Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 793-798, 2014
© Author(s) 2014. This work is distributed
under the Creative Commons Attribution 3.0 License.
28 Nov 2014
Texture Based Hyperspectral Image Classification
B. Kumar and O. Dikshit Department of Civil Engineering, Indian Institute of Technology Kanpur, India
Keywords: Hyperspectral, Spectral, Texture, Geometric Moments, Classification, Support Vector Machine Abstract. This research work presents a supervised classification framework for hyperspectral data that takes into account both spectral and spatial information. Texture analysis is performed to model spatial characteristics that provides additional information, which is used along with rich spectral measurements for better classification of hyperspectral imagery. The moment invariants of an image can derive shape characteristics, elongation, and orientation along its axis. In this investigation second order geometric moments within small window around each pixel are computed which are further used to compute texture features. The textural and spectral features of the image are combined to form a joint feature vector that is used for classification. The experiments are performed on different types of hyperspectral images using multi-class one-vs-one support vector machine (SVM) classifier to evaluate the robustness of the proposed methodology. The results demonstrate that integration of texture features produced statistically significantly better results than spectral classification.
Conference paper (PDF, 671 KB)

Citation: Kumar, B. and Dikshit, O.: Texture Based Hyperspectral Image Classification, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 793-798, doi:10.5194/isprsarchives-XL-8-793-2014, 2014.

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