Title :
A Simulated Annealing Feature Extraction approach for hyperspectral images
Author :
Chang, Yang-Lang ; Fang, Jyh-Perng ; Liu, Jin-Nan ; Ren, Hsuan ; Liang, Wen-Yew
Author_Institution :
Nat. Taipei Univ. of Technol., Taipei
Abstract :
In this paper, a novel study is proposed for the feature extraction of high volumes of remote sensing images by using a simulated annealing feature extraction (SAFE) approach. For hyperspectral imagery, complete modular eigenspace (CME) has been developed by clustering highly correlated hyperspectral bands into a smaller subset of band modular based on greedy algorithm. Instead of greedy paradigm as adopted in CME approach, this paper introduces a simulated annealing (SA) approach for hyperspectral imagery. It presents a framework which consists of three algorithms, referred to as SAFE, CME and the feature scale uniformity transformation (FSUT). SAFE selects the sets of non-correlated hyperspectral bands based on SA algorithm while utilizing the inherent separability of different classes in hyperspectral images to reduce dimensionality and further to effectively generate a unique CME feature. The proposed SA features avoids the bias problems of transforming the information into linear combinations of bands as does the traditional principal components analysis and provides a fast procedure to simultaneously select the most significant features according to a scheme of SA. The experimental results show that the SAFE approach is effective and can be used as an alternative to the existing feature extraction algorithms.
Keywords :
eigenvalues and eigenfunctions; feature extraction; geophysical signal processing; geophysical techniques; greedy algorithms; image processing; principal component analysis; remote sensing; simulated annealing; spectral analysis; class separability; complete modular eigenspace; dimensionality reduction; feature extraction; feature scale uniformity transformation; greedy algorithm; highly correlated hyperspectral band clustering; hyperspectral images; principal components analysis; remote sensing images; simulated annealing; Computational modeling; Computer science; Computer simulation; Feature extraction; Greedy algorithms; Hyperspectral imaging; Hyperspectral sensors; Remote sensing; Simulated annealing; Space technology;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-1211-2
Electronic_ISBN :
978-1-4244-1212-9
DOI :
10.1109/IGARSS.2007.4423523