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Rahimzadegan M.,K. N. Toosi University of Technology | Sadeghi B.,Islamic Azad University at Tehran | Masoumi M.,National Geographical Organization | Taghizadeh Ghalehjoghi S.,National Geographical Organization
Arabian Journal of Geosciences | Year: 2015

In recent years, satellite sensor images have been commonly used in mineral detection. These images, with different spectral and spatial resolutions, can be used in detecting minerals which have surface indicators. Due to numerous sources of uncertainties in using spectral processes for mineral explorations and atmospheric effects on the spectrum of the pixels, in this research, mineral target detection methods were selected and implemented. The implementation of this research was done using an ASTER scene from the northern area of Semnan province in Iran. There were seven iron mines in the region covered by this scene. Three of them were used as training data and four other ones were used as test data. Several target detection methods were implemented and the mean of the results of these algorithms, which embrace the results of all algorithms, was evaluated. The output of the algorithm is an image where the gray value of each pixel corresponds to the probability of similarity to the training data. Considering the fact that the probabilities’ range is between 0 and 1, after implementing the algorithms, it was concluded that the spectral angle mapper (SAM) method has the best performance with mean probability value of 0.99 for the test mines. Based upon the fact that the mean value of the algorithms was 0.87, it was proved that these methods can be very practical thanks to their high accuracy in prospection of new exploration targets which may detect new equivalent iron potentials. © 2014, Saudi Society for Geosciences.

Haghi M.,Khorramshahr Marine Science and Technology University | Savari A.,Khorramshahr Marine Science and Technology University | Kochanian P.,Khorramshahr Marine Science and Technology University | Nabavi M.B.,Khorramshahr Marine Science and Technology University | And 3 more authors.
Marine Ecology | Year: 2012

Mapping surveys of coastal benthic habitat in Qeshm Island Geopark, Persian Gulf, were conducted using a combination of biological, sedimentological and echo-sounding data. The survey area covered approximately 233km 2 in a depth range of 5-25m, and the data were acquired from a single beam echo sounder, grab, video and still photography. Sediment and macrofauna samples were collected by grab at 76 stations and subjected to classification and ordination analyses. Two acoustic classes were identified differentiating along the near/offshore axis. Sediment texture was dominated by fine grain sizes, with five distinct sub-sediment types. In total, 214 macrobenthic taxa were identified, of which polychaetes accounted for 60%. Other dominant groups included young sponges, nematodes, malacostracan crustacean, bivalves, ostracods and ophiuroids. Underwater videos and still photos integrated the macrofaunal and sedimentary data and revealed a range of biogenic sedimentary features such as burrows and tubes. The biological data identified six main biological assemblages showing an inshore/offshore pattern. The macrobenthic abundance did not demonstrate a significant difference with depth, although polychaetes were positively correlated with depth. The highest abundance and species richness were observed at median depths. Species distribution and diversity did not show any correlation with sediment type. A preliminary habitat mapping of the south coast of the Qeshm Island Geopark has been carried out, integrating acoustic, sediment and biological data. © 2012 Blackwell Verlag GmbH.

Modiri M.,National Geographical Organization | Salehabadi A.,National Geographical Organization | Mohebbi M.,National Geographical Organization | Hashemi A.M.,National Geographical Organization | Masumi M.,National Geographical Organization
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2015

The use of UAV in the application of photogrammetry to obtain cover images and achieve the main objectives of the photogrammetric mapping has been a boom in the region. The images taken from REGGIOLO region in the province of, Italy Reggio -Emilia by UAV with non-metric camera Canon Ixus and with an average height of 139.42 meters were used to classify urban feature. Using the software provided SURE and cover images of the study area, to produce dense point cloud, DSM and Artvqvtv spatial resolution of 10 cm was prepared. DTM area using Adaptive TIN filtering algorithm was developed. NDSM area was prepared with using the difference between DSM and DTM and a separate features in the image stack. In order to extract features, using simultaneous occurrence matrix features mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation for each of the RGB band image was used Orthophoto area. Classes used to classify urban problems, including buildings, trees and tall vegetation, grass and vegetation short, paved road and is impervious surfaces. Class consists of impervious surfaces such as pavement conditions, the cement, the car, the roof is stored. In order to pixel-based classification and selection of optimal features of classification was GASVM pixel basis. In order to achieve the classification results with higher accuracy and spectral composition informations, texture, and shape conceptual image featureOrthophoto area was fencing. The segmentation of multi-scale segmentation method was belonged class. Search results using the proposed classification of urban feature, suggests the suitability of this method of classification complications UAV is a city using images. The overall accuracy and kappa coefficient method proposed in this study, respectively, 47/93% and 84/91% was.

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