The preparation of control data is a primary concern in many supervised classification schemes. In coral reef mapping, this issue becomes more severe for three reasons: (1) control samples, located ...beneath the water, are quite difficult and costly to access; (2) because of the high spatial variability of coral reef habitats, it is very difficult to obtain high-quality samples; and (3) pure training samples are also hardly achievable. These issues, namely quantity, quality, and impurity challenges, are the main focus of this study. Three classification algorithms, including Maximum Likelihood Classifier (MLC), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs), are comprehensively evaluated, and their requirements for control data are determined. To accomplish this, rich field data, collected from diving off of Lizard Island in eastern Australia, and Landsat-8 images are used as the input data. With respect to accuracy, ANN is best, as it can deal with the complexity of coral reef environments; however, it requires a higher number of training samples (i.e. ANN cannot manage the quantity challenge). On the other hand, SVM shows the best resistance against the quantity and impurity challenges. Being aware of these points, a coral reef map is produced, for the first time, of the northern Persian Gulf, a coral habitat with very special environmental conditions. In this region, SVM achieved 68.42% overall accuracy, even though a very limited field work campaign was conducted to provide the control data.
Spatial data and related technologies have proven to be crucial for effective collaborative decision-making in disaster management. However, there are currently substantial problems with ...availability, access and usage of reliable, up-to-date and accurate data for disaster management. This is a very important aspect to disaster response as timely, up-to-date and accurate spatial data describing the current situation is paramount to successfully responding to an emergency. This includes information about available resources, access to roads and damaged areas, required resources and required disaster response operations that should be available and accessible for use in a short period of time. Any problem or delay in data collection, access, usage and dissemination has negative impacts on the quality of decision-making and hence the quality of disaster response. Therefore, it is necessary to utilize appropriate frameworks and technologies to resolve current spatial data problems for disaster management.
This paper aims to address the role of Spatial Data Infrastructure (SDI) as a framework for the development of a web-based system as a tool for facilitating disaster management by resolving current problems with spatial data. It is argued that the design and implementation of an SDI model and consideration of SDI development factors and issues, together with development of a web-based GIS, can assist disaster management agencies to improve the quality of their decision-making and increase efficiency and effectiveness in all levels of disaster management activities.
The paper is based on an ongoing research project on the development of an SDI conceptual model and a prototype web-based system which can facilitate sharing, access and usage of spatial data in disaster management, particularly disaster response.
3D Building Reconstruction Using Dense Photogrammetric Point Cloud Malihi, S.; Valadan Zoej, M. J.; Hahn, M. ...
International archives of the photogrammetry, remote sensing and spatial information sciences.,
01/2016, Letnik:
XLI-B3
Journal Article, Conference Proceeding
Recenzirano
Odprti dostop
Three dimensional models of urban areas play an important role in city planning, disaster management, city navigation and other applications. Reconstruction of 3D building models is still a ...challenging issue in 3D city modelling. Point clouds generated from multi view images of UAV is a novel source of spatial data, which is used in this research for building reconstruction. The process starts with the segmentation of point clouds of roofs and walls into planar groups. By generating related surfaces and using geometrical constraints plus considering symmetry, a 3d model of building is reconstructed. In a refinement step, dormers are extracted, and their models are reconstructed. The details of the 3d reconstructed model are in LoD3 level, with respect to modelling eaves, fractions of roof and dormers.
The estimation of cultivation area and categorizing the agricultural product types is one of the prerequisites for achieving sustainable development in the agricultural studies. In this study, an ...unsupervised zoning the cultivation areas with the same cultivation pattern in Golestan province is on the agenda. Therefore, due to wide spatial range, high temporal resolution and easy access of 16-day products of the vegetation of the MODIS sensor which acquired in a year (From November 2017 to October 2018), these images are used in this research. In the proposed method, after the generating of NDVI vegetation time series as a hyper-cube and separating farmlands’ boundaries in Golestan province using the land-use map; the Sequential Maximum Angle Convex Cone (SMACC) endmember extraction algorithm and the maximum number of product variation using the statistical information of the region (Obtained from the statistics centre of Iran) are used to extract endmembers of the hyper-cube. In the following, the timing responses of the NDVI, identified as endmembers, will be refined in the second phase. In this process, identifying and eliminating noise signals (unrelated to cultivating patterns) and integrating the same cultivating patterns will be on the agenda. At the last stage of the proposed method and after refinement of the endmembers, the hyper-cube is clustered by Spectral Angle Mapper (SAM) algorithm and the mapping of regions with the same cultivation pattern is produced. In the proposed method, the zoning of agricultural land is based solely on the statistical knowledge of the variety of cultivation and the results have led to the production of interconnected spatial parts. This is consistent with the reality of the spatial occurrence of similar cultivating patterns in a geographic area. On the other hand, the visual comparison of results with large scale satellite images illustrates that there is a significant relationship between clustering results and ground truth in terms of cultivating pattern. Obviously, such products can be used as initial layers of information to produce the results of a supervised classification with the aim of applying the cultivation area of a variety of agricultural products.
In this paper pixel-based and object-oriented classifications were investigated for land-cover mapping in an urban area. Since the image fusion methods are playing a useful role in supplying ...classification different fusion approaches such as Gram-Schmidt Transform (GS), Principal Component Transform (PC), Haar wavelet, and À Trous Wavelet Transform (ATWT) algorithms have been used and the fused image with the best quality has been assessed on its respected classification. A Hyperion image and IRS-PAN image covering a region near Tehran, Iran have been used to demonstrate the enhancement and accuracy assessment of fused image over the initial images. The evaluation results of fused images showed that the Haar wavelet approach has good quality in preserving spectral information as well as spatial information. Classification results were compared to evaluate the effectiveness of the two classification approaches. Result of the pan-sharpened image classifications displayed that the object-oriented procedure presented more accurate outcomes (90.47 %) than those obtained by pixel-based classification method (77.33 %).
Road vectorization aims to delineate road centerlines from aerial and satellite images. In this paper, binary road image space clustering techniques, which are used to determine key points on the ...road, are expanded to a more accurate and reliable algorithm, the Increasing Ellipse
Clustering technique. Accurate noise cluster recognition and omission are two strengths of the proposed algorithm. In order to establish the true connections between predetermined key points on the road, a very fast, novel, and reliable fuzzy ellipse-shaped clustering methodology is introduced.
Different accuracy assessment parameters are established and evaluated based on results obtained for simulated and real road binary images. The sub-pixel geometric accuracy of the extracted road network, with a completeness of more than 80 percent, demonstrates the promising results of the
vectorization algorithm that is presented in this paper.
A new glacier inventory of Iran Moussavi, M.S.; Zoej, M.J. Valadan; Vaziri, F. ...
Annals of glaciology,
2009, Letnik:
50, Številka:
53
Journal Article
Recenzirano
Odprti dostop
A new glacier inventory of Iran, compiled according to GLIMS guidelines through the use of photogrammetry and remote sensing supported by fieldwork, provides the first comprehensive study of its ...mountain glaciers. The glaciers are found in five main areas: two in the higher elevations of the Alborz mountain range (Damavand and Takhte–Soleiman regions), two on the Zardkuh and Oshtorankuh mountain chain in the Zagros mountain range and one in the Sabalan Mountains in northwest Iran. Several important glacier attributes, including minimum and maximum height of ice, area and maximum length and width, together with glacier extent, were successfully extracted using aerial and satellite imagery. Thereafter a comprehensive glacier database was established in a GIS environment.
The aim of road detection is to discriminate between road and background pixels. This discrimination is considered to be the most important stage in automatic road network extraction from satellite ...imagery. In this paper, neural networks are applied to high-resolution IKONOS and QuickBird images for road detection. This paper has endeavored to optimize the functionality of neural networks using a variety of texture parameters. These parameters had different window sizes and gray level numbers, not only from the source but also from the preclassified image. It was discovered that using texture parameters from a preclassified image accompanied by primary spectral information in reclassifying the source image could improve both road and background detection ability of the neural network. Accuracy assessment parameters were evaluated on several pan-sharpened IKONOS and QuickBird images. The obtained results attest to the efficiency of the proposed method.
Fractional Vegetation Cover Estimation In Urban Environments Salimi Kouchi, H.; Sahebi, M. R.; Abkar, A. A. ...
International archives of the photogrammetry, remote sensing and spatial information sciences.,
09/2013, Letnik:
XL-1/W3
Journal Article
Recenzirano
Odprti dostop
Quality of life in urban environments is closely related to vegetation cover. The Urban growth and its related environmental problems, planners are forced to implement policies to improve the quality ...of urban environment. Thus, vegetation mapping for planning and managing urban is critical. Given the spectral complexity of the urban environment and the sparse vegetation in these areas, to generate a reliable map of coverage Vegetation in these areas requires the use of high spatial resolution images. But given the size of cities and the rapid changes in vegetation status, Mapping of vegetation using these images will have cost much. In this study, using a moderate spatial resolution image with the help of a small part of high spatial resolution image vegetation cover in a Metropolitan area is obtained. We make use of Ikonos image to get Fractional vegetation cover (FVC) and used as a vicarious validation of FVC. Then using linear and nonlinear regression and neural network between the FVC derived from the Ikonos image and vegetation indices on Landsat image, the relationship was established. A number of pixels were randomly selected from the images for the model validation. The results show that the neural network, nonlinear regression and linear regression models are more accurate for the estimation of FVC respectively.
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