3,600 research outputs found
GeoSay: A Geometric Saliency for Extracting Buildings in Remote Sensing Images
Automatic extraction of buildings in remote sensing images is an important
but challenging task and finds many applications in different fields such as
urban planning, navigation and so on. This paper addresses the problem of
buildings extraction in very high-spatial-resolution (VHSR) remote sensing (RS)
images, whose spatial resolution is often up to half meters and provides rich
information about buildings. Based on the observation that buildings in VHSR-RS
images are always more distinguishable in geometry than in texture or spectral
domain, this paper proposes a geometric building index (GBI) for accurate
building extraction, by computing the geometric saliency from VHSR-RS images.
More precisely, given an image, the geometric saliency is derived from a
mid-level geometric representations based on meaningful junctions that can
locally describe geometrical structures of images. The resulting GBI is finally
measured by integrating the derived geometric saliency of buildings.
Experiments on three public and commonly used datasets demonstrate that the
proposed GBI achieves the state-of-the-art performance and shows impressive
generalization capability. Additionally, GBI preserves both the exact position
and accurate shape of single buildings compared to existing methods
Bootstrapped CNNs for Building Segmentation on RGB-D Aerial Imagery
Detection of buildings and other objects from aerial images has various
applications in urban planning and map making. Automated building detection
from aerial imagery is a challenging task, as it is prone to varying lighting
conditions, shadows and occlusions. Convolutional Neural Networks (CNNs) are
robust against some of these variations, although they fail to distinguish easy
and difficult examples. We train a detection algorithm from RGB-D images to
obtain a segmented mask by using the CNN architecture DenseNet.First, we
improve the performance of the model by applying a statistical re-sampling
technique called Bootstrapping and demonstrate that more informative examples
are retained. Second, the proposed method outperforms the non-bootstrapped
version by utilizing only one-sixth of the original training data and it
obtains a precision-recall break-even of 95.10% on our aerial imagery dataset.Comment: Published at ISPRS Annals of the Photogrammetry, Remote Sensing and
Spatial Information Science
Radar shadow detection in SAR images using DEM and projections
Synthetic aperture radar (SAR) images are widely used in target recognition
tasks nowadays. In this letter, we propose an automatic approach for radar
shadow detection and extraction from SAR images utilizing geometric projections
along with the digital elevation model (DEM) which corresponds to the given
geo-referenced SAR image. First, the DEM is rotated into the radar geometry so
that each row would match that of a radar line of sight. Next, we extract the
shadow regions by processing row by row until the image is covered fully. We
test the proposed shadow detection approach on different DEMs and a simulated
1D signals and 2D hills and volleys modeled by various variance based Gaussian
functions. Experimental results indicate the proposed algorithm produces good
results in detecting shadows in SAR images with high resolution.Comment: 10 pages, 6 figure
Use of multi-angle high-resolution imagery and 3D information for urban land-cover classification: a case study on Istanbul
The BELSPO-MAMUD project focuses on the use of Remote Sensing data for measuring and modelling urban dynamics. Remote sensing is a wonderful tool to produce long time-series of high resolution maps of sealed surface useful for this purpose. In the urban context of Istanbul, a very dynamic city, recent high resolution satellite images and medium resolution images from the past have been exploited to calibrate and validate a regression-based sub-pixel classification method allowing this production.
In this context it’s a tricky task for several reasons: prominent occurrence of shadowed and occluded areas and urban canyons, spectral confusions between urban and non-urban materials at ground and roof levels, moderately hilly relief ... To cope with these difficulties the combined use of three types of data may be helpful: diachronic (i), multi-angle and 3D data. A master multispectral and panchromatic QuickBird image and a panchromatic Ikonos stereopair, all acquired in March 2002, were used in combination with a multispectral and panchromatic Ikonos image of May 2005. A DSM was generated from the Ikonos stereopair and building vector file. It was used for orthorectification, building height estimation and classification procedure. The area covered by the high resolution products was divided in 3 partitions and each one was classified independently. This application demonstrates that recent high resolution land-cover classification produced using multi-date, multi-angle and DSM can be used to produce sealed surface maps from longer timeseries of medium resolution images over large urban areas enabling so the analysis of urban dynamics
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