Urban Remote Sensing Joint Event 

About: Urban Remote Sensing Joint Event is an academic conference. The conference publishes majorly in the area(s): Synthetic aperture radar & Feature extraction. Over the lifetime, 861 publications have been published by the conference receiving 6438 citations.

Application of high-resolution satellite images to detailed landslide hazard assessment

Abstract: The study investigates and demonstrates the state of the art in remote sensing techniques for detailed landslide hazard assessment over large areas using (i) single and (ii) stereo satellite images from IKONOS Very High Resolution (VHR) sensor. The image fusion technique provides the ability for detailed landslide interpretation using single image and this is comparable to that obtainable from 1:10,000 scale air photos. The Pan-sharpening method of image fusion permits most of the qualitative (spatial and spectral) parameters used in air photo interpretation to be available on satellite images. For more detailed investigation such as the evidence of shadow, positional relationships to streams and ridges, stereoscopic viewing using a pair of stereo-images can be used. The DEM created from IKONOS stereo images appears to be much more accurate, and sensitive to micro-scale terrain features, than a DEM created with digital contour data with 2m contour interval, when both are compared with a high resolution photogrammetric model. This terrain sensitivity permits interpretation of recent landslides, as small as 2-3m in width on pan-sharpened, stereo IKONOS images, as well as relict landslides over 50 years old.

Fusion of Difference Images for Change Detection Over Urban Areas

Abstract: As a result of urbanization, land use/land cover classes in urban areas are changing rapidly, and this trend increased in the recent years. Change information detected from multi-temporal remote sensing images can thus help to understand urban development and to effectively support urban planning. Differences in reflectance spectra, easily obtained by multi-temporal remote sensing images, are important indicators to characterize these changes. Although many algorithms were proposed to generate difference images, the results are usually greatly inconsistent. In order to integrate the merits of different algorithms to recognize spectral changes, fusion techniques merging multiple difference images are proposed and implemented in this paper. Feature and decision level fusion are used to combine simple change detectors, and to build an automatic change detection procedure. The proposed approach is tested with multi-temporal CBERS and HJ-1 images, and experimental results demonstrate its effectiveness and reliability. By integrating different change information, the appropriate fusion method can be selected according to the specific application in order to minimize the omission or the commission errors.

Automatic Building Extraction in VHR Images Using Advanced Morphological Operators

Abstract: This paper presents a new method for buildings extraction in Very High Resolution (VHR) remotely sensed images based on binary mathematical morphology (MM) operators. The proposed approach involves several advanced morphological operators among which an adaptive hit-or-miss transform with varying sizes and shapes of the structuring element and a bidimensional granulometry intended to determine the optimal filtering parameters automatically. A clustering-based approach for image binarization is also introduced. This one avoids an empirical thresholding of input panchromatic images. Experiments made on a Quickbird VHR-image show the effectiveness of the method.

Overview of DMSP nightime lights and future possibilities

Abstract: The Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) has a unique capability to collect low-light imaging data of the earth at night. The OLS and its predecessors have collected this style of data on a nightly global basis since 1972. The digital archive of OLS data extends back to 1992. Over the years several global nighttime lights products have been generated. NGDC has now produced a set of global cloud-free nighttime lights products, specifically processed for the detection of changes in lighting emitted by human settlements, spanning 1992-93 to 2008. While the OLS is far from ideal for observing nighttime lights, the DMSP nighttime lights products have been successfully used in modeling the spatial distribution of population density, carbon emissions, and economic activity.

Point cloud segmentation towards urban ground modeling

Abstract: This paper presents a new method for segmentation and interpretation of 3D point clouds from mobile LIDAR data. The main contribution of this work is the automatic detection and classification of artifacts located at the ground level. The detection is based on Top-Hat of hole filling algorithm of range images. Then, several features are extracted from the detected connected components (CCs). Afterward, a stepwise forward variable selection by using Wilk's Lambda criterion is performed. Finally, CCs are classified in four categories (lampposts, pedestrians, cars, the others) by using a SVM machine learning method.