10,688 research outputs found
Topology, homogeneity and scale factors for object detection: application of eCognition software for urban mapping using multispectral satellite image
The research scope of this paper is to apply spatial object based image
analysis (OBIA) method for processing panchromatic multispectral image covering
study area of Brussels for urban mapping. The aim is to map different land
cover types and more specifically, built-up areas from the very high resolution
(VHR) satellite image using OBIA approach. A case study covers urban landscapes
in the eastern areas of the city of Brussels, Belgium. Technically, this
research was performed in eCognition raster processing software demonstrating
excellent results of image segmentation and classification. The tools embedded
in eCognition enabled to perform image segmentation and objects classification
processes in a semi-automated regime, which is useful for the city planning,
spatial analysis and urban growth analysis. The combination of the OBIA method
together with technical tools of the eCognition demonstrated applicability of
this method for urban mapping in densely populated areas, e.g. in megapolis and
capital cities. The methodology included multiresolution segmentation and
classification of the created objects.Comment: 6 pages, 12 figures, INSO2015, Ed. by A. Girgvliani et al. Akaki
Tsereteli State University, Kutaisi (Imereti), Georgi
Weighted simplicial complex reconstruction from mobile laser scanning using sensor topology
We propose a new method for the reconstruction of simplicial complexes
(combining points, edges and triangles) from 3D point clouds from Mobile Laser
Scanning (MLS). Our method uses the inherent topology of the MLS sensor to
define a spatial adjacency relationship between points. We then investigate
each possible connexion between adjacent points, weighted according to its
distance to the sensor, and filter them by searching collinear structures in
the scene, or structures perpendicular to the laser beams. Next, we create and
filter triangles for each triplet of self-connected edges and according to
their local planarity. We compare our results to an unweighted simplicial
complex reconstruction.Comment: 8 pages, 11 figures, CFPT 2018. arXiv admin note: substantial text
overlap with arXiv:1802.0748
Automatic Object-Oriented, Spectral-Spatial Feature Extraction Driven by Tobler’s First Law of Geography for Very High Resolution Aerial Imagery Classification
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)Aerial image classification has become popular and has attracted extensive research efforts in recent decades. The main challenge lies in its very high spatial resolution but relatively insufficient spectral information. To this end, spatial-spectral feature extraction is a popular strategy for classification. However, parameter determination for that feature extraction is usually time-consuming and depends excessively on experience. In this paper, an automatic spatial feature extraction approach based on image raster and segmental vector data cross-analysis is proposed for the classification of very high spatial resolution (VHSR) aerial imagery. First, multi-resolution segmentation is used to generate strongly homogeneous image objects and extract corresponding vectors. Then, to automatically explore the region of a ground target, two rules, which are derived from Tobler’s First Law of Geography (TFL) and a topological relationship of vector data, are integrated to constrain the extension of a region around a central object. Third, the shape and size of the extended region are described. A final classification map is achieved through a supervised classifier using shape, size, and spectral features. Experiments on three real aerial images of VHSR (0.1 to 0.32 m) are done to evaluate effectiveness and robustness of the proposed approach. Comparisons to state-of-the-art methods demonstrate the superiority of the proposed method in VHSR image classification.Peer Reviewe
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