Title :
ISOMAP-based subspace analysis for the classification of hyperspectral data
Author :
Ling Ding ; Ping Tang ; Hongyi Li
Author_Institution :
Inst. of Remote Sensing & Digital Earth, Beijing, China
Abstract :
A new object-oriented mapping approach is proposed based on nonlinear subspace feature analysis of hyperspectral data. A nonlinear manifold learning approach ISOMAP were utilized to obtain subspace feature representation of hyperspectral remote sensing imagery. Afterwards, the extracted subspace feature images were fed into the object-oriented system. Multiresolution segmentation algorithm was utilized to extract objects from subspace feature images and support vector machines (SVM) classifier was then used to classify the object-based feature images, texture features derived from gray level co-occurrence matrix (GLCM) and wavelet filter at the pixel level of the feature images with the use of SVM classifier were used as benchmarks to evaluate the proposed algorithm. Classification results show that the proposed object-oriented nonlinear subspace analysis approach can give significantly higher accuracies than the traditional pixel-based and texture-based subspace classification.
Keywords :
Gabor filters; feature extraction; geophysical image processing; hyperspectral imaging; image classification; image colour analysis; image resolution; image segmentation; learning (artificial intelligence); remote sensing; support vector machines; GLCM; Gabor wavelet filter; ISOMAP-based subspace analysis; SVM classifier; gray level cooccurrence matrix; hyperspectral data classification; hyperspectral remote sensing imagery; isometric feature mapping; multiresolution segmentation algorithm; nonlinear manifold learning approach; object-oriented mapping approach; object-oriented nonlinear subspace feature representation analysis; pixel-based subspace classification; subspace feature image extraction; support vector machine classifier; texture-based subspace classification; Accuracy; Feature extraction; Hyperspectral imaging; Manifolds; Support vector machines; Tiles; Hyperspectral data; ISOMAP; Subspace feature analysis; Texture; object-oriented classification;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International
Conference_Location :
Melbourne, VIC
Print_ISBN :
978-1-4799-1114-1
DOI :
10.1109/IGARSS.2013.6721184