Geographic Information System
E. Norouzi; S. Behzadi
Abstract
Background and Objectives: Climate phenomena such as quantity of surface evaporation are affected by many environmental factors and parameters, which makes modeling and data mining difficult. On the other hand, the estimation of surface evaporation for a target station can be difficult as a result of ...
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Background and Objectives: Climate phenomena such as quantity of surface evaporation are affected by many environmental factors and parameters, which makes modeling and data mining difficult. On the other hand, the estimation of surface evaporation for a target station can be difficult as a result of partial or complete lack of local meteorological data under many conditions. In this regard, satellite imagery can play a special role in modeling and data mining of climatic phenomena, because of their significant advantages, including availability and their potential analysis. Therefore, addressing the improvement and expansion of machine learning methods and modeling algorithms along with remote sensing data is inevitable.Methods: In this research, we intend to study the ability of 11 machine-learning modeling algorithms to model data and surface evaporation phenomena using satellite imagery. We used two methods to prepare the database: PCA and its opposite method using standard deviation and correlation.Results: The calculation of the Root Mean Squared Error (RMSE) indicated that, in general, the use of the PCA method has a better result in preparing and reducing the dimensions of large databases for all methods of machine learning. The SEGPR model was ranked first with the least error (93.49%) in the Principal Component Analysis (PCA) method, and the Artificial Neural Network (ANN) model performed well in both data preparation methods (93.42, 93.38), and the Classification-Tree-Coarse model had the highest error in both methods (92.66, 92.67).Conclusion: Consequently, it can be said that by changing the methods of database preparation in order to train models, the modeling results can be changed effectively.
Image Processing
A. Sharifi
Abstract
Background and Objectives: High resolution multi-spectral (HRMS) images are essential for most of the practical remote sensing applications. Pan-sharpening is an effective mechanism to produce HRMS image by integrating the significant structural details of panchromatic (PAN) image and rich spectral ...
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Background and Objectives: High resolution multi-spectral (HRMS) images are essential for most of the practical remote sensing applications. Pan-sharpening is an effective mechanism to produce HRMS image by integrating the significant structural details of panchromatic (PAN) image and rich spectral features of multi-spectral (MS) images.Methods: The traditional pan-sharpening methods incur disadvantages like spectral distortion, spatial artifacts and lack of details preservation in the fused image. The pan-sharpening approach proposed in this paper is based on integrating wavelet decomposition and convolutional sparse representation (CSR). The wavelet decomposition is performed on PAN and MS images to obtain low-frequency and high-frequency bands. The low-frequency bands are fused by exploring the CSR based activity level measurement.Results: The HRMS image is restored by implementing the inverse transform on fused bands. The fusion rules are constructed, thus to transfer the crucial details from source images to the fused image effectively.Conclusion: The proposed method produces HRMS images with rational spatial and spectral qualities. The visual outcomes and quantitative measures approve the eminence of the proposed fusion framework.
