| Spectral-spatial Classification of Hyperspectral Imagery Using a Hybrid Framework |
| Paper ID : 1053-SMPR |
| Authors: |
|
davood - akbari *1, Mina - Moradizadeh2 1Department of Surveying and Geomatics Engineering, College of Engineering, University of Zabol, Zabol, Iran 2Department of Geomatics, Faculty of Civil and Transportation Engineering, University of Isfahan, Isfahan, Iran |
| Abstract: |
| Hyperspectral Images are worthwhile data for many processing algorithms (e.g. Dimensionality Reduction, Target Detection, Change Detection, Classification and Unmixing). Classification is a key issue in processing hyperspectral images. Spectral-identification-based algorithms are sensitive to spectral variability and noise in acquisition. There are many algorithms for classification. This paper describes a new framework for classification of hyperspectral images, based on both spectral and spatial information. The spatial information is obtained by an enhanced Marker-based Hierarchical Segmentation (MHS) algorithm. The hyperspectral data is first fed into the Multi-Layer Perceptron (MLP) neural network classification algorithm. Then, the MHS algorithm is applied in order to increase the accuracy of less-accurately classified land-cover types. In the proposed approach, the markers are extracted from the classification maps obtained by MLP and Support Vector Machines (SVM) classifiers. Experimental results on Quebec City hyperspectral dataset, demonstrate the superiority of proposed approach compared to the MLP and the original MHS algorithms. |
| Keywords: |
| Remote sensing, Hyperspectral image, neural network, Marker selection |
| Status : Conditional Accept (Oral Presentation) |
