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The hyperspectral image has the advantages of wide spectral range and the high spectral resolution, and is widely applied in the terrain classification. In this paper, we study the airborne hyperspectral image classification methods using the airborne hyperspectral image. Considering the hyperspectral image has amounts of bands and there is redundancy among the bands, the principle component analysis...
Marginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data and separate the interclass data, and it is very useful to analyze the high-dimensional data. However, MFA just considers the structure relationship of neighbor points, and it cannot effectively represent the intrinsic structure of hyperspectral image (HSI) that possesses many homogenous areas. In this paper,...
Unsupervised classification plays a key role in remote sensing hyperspectral image analysis. Complexities arise from the high dimensionality of hyperspectral imagery and this implies the need for dimensionality reduction as a vital preprocessing step. However, conventional dimensionality reduction techniques, such as linear and nonlinear manifold learning approaches, may fail if the hyperspectral...
High-dimensional data such as hyperspectral images contain abundant information of surface radiation. But the massive redundant information makes it complex to be utilized conveniently. To solve this problem, a manifold learning dimensionality reduction framework for hyperspectral image is proposed. Firstly, statistical sampling methods were used to sample a subset of data points as landmarks. A skeleton...
Sparse unmixing algorithm aims at finding the optimal subset of signatures from a spectral library to best model each pixel in hyperspectral image and estimating their corresponding abundance. However, the high mutual coherence of spectral library limits the performance of sparse unmixing algorithm. In this paper, a method referencing the extracted information from hyperspectral image was managed...
Anomaly detection has been known to be a challenging, ill-posed problem due to the uncertainty of anomaly and the interference of noise. In this paper, we propose a novel low rank anomaly detection algorithm in hyperspectral images (HSI), where three components are involved. First, due to the highly mixed nature of pixels in HSI, instead of using the raw pixel directly for anomaly detection, the proposed...
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