You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
16 September 2003Further results in the use of independent component analysis for target detection in hyperspectral images
The paper presents a novel algorithm based on Independent Component Analysis (ICA) for the detection of small targets present in hyperspectral images. Compared to previous approaches, the algorithm provides two significant improvements. First, an important speedup is obtained by preprocessing the data through spectral screening. Spectral screening is a technique that measures the similarity between pixel vectors by calculating the angle between them. For a certain threshold α, a set of pixel vectors is selected such that the angle between any two of them is larger than α and the angle between any of the pixel vectors not selected and at least one selected vector is smaller than α. In addition to significantly reducing the size of the data, spectral screening reduces the influence of dominating features. The second improvement is the modification of the Infomax algorithm such that the number of components that are produced is lower than the number of initial observations. This change eliminates the need for feature reduction through PCA, and leads to increased accuracy of the results. Results obtained by applying the new algorithm on data from the hyperspectral digital imagery collection experiment (HYDICE) show that, compared with previous ICA based target detection algorithms developed by the authors, the novel approach has an increased efficiency, at the same time achieving a considerable speedup. The experiments confirm the efficiency of ICA as an attractive tool for hyperspectral data processing.
The alert did not successfully save. Please try again later.
Stefan A. Robila, Pramod Kumar Varshney, "Further results in the use of independent component analysis for target detection in hyperspectral images," Proc. SPIE 5094, Automatic Target Recognition XIII, (16 September 2003); https://doi.org/10.1117/12.487452