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Automatic Normalization of Multitemporal Satellite Images using Artificial Neural Network and Unchanged Pixels
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Vahid Sadeghi *   , Hamid Ebadi , Farshid Farnood Ahmadi |
| K.N.Toosi University of Technology |
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Abstract: (6394 Views) |
| Relative Radiometric Normalization is often required in remote sensing image analyses particularly in the land cover change detection process. The normalization process minimizes the radiometric differences between two images caused by inequalities in the acquisition conditions rather than changes in surface reflectance. In this paper a new automatic Relative Radiometric Normalization (RRN) method is proposed which uses an Artificial Neural Network (ANN) and unchanged pixels. The proposed method includes the following stages: 1) automatic detection of unchanged pixels based on a new idea that uses CVA method, PCA transformation and K-means clustering technique, 2) evaluation of different architectures of perceptron neural networks in order to find the best architecture for this specific task and 3) use of the aforementioned network for normalizing the subject image. The method has been implemented on two paires of reference and subject images taken by the TM sensor. Normalization results obtained from the proposed method compared with the 8 conventional methods includes: Histogram matching, Haze Correction, Minimum-Maximum, Mean-Standard deviation, Simple Regression, Linear, Quadratic and Cubic Simple Regression Using Unchanged pixels and Multi Line Regression Using Unchanged Pixels. Experimental results confirm the effectiveness of the presented technique in the automatic detection of unchanged pixels and minimizing any imaging condition effects (i.e., atmosphere and other effective parameters). The proposed method for automatic change detection shows a high capability in detection of changes in covered vegetation areas. Using of this proposed method improves normalization results in all bands, especially in the third and fourth bands which are located in the red and infrared portion of the electromagnetic spectrum. The evaluation results of modeling stage reveal that the normalization using ANN in all 6 bands of all images has produced the more optimum results compared to those of normalization with conventional methods. |
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| Keywords: Rheology, Finite-element methods, Brittle, Geotherm, Iran. |
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Full-Text [PDF 1689 kb]
(1595 Downloads)
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Type of Study: Research |
Received: 2015/07/11 | Accepted: 2015/07/11 | Published: 2015/07/11
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