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Seyed Ali Jozi Farkhondeh Ebadzadeh 《Journal of the Indian Society of Remote Sensing》2014,42(2):363-371
The present study was carried out to evaluate agricultural capability of a watershed located in Khuzestan; a province in southern Iran. It is aimed to examine the applicability of Multi Criteria Decision-Making (MCDM) methods in site selection process. Accordingly, the ecological resources of the watershed were initially identified. To specify the criteria required for agricultural land evaluation, Delphi method was applied. After selecting the criteria, they were weighted using Analytical Hierarchy Process (AHP) Method. Weighted Overlay (WO) Method was also used to overlay the map layers in the GIS environment. Afterwards, sensitivity analysis was performed using Weights Sensitivity Analysis (WSA) method to show the impressibility rate of the results against a certain changes in the inputs. The results revealed that out of 6591.2 ha of the total watershed area, 50.8 % has unsuitable potentiality while 27.32 % has a poor suitability for irrigated agriculture. It was also determined that only 6.96 % of the whole study area has a suitable potential for this purpose. Besides, the findings indicated that 23.38 % of the total watershed area is unsuitable for rain-fed farming. the results also showed that 31.78 % and 19.12 % of the entire study area has moderate and high potentials for rain-fed agriculture, respectively. In a general overview, this study could present how MCDM is effective in handling land capability studies. 相似文献
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Ali Jafari Mohammad Mehdi Ebadzadeh Reza Safabakhsh 《Journal of the Indian Society of Remote Sensing》2017,45(3):417-429
The normal compositional model (NCM) is a well-known and powerful model in hyperspectral unmixing which represents endmembers as independent Gaussian vectors to capture endmember variability. However, the assumption of independent endmembers diminishes the model accuracy because the high degree of correlation between endmembers of a scene and identical sources of variability demonstrate that the endmembers are dependent. This paper proposes a new hyperspectral unmixing algorithm which represents endmembers using dependent Gaussian vectors to estimate abundance fractions. To overcome the higher complexity caused by dependence assumption, this algorithm introduces new independent Gaussian vectors named Base Vectors to represent different endmembers by a weighted linear combination. Also, the proposed unmixing algorithm uses maximum likelihood method to estimate weight coefficients of Base Vectors which are used to represent mixed pixel. Finally, abundance estimation can be done using the new representation for endmembers and mixed pixel. The proposed algorithm is evaluated and compared with other state-of-the-art unmixing algorithms using simulated and real hyperspectral images. Experimental results demonstrate that the proposed unmixing algorithm can unmix pixels composed of correlated endmembers in hyperspectral images in the presence of spectral variability more accurately than previous methods. 相似文献
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Spectral unmixing estimates the abundance of each endmember at every pixel of a hyperspectral image. Each material in traditional unmixing algorithms is represented through a constant spectral signature. However, endmember variability always exists due to environmental, atmospheric, and temporal conditions, which leads to poor accuracy of the estimated abundances. This paper proposes a new unmixing algorithm based on a new linear transformation called endmember orthonormal mapping (EOM) to overcome the aforementioned problem. The EOM transformation maps original spectral space to a new EOM space to reduce endmember variability. In the original spectral space, each material is represented by a set of spectra (endmember set) which is extracted using the automated endmember bundles (AEB) method. The EOM transforms each endmember set to a vector in the EOM space so that these vectors are orthonormal. On account of orthonormalized endmembers, the condition number of the mixing matrix in the EOM space reduces. Furthermore, we consider the noise term as an additional virtual endmember set mapped to a vector that is orthogonal to other endmembers. As a result, a promising unmixing accuracy is obtained through applying the least squares abundance estimation in the subspace orthogonal to noise. Experimental results of both synthetic and real hyperspectral images demonstrate that the proposed algorithms provide much enhanced performance compared with the state-of-the-art algorithms. 相似文献
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