The development of remote geological interpretation technology is booming during recent years.
However, there is a significant obstacle for extracting geology information from remote sensing
imagery--the presence of clouds and their shadows. Diverse techniques have been proposed including
different algorithms such as filtering algorithm and multi-temporal cloud removing algorithm to solve
the problem. This paper presents a modified solution to denoise the haze, based on ETM+ imagery.
First of all, wavelet transform is applied to Band1, Band2 and Band3 imagery to determine the clear
region and different levels of cloud regions. Then all pixels of the ETM+ imagery are classified to
specific cover types after the cluster analysis of band4, Band5 and Band7. At last, the mean reflectance
matching is performed in the first three bands separately according to different cover types in both clear
region and cloud region. Above all, the method is implemented by IDL. The results show that this
modified method not only can quantitatively determine the cloud area but also can remove cloud from
imagery efficiently. Moreover, compared with the homomorphic filtering method, the experiment
results of the proposed method is much more satisfying in Geology Interpretation.
In this paper a Hyperspectral Expert Classifier (HEC) based on data-fusion technique was presented. The spectral-spatial contextual image analysis approaches were applied on hyperspectral images, ETM+ images, and GIS data. First, the samples were selected according to the available information to build the reference spectral and calculate the maximum angle after data fusion. The created maps using Spectral Angle Mapping (SAM), GIS data, hyperspectral image, and ETM+ images were used as an input data in HEC. The result showed that the Land-use in the study area could be identified from Hyperion data efficiently. The hyperspectral expert classifier approach is found to have a merit of high classification precision, low computational cost, and without much interference from the users compared with other classifiers. This methodology could easily extended to a large number of classes and used in practical applications (for example mine exploration).
The regularization parameter and the kernel parameters greatly affect the performance of support vector machines (SVM)
models. This paper proposes an evolutionary algorithm (EA) to automatically determine the optimal parameters of SVM
with the better classification accuracy and generalization ability simultaneously. The proposed ESVM model, called
evolutionary SVM or ESVM, was applied to a Land-cover classification experiment in a 840×840 pixels Landsat-7
Enhanced Thematic Mapper plus (ETM+) high-resolution image of Wuhan in Hubei province of China compared with
the conventional SVM model. Experimental results show that the use of EA for finding the optimal parameters results
mainly in improvements in overall accuracy and generalization ability in comparison with conventional SVM. It is
observed that classification accuracy of up to 91% is achievable for Landsat data produced by ESVM.
This paper presents a Pareto evolutionary artificial neural network (Pareto-EANN) approach based on the evolutionary algorithms for multiobjective optimization augmented with local search for the classification of remote sensing image. Its novelty lies in the use of a multiobjective genetic algorithm where single hidden layers Multilayer Perceptrons (MLP) are employed to indicate the accuracy/complexity trade-off. Some advantages of this approach include the ability to accommodate multiple criteria such as accuracy of the classifier and number of hidden units. We compared Pareto-EANN classifiers results of the classification of remote sensing image against standard backpropagation neural network classifiers and EANN classifiers; we show experimentally the efficiency of the proposed methodology.
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