Coastal zone detection is an important task in sustainable development and environmental protection. For coastline zone monitoring, extraction and analysis of coastline changes are an important task. Detection of coas...
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Mapping landslides and building landslides inventory have received a special attention from a wide range of specialist. In building a landslide inventory an important step is the spatial delineation of the landslides ...
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Mapping landslides and building landslides inventory have received a special attention from a wide range of specialist. In building a landslide inventory an important step is the spatial delineation of the landslides body, followed by the landslides classification according with an international used classification system and the identification of other landslides characteristics. The main methods for landslides mapping are based on field observation, image interpretation and stereo-restitution. Our paper discusses a semi-automated process based on objected-oriented analysis for landslides bodies' delineation. Several recent papers Moine et al. 2009, Tapas et al. 2010 had similar approaches for landslides bodies' delineation and classification using objected-oriented analysis combined with spectral and morphometric properties of the landslides. Our approach is similar with Tapas et al. 2010, but we take into account, besides the morphometric properties, the meteorological data for the periods when the landslides have occurred. The algorithm is using high resolution aerial images with a spatial resolution 0.5 meters, a DEM with a spatial resolution of 2.5 meters and daily meteorological data for the year 2005. The meteorological data was spatial interpolated and the images were used in the objected oriented analysis and this has led to a significant increase in the number of corrected indentified landslides. The algorithm was tested in the administrative area of Breaza Town from Romanian Curvature Sub-Carpathians, for which a detailed landslides inventory was available
Satellite hyperspectral imagery has been widely used in geological and agricultural studies. However, its use for an urban environment is limited due to low spatial resolution. Few methods in the literature attempt to...
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A method based on textural features and Adaboost for detecting buildings in satellite images is proposed. Several local textural features including mean and standard deviation of image intensity and gradient, Zernike ...
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A method based on textural features and Adaboost for detecting buildings in satellite images is proposed. Several local textural features including mean and standard deviation of image intensity and gradient, Zernike moments, Circular-Mellin features, Haralick features, Fourier Power Spectrum, Wavelets, Gabor Filters, and a set features extracted from HSV color space are extracted. Adaboost learning algorithm is employed for both classification and determining the beneficial feature subset, due to its feature selector nature. Some operation including morphological operators are applied for post processing. The approach was tested on a set of satellite images having different types of buildings and promising experimental results are achieved.
Very high spatial resolution (VHR) images allow to feature man-made structures such as roads and thus enable their accurate analysis. Geometrical characteristics can be extracted using mathematical morphology. However...
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Very high spatial resolution (VHR) images allow to feature man-made structures such as roads and thus enable their accurate analysis. Geometrical characteristics can be extracted using mathematical morphology. However, the prior choice of a reference shape (structuring element) introduces a shape-bias. This paper presents a new method for extracting roads in Very High Resolution remotely sensed images based on advanced directional morphological operators. The proposed approach introduces the use of Path Openings and Path Closings in order to extract structural pixel information. These morphological operators remain flexible enough to fit rectilinear and slightly curved structures since they do not depend on the choice of a structural element shape. As a consequence, they outperform standard approaches using rotating rectangular structuring elements. The method consists in building a granulometry chain using Path Openings and Path Closing to construct Morphological Profiles. For each pixel, the Morphological Profile constitutes the feature vector on which our road extraction is based. (C) 2009 Published by Elsevier B.V.
The determination of conjugate points in a stereo image pair, i.e. image matching, is the critical step to realize automatic surveying and recognition in digital photogrammetric processing. The accuracy of image match...
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ISBN:
(纸本)9780819480880
The determination of conjugate points in a stereo image pair, i.e. image matching, is the critical step to realize automatic surveying and recognition in digital photogrammetric processing. The accuracy of image matching is closely related to specific matching algorithm as well as images. In this paper, the qualitative and quantitative relationships between the matching accuracy and the image metrics are studied at the basic of Least Squares image Matching algorithm (LSIMA). Firstly, the algorithm is deduced mathematically, and then the main image metrics affecting the matching accuracy are presented, including total variation (TV) metric and difference of signal-to-noise ratio (DSNR) metric. Subsequently, variations of matching accuracy with TV and DSNR are analyzed, and mathematical model between them is developed. Studies show that the matching accuracy presents the natural exponential rule along with TV and DSNR of image pairs. Besides, parameters of the model are estimated and the model is verified by simulation experiments. Finally, the correctness of the model is verified using real remotesensingimages. Experimental results demonstrate the robustness and accuracy of the proposed model.
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