Davood Akbari; Ali Ashrafi; Mostafa Yaghoobzadeh
Abstract
The Hyperspectral remote sensing technology has many applications in classifying land covers and studying their changes. With recent developments and the creation of images with high spatial resolution, the simultaneous use of spectral and spatial information in the classification of hyperspectral images ...
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The Hyperspectral remote sensing technology has many applications in classifying land covers and studying their changes. With recent developments and the creation of images with high spatial resolution, the simultaneous use of spectral and spatial information in the classification of hyperspectral images is necessary. In this research, a new method for the classification of hyperspectral images is introduced with the help of dimensionality reduction techniques and spatial feature extraction and neural network algorithm. In the proposed method, first, the dimensions of the hyperspectral image are reduced with the help of the principal components analysis algorithm. Then ten spatial features, mean, standard deviation, contrast, homogeneity, correlation, dissimilarity, energy, entropy, wavelet transform and Gabor filter, are extracted and then the weighted genetic algorithm is applied on the spectral and spatial features obtained. In the weighted genetic algorithm, according to the information available in the features, it gives them a weight between zero and one. Finally, a multilayer perceptron neural network classification algorithm was applied to the existing features. The proposed method was implemented on two hyperspectral images of Pavia and Berlin. The results of the obtained experiments show the superiority of the proposed method compared to the support vector machines, multilayer perceptron neural network and minimum spanning forest classification methods. This increase is about 13, 6, and 5% for the Pavia image and about 8, 6, and 5% for the Berlin image in the overall accuracy parameter and in comparison with the mentioned methods, respectively.
Davood Akbari
Abstract
The complexity and large volume of data from hyperspectral sensors have led to the consideration of more specialized and advanced methods of data analysis in order to extract information. One of the analyzes performed on hyperspectral images is target detection. With recent developments and the creation ...
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The complexity and large volume of data from hyperspectral sensors have led to the consideration of more specialized and advanced methods of data analysis in order to extract information. One of the analyzes performed on hyperspectral images is target detection. With recent developments and the creation of images with high spatial resolution, it is necessary to use both spectral and spatial information to detect hyperspectral images. This research introduces a new method for building detection in hyperspectral images based on the marker-based hierarchical segmentation algorithm. In the proposed method, first, multilayer perceptron neural network (MLP) and support vector machine (SVM) classification algorithms were implemented and their results were combined. Then, the resulting map was used to select the markers and combine them with the marker-based hierarchical segmentation algorithm using the majority voting decision law. The above techniques were applied to a series of CASI image data taken from the urban area of Toulouse in southern France. The results of quantitative and qualitative evaluations show that the proposed method has improved the kappa coefficient by 33, 28, 19, and 17% compared to the spectral correlation similarity (SCS), spectral information divergence (SID), SVM, and MLP algorithms, respectively.
