A NOVEL BAND SELECTION METHOD FOR HYPERSPECTRAL DATA ANALYSIS 
B.Mojaradi 3 * , H.Emami b ., M.Varshosaz c , S.Jamali d 
a Department of Geodesy & Geomatics Shahid Rajaee Teacher Training University ,P.O.Box 16785/163, Tehran , Iran 
bmoj aradi@gmai 1. com 
b University of Tabriz, Eng. College of Marand, Tabriz Iran, H_emami@tabrizu.ac.ir 
c Department of Geodesy & Geomatics K.N.Toosi University of Technology ,Mirdamad Cross, Tehran, Iran 
Mvarshosaz@yahoo.com 
d Department of Geodesy & Geomatics, Faculty of Engineering, Zanjan University, Zanjan, Iran, jamali@znu.ac.ir 
Commission VII, WG VII/3 
KEY WORDS: Hyperspectral data, Band Selection, Search Strategy, Data Representation, 
ABSTRACT: 
This paper proposes an innovative band selection (BS) method called prototype space band selection (PSBS) based only on class 
spectra. The main novelty of the proposed BS lies in band representation in a new space called prototype space, where bands are 
characterized in terms of class reflectivity to pose reflection properties of classes to bands. Having clustered the bands by K-means 
in the prototype space, highly correlated bands are trapped in a cluster. In each cluster a band that is close to cluster center identified 
as representative of clustered bands. In contrast to the previous BS methods, PSBS substitutes the search strategies with K-means 
clustering to find relevant bands. Moreover, instead of optimizing separability criteria, the accuracy of classification over a subset of 
training data is used to decide which band subset yield maximum accuracy. Experimental results demonstrated higher overall 
accuracy of PSBS compared to its conventional counterparts with limited sample sizes. 
1. INTRUDUCTION 
Hyperspectral technology, compared to the multi-spectral, is 
capable of reconstructing spectral signatures of phenomena, as 
well as producing a spectral library for earth observation. The 
NASA Jet Propulsion Laboratory (NASA/JPL) airborne 
visible/infrared imaging spectrometer (AVIRIS) and the Naval 
Research Laboratory HYperspectral Digital Imagery Collection 
Experiment (HYDICE) are two types of such sensors that 
collect image data with hundreds of spectral bands. Since the 
signatures of phenomena are sampled systematically in narrow 
band width with fixed sample intervals, not all bands are 
essentially useful for information extraction. Hence, in the 
context of hyperspectral data analysis, determination of 
informative bands needs to be considered for efficient 
representation of phenomena. 
Recent Support Vector Machine (SVM) algorithms have 
demonstrated good performance in dealing with high 
dimensional data (Mao, 2004). In the relevant, dimensionality 
reduction, however, is still required (due to the intrinsic 
dimensionality of the studied phenomena) to improve the 
generalization ability of the classification algorithm and to 
reduce the computational overhead (Mao, 2004; Pal, 2006). In 
the relevant literature, different band selection methods for 
dimensionality reduction are reported (Kudo and Sklansky, 
2000; Guan and et al, 2006; Martinez-Uso and et al, 2007). 
Band selection (BS) algorithms are categorized into two main 
approaches: supervised and unsupervised BS. In general, 
supervised BS methods, regardless of their search strategy stage, 
can be classified into two approaches based on their predefined 
criteria. The first one, called the filter approach, is based on 
optimising a discrimination measure, such as the Mahalanobis 
distance, Bhattacharyya distance, etc. The filter approach 
operates independently from any classification algorithm, so 
undesirable features are omitted before the classification 
process begins. The second one, called the wrapper approach, 
tries to optimize the classification accuracy of the desired 
classifier by selecting feature subsets. Feature shaving 
(Verzakov and et al, 2004) is an example of this category. 
Recently, also unsupervised feature selection procedures based 
on feature similarity (mitraand et al, 2002) and mutual 
information (Mart'mez-Us'o et al, 2006) have been proposed. 
In particular, the profit of BS depends on many parameters: an 
effective search strategy for exploring all possible subsets, 
definition of a criterion for evaluation of subsets and a 
classification algorithm for assessment of the accuracy of 
selected bands as the final subspace (feature size). 
Since these methods depend on the search strategy and in filter 
methods the criteria are based on predefined pair-wise class 
discriminant measure (like Mahalanobis and Bhattacharyya 
distances), they result in suboptimal solutions. Sometimes they 
suffer from shortcomings, such as a high correlation of 
neighbour bands for computing a separability measure, and 
classification with limited training samples in high dimensional 
space at the beginning of backward search or at the final steps 
of forward search algorithms. 
In this article, we attempt to introduce an innovative method for 
BS that makes use of only classes’ spectra. A natural question 
that motivated us was whether one can perform BS based on 
first order statistic parameters or class spectra obtained from a 
spectral library, while discarding separability criteria based on 
distribution of classes in high dimension. For this study, we 
propose a new space called the prototype space for band 
representation. Feature vectors in this space describe the band 
behaviour in terms of their reflectance in dealing with imaging 
scene phenomena. Conventional BS analyse the bands in terms 
of feature vectors, which are defined based on pixels. In