interest. To this aim optical images can represent useful tools for this application thanks their very high ground geometric resolution, especially when more frequent revisit times will be feasible with the implementation of new missions and future possible constellations of satellites. Sub-meter resolution images at visible frequencies are able to provide information at the scale of a single building. This kind of information is extremely important if provided with sufficient timeliness to rescue teams. In this work, the earthquake that hit the ancient city of Bam, Iran, on December 26th, 2003 has been investigated. The urban area was very close to the epicenter of the seism which caused strong damage to the urban structures. Pre- and post-earthquake Quickbird panchromatic images have been used to show the capability of this data to map damage at building scale by means of segmentation approach based on the application of morphological operators. A validation process has been performed by comparing the map of damage levels at single building scale with a detailed ground-based damage map provided by in situ survey.

Using satellite sensors to detect urban damage and other surface changes due to earthquakes is gaining increasing interest. Optical images at different resolutions and radar images represent useful tools for this application, particularly when more frequent revisit times will be available with the implementation of new missions and future possible constellations of satellites. Very high resolution (VHR) images (on the order of 1 m or less) may provide information at the scale of a single building, whereas images at resolutions on the order of tens of meters may give indications of damage levels at a district scale. Both types of information may be extremely important if provided with sufficient timeliness to rescue teams. The earthquake that hit the city of Bam, Iran, has been taken as a test case, where QuickBird VHR optical images and advanced synthetic aperture radar data were available both before and after the event. Methods to process these data in order to detect damage and to extract features used to estimate damage levels are investigated in this paper, pointing out the significant potential of these satellite data and their possible synergy.