Epipolar resampling aims to eliminate the vertical parallax of stereo images. Due to the dynamic nature of the exterior orientation parameters of linear pushbroom satellite imagery and the complexity of reconstructing the epipolar geometry using rigorous sensor models, so far, no epipolar resampling approach has been proposed based on these models. In this paper for the first time it is shown that the orientation of the instantaneous baseline (IB) of conjugate image points (CIPs) in the linear pushbroom satellite imagery can be modeled with high precision in terms of the rows- and the columns-number of CIPs. Taking advantage of this feature, a novel approach is then presented for epipolar resampling of cross-track linear pushbroom satellite imagery. The proposed method is based on the rigorous sensor model. As the instantaneous position of sensors remains fixed, the digital elevation model of the area of interest is not required in the resampling process. Experimental results obtained from two pairs of SPOT and one pair of RapidEye stereo imagery with different terrain conditions shows that the proposed epipolar resampling approach benefits from a superior accuracy, as the remained vertical parallaxes of all CIPs in the normalized images are close to zero.
The occurrence of dust storms is one of the major problems in Iran and neighboring countries. Monitoring dust storms and identifying their patterns, areas with high frequency of occurrence and high dense storms using remote sensing technology can help decision makers in different dimensions. This paper tries to investigate the occurrence of dust storms in Iran and adjacent regions over two annual periods, and produce the maps of cumulative density and frequency of occurrence in order to find dust patterns and hotspots and make a comparison between these periods. The daily MODIS images received at the Mahdasht Space Center in 1396 and 1397 were used. Brightness temperature images were prepared using these data and the bands 20, 29, 31 and 32 of them were used. After applying appropriate algorithms, daily dust storm maps were produced. Using the data of 212 synoptic stations throughout the country, the accuracy of the algorithms were assessed and the thresholds were modified. By combining these daily maps, annual cumulative density maps were prepared. Also, by combining daily occurrence maps of dust storms, frequency of occurrence maps were produced. The results show that in 1397, more areas have experienced dust storms in comparison to 1396. Besides, the number and density of dust storms at west and southwest areas have been reduced in 1397. Conversely, in the eastern and southeast areas we have faced with an increase in the number and density of monitored dust storms in 1397. It can be concluded that the problem of dust storms in Iran have been shifted from the west and southwest of the country to the eastern and southeastern regions.
Genetic algorithms (GAs) are frequently used for optimization of remote sensing models. Recently, they have been used in optimization of rational function models (RFMs) for georeferencing of satellite images. In this way, fewer ground control points (GCPs) are needed while accurate results are achieved in comparison to manual or try and error based approaches of terrain dependent RFM term selection. However, GAs are quite inefficient in terms of computational speed. In this article, a novel optimization approach adopting a newly introduced concept in natural sciences called 'genetic modification' is proposed to speed up the basic GA. According to the proposed method, a qualification coefficient is defined to examine the qualification of individual genes. Therefore, qualified genes are identified and are used to produce a new set of chromosomes in each iteration of the algorithm as 'transgenic chromosomes'. Considering these chromosomes as a part of parents for next generation, desired characteristics (optimal parameters) appeared with an efficient speed. To evaluate the performance of the proposed algorithm, over two different case studies, RFM is optimized using both proposed and basic GAs. The results indicate that the optimization speed is improved by 20 times, while the accuracies are preserved.
As a process to detect changes in land cover by using multi-temporal satellite images, change detection is one of the practical subjects in field of remote sensing. Any progress on this issue increase the accuracy of results as well as facilitating and accelerating the analysis of multi-temporal data and reducing the cost of producing geospatial information. In this study, an unsupervised change detection method is proposed based on using the image quality parameters; including correlation, spectral distortion, radiometric distortion and contrast between pixels in multi-temporal images. To calculate these indices, a binary mask is used to divide the image into change and unchanged classes. In this paper, to generate the mask, the proposed method applied asymmetric thresholding on signed difference image and in order to produce optimal mask, an iterative algorithm are suggested to find the optimal thresholds. The results demonstrate 5 percent increasing when two asymmetric thresholds are used with respect to use one threshold in absolute difference image. The proposed method is less sensitive to radiometric changes in multi-temporal images. Besides, due to usage optimized threshlding method, this method has less computational cost than random mask optimization methods. Moreover, in comparison with the Otsu thresholding method and Fisher criterion function, the results obtained from the proposed method demonstraste 24 and 21 percent incressing the accuracy, respectively.
End-member extraction could be considered as the most challenging stage of the spectral unmixing process. In this study, a new approach is proposed based on error analysis of Linear Spectral Mixture Model (LSMM) to extract optimal pure pixels. First a number of approximate end-members are identified visually or using N-Finder algorithm then LSMM is applied to identify pixels with proportions greater than one (over-shoots). Over-shoots are then replaced with initial end-members and the LSMM is performed again. This process is continued until reduction of the number of overshoot and under-shoot pixels below 5% of total image pixels. According to the results, the proposed end-member extraction approach satisfies this criterion within a few iterations (2 or 3 runs). The total numbers of under/over shoots are estimated 4.17% and 3.55% of total image pixels respectively for choosing the initial end-members visually and by means of N-Finder algorithm.
Abstract:Identifying the optimal structure of terrain dependent Rational Function Models (RFMs) not only decreases the number of Ground Control Points (GCPs) required, but also increases the accuracy of the model, by reducing the multi-collinearity of Rational Polynomials Coefficients (RPCs) and avoiding the ill-posed problem. Global optimization algorithms such as Genetic Algorithm (GA), evaluate the different combinations of parameters effectively. Therefore, they have a high ability to detect the optimal structure of RFMs. However, one drawback of these algorithms is their high computation cost. This article proposes a knowledge-based search strategy to overcome this deficiency. The backbone of the proposed method relies on the technical knowledge about the geometric condition of image at the time of acquisition, as well as the effect of external factors such as terrain relief on the image. This method was evaluated on four different datasets, including a SPOT-1A, a SPOT-1B, an IKONOS-Geo image, and a GeoEye-Geo imagery, using various number of GCPs. Experimental results demonstrate the efficiency of the proposed method to achieve a sub-pixel accuracy using few GCPs (only 4-6 points) in all datasets. The results also indicate that the proposed method improves the computation speed by 140 times when comparing with GA.
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