22,861 research outputs found
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
Discriminating small wooded elements in rural landscape from aerial photography: a hybrid pixel/object-based analysis approach
While small, fragmented wooded elements do not represent a large surface area in
agricultural landscape, their role in the sustainability of ecological processes is
recognized widely. Unfortunately, landscape ecology studies suffer from the lack
of methods for automatic detection of these elements. We propose a hybrid
approach using both aerial photographs and ancillary data of coarser resolution
to automatically discriminate small wooded elements. First, a spectral and textural
analysis is performed to identify all the planted-tree areas in the digital photograph.
Secondly, an object-orientated spatial analysis using the two data sources
and including a multi-resolution segmentation is applied to distinguish between
large and small woods, copses, hedgerows and scattered trees. The results show the
usefulness of the hybrid approach and the prospects for future ecological
applications
An object-based classification approach for mapping "migrant housing" in the mega-urban area of the Pearl River Delta (China)
Urban areas develop on formal and informal levels. Informal development is often highly dynamic, leading to a lag of spatial information about urban structure types. In this work, an object-based remote sensing approach will be presented to map the migrant housing urban structure type in the Pearl River Delta, China. SPOT5 data were utilized for the classification (auxiliary data, particularly up-to-date cadastral data, were not available). A hierarchically structured classification process was used to create (spectral) independence from single satellite scenes and to arrive at a transferrable classification process. Using the presented classification approach, an overall classification accuracy of migrant housing of 68.0% is attained
Site Characterization Using Integrated Imaging Analysis Methods on Satellite Data of the Islamabad, Pakistan, Region
We develop an integrated digital imaging analysis approach to produce a first-approximation site characterization map for Islamabad, Pakistan, based on remote-sensing data. We apply both pixel-based and object-oriented digital imaging analysis methods to characterize detailed (1:50,000) geomorphology and geology from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. We use stereo-correlated relative digital elevation models (rDEMs) derived from ASTER data, as well as spectra in the visible near-infrared (VNIR) to thermal infrared (TIR) domains. The resulting geomorphic units in the study area are classified as mountain (including the Margala Hills and the Khairi Murat Ridge), piedmont, and basin terrain units. The local geologic units are classified as limestone in the Margala Hills and the Khairi Murat Ridge and sandstone rock types for the piedmonts and basins. Shear-wave velocities for these units are assigned in ranges based on established correlations in California. These ranges include Vs30-values to be greater than 500 m/sec for mountain units, 200–600 m/sec for piedmont units, and less than 300 m/sec for basin units. While the resulting map provides the basis for incorporating site response in an assessment of seismic hazard for Islamabad, it also demonstrates the potential use of remote-sensing data for site characterization in regions where only limited conventional mapping has been done
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
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