Hyperspectral Image Classification Based on Artificial Immune System

The high spectral dimensionality in hyperspectral images causes the reduction of accuracy for common statistical classification methods in these images. Hence the generation and implementation of more complicated methods have gained great importance in this field. One of these methods is the Artificial Immune Systems which is inspired by natural immune system. Despite its great potentiality, it is rarely utilized for spatial sciences and image classification. In this paper a supervised classification algorithm with the application of hyperspectral remote sensing images is proposed. In order to gain better insight into its capability, its accuracy is compared with Artificial Neural Network. The results show better image classification accuracy for the Artificial Immune method.

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GRAPH-BASED SEMI-SUPERVISED HYPERSPECTRAL IMAGE CLASSIFICATION USING SPATIAL INFORMATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w4-91-2017 ◽

2017 ◽

Vol XLII-4/W4 ◽

pp. 91-96

Author(s):

N. Jamshidpour ◽

S. Homayouni ◽

A. Safari

Keyword(s):

Image Classification ◽

Supervised Classification ◽

Spatial Information ◽

Hyperspectral Image ◽

Main Idea ◽

Hyperspectral Data ◽

Training Data ◽

Data Sets ◽

Hyperspectral Image Classification ◽

Data Set

Hyperspectral image classification has been one of the most popular research areas in the remote sensing community in the past decades. However, there are still some problems that need specific attentions. For example, the lack of enough labeled samples and the high dimensionality problem are two most important issues which degrade the performance of supervised classification dramatically. The main idea of semi-supervised learning is to overcome these issues by the contribution of unlabeled samples, which are available in an enormous amount. In this paper, we propose a graph-based semi-supervised classification method, which uses both spectral and spatial information for hyperspectral image classification. More specifically, two graphs were designed and constructed in order to exploit the relationship among pixels in spectral and spatial spaces respectively. Then, the Laplacians of both graphs were merged to form a weighted joint graph. The experiments were carried out on two different benchmark hyperspectral data sets. The proposed method performed significantly better than the well-known supervised classification methods, such as SVM. The assessments consisted of both accuracy and homogeneity analyses of the produced classification maps. The proposed spectral-spatial SSL method considerably increased the classification accuracy when the labeled training data set is too scarce.When there were only five labeled samples for each class, the performance improved 5.92% and 10.76% compared to spatial graph-based SSL, for AVIRIS Indian Pine and Pavia University data sets respectively.

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