Band Selection of Hyperspectral-Image Based Weighted Indipendent Component Analysis
نویسندگان
چکیده
Huge amounts of data in hyperspectral images have been caused to represent approaches for the band selection of these images. In this paper, a new approach based on independent component analysis (ICA) is proposed. The idea of projection pursuit is used to order the bands on the basis of a non-gaussianity distribution. Applying a negentropy function to weight bands is a novel idea that leads to the selection of bands with minimum mutual information (MI) and besides maximum entropy, with respected to the bands selected using other methods. # 2010 The Japan Society of Applied Physics
منابع مشابه
Hyperspectral Image Classification Based on the Fusion of the Features Generated by Sparse Representation Methods, Linear and Non-linear Transformations
The ability of recording the high resolution spectral signature of earth surface would be the most important feature of hyperspectral sensors. On the other hand, classification of hyperspectral imagery is known as one of the methods to extracting information from these remote sensing data sources. Despite the high potential of hyperspectral images in the information content point of view, there...
متن کاملBand Selection Using Independent Component Analysis for Hyperspectral Image Processing
Although hyperspectral images provide abundant information about objects, their high dimensionality also substantially increases computational burden. Dimensionality reduction offers one approach to Hyperspectral Image (HSI) analysis. Currently, there are two methods to reduce the dimension, band selection and feature extraction. In this paper, we present a band selection method based on Indepe...
متن کاملOverlap-based feature weighting: The feature extraction of Hyperspectral remote sensing imagery
Hyperspectral sensors provide a large number of spectral bands. This massive and complex data structure of hyperspectral images presents a challenge to traditional data processing techniques. Therefore, reducing the dimensionality of hyperspectral images without losing important information is a very important issue for the remote sensing community. We propose to use overlap-based feature weigh...
متن کاملSeparation Between Anomalous Targets and Background Based on the Decomposition of Reduced Dimension Hyperspectral Image
The application of anomaly detection has been given a special place among the different processings of hyperspectral images. Nowadays, many of the methods only use background information to detect between anomaly pixels and background. Due to noise and the presence of anomaly pixels in the background, the assumption of the specific statistical distribution of the background, as well as the co...
متن کامل3D Gabor Based Hyperspectral Anomaly Detection
Hyperspectral anomaly detection is one of the main challenging topics in both military and civilian fields. The spectral information contained in a hyperspectral cube provides a high ability for anomaly detection. In addition, the costly spatial information of adjacent pixels such as texture can also improve the discrimination between anomalous targets and background. Most studies miss the wort...
متن کامل