M Panahi; M.J Valadan Zoej; S Yavari
Volume 10, Issue 1 , June 2018, , Pages 17-40
Abstract
Non-physical models have attracted the attention of experts in the field of photogrammetry and remote sensing due to the lack for need of ephemeris data at the time of imaging and not providing raw images by owners of these images. In this paper, a comprehensive research was performed on non-physical ...
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Non-physical models have attracted the attention of experts in the field of photogrammetry and remote sensing due to the lack for need of ephemeris data at the time of imaging and not providing raw images by owners of these images. In this paper, a comprehensive research was performed on non-physical models including: 3D Affine Model, First Order Rational Function Model with unequal denominator, SDLT, DLT, Rational Function Model with equal denominator, with the emphasis on the effect of linear and point features as control information to geometrically correct the high spatial resolution images. In addition, a new form of Pushbroom-Projective function is introduced, as a new idea for geometric correction of satellite images. The satellite images used in this research are GeoEye-1 from Urmia and Ikonos from Hamedan. Based on the results obtained, in the case of GeoEye-1 satellite image, First Order Rational Function Model with unequal denominator when using point features as control and +XY term of Rational Function Model with equal denominator when applying linear features as control reached the highest accuracy of 0.75 pixel and 2.03 pixel respectively. In the case of Ikonos satellite image, the +XY term of Rational Function Model with equal denominator when using control point features and First Order Rational Function Model with unequal denominator when using linear control features reached the accuracy of 0.68 pixel and 1.5 pixel respectively at the best. It is worth mentioning that the remaining systematic errors in the case of using linear features as control are always more than those obtained using point control features.
F Mahmoudi; M Mokhtarzadeh; M.J Valadan Zouj
Volume 9, Issue 3 , February 2018, , Pages 1-14
Abstract
This research studies the suitable process of change detection at at an Agricultural areas by focusing on object based method and color fusion. In order to obtain this goal, it is benefit from Landsat7 images. The main idea of offering object based method is a modern algorithm i.e. Double-layer image ...
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This research studies the suitable process of change detection at at an Agricultural areas by focusing on object based method and color fusion. In order to obtain this goal, it is benefit from Landsat7 images. The main idea of offering object based method is a modern algorithm i.e. Double-layer image are combined and An image of the entire layer is formed. Then by selecting suitable parameters a single image is separated in to several parts and by color fusion and object based classification method the changed and unchanged parts are classified. In fact, color fusion is determined by creating different color areas with elementary images that determines changed parts on visual basics and then by using object based classification method and selecting some parts by the user, the total parts of image is determined. Finally, by selecting training samples only one part of image is labeled and its classification is determined and the ultimate map of changes is obtained. Results show that this method is suitable for reducing training samples, increasing exactness (3%-2.5%), speed and increasing information for classification of spatial information and structure and in addition to spectral information it is better than ordinary methods of change detection from comparing 2 multi-temporal images.
, Y Rezaei; , M.J. Valadan Zouj; , M.R Sahebi
Volume 9, Issue 1 , October 2017, , Pages 1-16
Abstract
Mountain Glaciers are pertinent indicators of climate change and their surface velocity changes, are an essential climate variable. In order to retrieve the climatic signature from surface velocity, large scale study of glacier changes is required. Satellite remote sensing is an effective way to derive ...
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Mountain Glaciers are pertinent indicators of climate change and their surface velocity changes, are an essential climate variable. In order to retrieve the climatic signature from surface velocity, large scale study of glacier changes is required. Satellite remote sensing is an effective way to derive mountain glacier surface velocities. In this research, we have conducted a comprehensive assessment of Alam-Chal glacier surface changes (include displacement and velocity), all based on remotely-sensed data. All datasets include aerial photos and satellite images were ortho rectified, normalized and co-registered. By using an aerial photograph collected in 1955 as a baseline and comparing it against a 2003 image collected by the SPOT satellite, the glacier retreat, in direct response to changes in local climate conditions were extracted. Furthermore, we have assessed short-term changes over two-time scales (1988-2003, 2003-2005),using an aerial photo acquired in 1988, a 2003 SPOT image, and a high-resolution Quick Bird image collected over the study area in 2005. We have derived accurate glacier surface velocity vectors (RMSE~2m), based on an FFT-based image cross-correlation technique. Our results point to the capability of the proposed method in accurately retrieving glacier surface changes at a high level of spatial detail, which is important for studies of regional climate change.
Fateme Ameri1; Mohammad Javad Valadan Zoej; Mehdi Mokhtarzade
Volume 7, Issue 3 , November 2015, , Pages 33-48
Abstract
Nowadays, extraction of roads from digital aerial and satellite images is a common method of road database construction. Regarding to massive amount of road data and time and cost effective updating requirements, automation procedure is becoming an essential. In this research, which is mostly concentrated ...
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Nowadays, extraction of roads from digital aerial and satellite images is a common method of road database construction. Regarding to massive amount of road data and time and cost effective updating requirements, automation procedure is becoming an essential. In this research, which is mostly concentrated on road vectorization process, an automatic approach of road centerline vectorization from detected road image with negligible operator interventions is designed. The proposed system consists of two main stages including road key points determination and connection. At the first stage, the road key points representative of the road centerline are determined using particle swarm optimization clustering. At the second stage, in order to model the road networks weighted graph theory is considered. In this model cost of each connection is calculated by aggregating appropriate road geometric criteria by means of ordered weighted averaging operators. The least cost connections constitute the vectorized road networks. The proposed approach was implemented on several high resolution satellite images and their results were compared with the results of the minimum spanning tree algorithm. On the whole, the obtaining results proved the efficiency of the vectorization approach in attaining the complete and accurate road network. Extracting different road shapes including direct and curved roads, roads with different widths, parallel roads with different distances, junctions and square with average RMSE value about 0.9 meter, average completeness of %94, and average correctness greater than %95 proves the efficiency of the algorithm in yielding complete road networks.
Volume 6, Issue 4 , October 2014
Abstract
Land cover information is one of the most important prerequisite in urban management system. In this way remote sensing, as the most economic technology, is mainly used to produce land cover maps. Considering the complicated and dense urban areas in third world countries, object based approaches are ...
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Land cover information is one of the most important prerequisite in urban management system. In this way remote sensing, as the most economic technology, is mainly used to produce land cover maps. Considering the complicated and dense urban areas in third world countries, object based approaches are suggested as an effective image processing technique. The purpose of this paper are the introduction of a new object based approach for classification of complicated urban area using high resolution satellite image and approaching to a standard and effective process of map generation by satellite images. This paper used a new approach to select the segmentation parameters and a new hierarchical classification model based on a rule based strategy is used to overcome the confusions between urban classes too. In this article an innovative hierarchical model is proposed for object-based classification of complicated urban areas. In this way, beside of feature space optimization in a multi scale analysis, rule based and fuzzy nearest neighbor approaches are used as the object-based classification strategies. The proposed method is implemented on an urban IKONOS image where 84% and 87%overall accuracies are obtained for rule based and fuzzy nearest neighbor classification approaches respectively. The implementation of the devised algorithm on another IKONOS image moved its general ability to other urban areas. Keywords: Land cover classification, Rule based, Object based, Fuzzy nearest neighbor, Complicated urban areas.
Volume 4, Issue 3 , September 2012
Abstract
This paper proposes a new approach for geometrical modeling of satellite imagery which uses 2D-polynomials for 3D point determination from satellite stereo images. In this model, 2D polynomials are considered as additional parameters in colinearity equation, instead of considering as models for relating ...
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This paper proposes a new approach for geometrical modeling of satellite imagery which uses 2D-polynomials for 3D point determination from satellite stereo images. In this model, 2D polynomials are considered as additional parameters in colinearity equation, instead of considering as models for relating between the ground and space images. Orbital parameters model are used as fundamental colinearity equations in this modeling. Essential parameters in the orbital parameters model are determined from satellite ephemeris data and they are considered as fixed parameters in the modeling. In this model, polynomial coefficients are the only unknown parameters which are determined from GCPs in a linear equations set. The major advantages of this model are: Decreasing the performance complexities in using orbital parameters model, ease of implementation, applicability on raw geometrically corrected images, and possibility of using maximum capacity of satellite auxiliary data, and linearity of equations in space intersection procedure. Implementation of this model on different datasets shows high potentiality of the mentioned approach for 3D point determinations from satellite stereo images.
