In recent years, the satellite technique has a tremendous progress. The images captured by satellites contain larger data and dimensions. The higher number of spectral bands increases the complexity of a classificatio...
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ISBN:
(纸本)9781538691540
In recent years, the satellite technique has a tremendous progress. The images captured by satellites contain larger data and dimensions. The higher number of spectral bands increases the complexity of a classification task. Therefore, it is necessary to reduce highly correlated and redundant neighboring bands which cause the huge phenomenon. In this paper, we proposed a hybrid hierarchical approaches that combine the greedy modular eigenspace (GME) and impurity function band prioritization with hotspot analysis. Unfortunately, GME doesn't guarantee to reach a global optimal solution by the greedy algorithm except by the exhaustive search method. In order to mitigate this limitation, we used a particle swarm optimization (PSO) algorithm to cluster the highly correlated bands and hotspot analysis to give weighting to the clustered blocks. The experimental results on two publicly available benchmark dataset demonstrate that the presented approach can select those bands with discriminative information. The effectiveness of the proposed approach is tested on both images with different parameters of PSO. To verify the effectiveness of a hybrid hierarchical approach put forward in this paper, KNN classifier is performed on the selected bands. In MASTER dataset, the proposed method has 90.91% achievement in dimensionality redaction rate with classification accuracy of 95.3%. In Northwest Tippecanoe County (NTC) dataset, the dimensionality reduction rate is 87.7% and the method achieve a classification accuracy of 96.48%. The results clearly show that the proposed method has the better effects both in dimensionality reduction rate and classification accuracy.
Modern satellite imaging technology has resulted in an increased number of hyperspectral bands acquired by state-of-the-art sensors. It significantly advances the field of remote sensing. Owing to the increasing numbe...
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ISBN:
(纸本)9781509049516
Modern satellite imaging technology has resulted in an increased number of hyperspectral bands acquired by state-of-the-art sensors. It significantly advances the field of remote sensing. Owing to the increasing number of bands, the huge data quantity causes the curse of dimensionality and leads to the worse accuracy. It also increases the computational complexity exponentially as the problem size increases. It's therefore important to reduce dimensionality in order to prevent the curse of dimensionality. In this paper, a novel dimensionality reduction, named impurity function band prioritization method based on the particle swarm optimization and the gravitational search algorithms, is proposed to reduce the number of hyperspectral bands. The experimental results show that our approach can efficiently reduce dimensionality of hyperspectral data sets and significantly achieve a better classification accuracy compared to other methods.
In recent years, satellite imaging technologies have resulted in an increased number of bands acquired by hyperspectral sensors, greatly advancing the field of remote sensing. Accordingly, owing to the increasing numb...
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In recent years, satellite imaging technologies have resulted in an increased number of bands acquired by hyperspectral sensors, greatly advancing the field of remote sensing. Accordingly, owing to the increasing number of bands, band selection in hyperspectral imagery for dimension reduction is important. This paper presents a framework for band selection in hyperspectral imagery that uses two techniques, referred to as particle swarm optimization (PSO) band selection and the impurity function band prioritization (IFBP) method. With the PSO band selection algorithm, highly correlated bands of hyperspectral imagery can first be grouped into modules to coarsely reduce high-dimensional datasets. Then, these highly correlated band modules are analyzed with the IFBP method to finely select the most important feature bands from the hyperspectral imagery dataset. However, PSO band selection is a time-consuming procedure when the number of hyperspectral bands is very large. Hence, this paper proposes a parallel computing version of PSO, namely parallel PSO (PPSO), using a modern graphics processing unit (GPU) architecture with NVIDIA's compute unified device architecture technology to improve the computational speed of PSO processes. The natural parallelism of the proposed PPSO lies in the fact that each particle can be regarded as an independent agent. Parallel computation benefits the algorithm by providing each agent with a parallel processor. The intrinsic parallel characteristics embedded in PPSO are, therefore, suitable for parallel computation. The effectiveness of the proposed PPSO is evaluated through the use of airborne visible/infrared imaging spectrometer hyperspectral images. The performance of PPSO is validated using the supervised K-nearest neighbor classifier. The experimental results demonstrate that the proposed PPSO/IFBP band selection method can not only improve computational speed, but also offer a satisfactory classification performance. (C) 2014 Soci
High-dimensional datasets suffer from the curse of dimensionality. It influences the accuracy of imaging classification. Thus, the hyperspectral image processing usually selects specific bands to reduce data dimension...
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ISBN:
(纸本)9781538614457
High-dimensional datasets suffer from the curse of dimensionality. It influences the accuracy of imaging classification. Thus, the hyperspectral image processing usually selects specific bands to reduce data dimensionality. In our previous work, a particle swarm optimization (PSO) combined with partial correlation coefficient and multiple correlation coefficient is proposed to overcome the traditional method. It proposed two-dimension impurity function band prioritization (2D-IFBP) method to improve the performance of reduction dimension for hyperspectral images. However, due to the limited effects, this method cannot process in the sense of complete two-dimensional orders. To overcome this drawback, a multiple correlation coefficient matrix using PSO to cluster eigenspace and select representative bands is proposed in this paper. The proposed 2D cluster of multiple correlation coefficient with mutual information method is a complete 2D process to obtain the correlation between hands. The effectiveness of the proposed method is evaluated by AVIRIS dataset. The experimental results show that the proposed method can stably select representative bands and obtain the high accuracy.
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