A NEW APPROACH FOR ACCURATE CLASSIFICATION

OF HYPERSPECTRAL IMAGES USING VIRTUAL SAMPLE

GENERATION BY CONCURRENT SELF-ORGANIZING MAPS

Victor-Emil Neagoe and Adrian-Dumitru Ciotec

Faculty of Electronics, Telecomm. & Information Technology, Polytechnic University of Bucharest

Splaiul Independenţei No. 313, Sector 6, Bucharest, Romania

Email: victoremil@gmail.com, adryyandc@yahoo.com

ABSTRACT

This paper presents an original approach for improving

performances of the supervised classifiers in hyperspectral

remote sensing imagery using generation of synthetic

samples to optimize the training set. The proposed model

called Virtual Sample Generation by Concurrent Self-

Organizing Maps (VSG-CSOM) is based on the idea of

improving the training set of a supervised classifier by

substituting the initial labeled sample set with the „virtual”

samples generated with a system of concurrent SOMs.

We have considered a Spatial-Contextual Support Vector

Machine (SC-SVM) classifier, taking into consideration

both intraband and also interband pixel correlation.

The proposed method is implemented and evaluated using

two of the most known data sets of hyperspectral images:

Indian Pines and Pavia University. The experimental results

prove the significant advantage of the new model.

1. INTRODUCTION

The information content of hyperspectral images with

hundreds of channels allows us to remotely identify ground

materials, based on their spectral signature [2], [3], [8], [14].

Each hyperspectral image pixel is a high dimensional vector

corresponding to the sampling of the incoming radiance at

this pixel. Classification of hyperspectral images is a very

challenging task due to the generally unfavorable ratio

between the large number of spectral bands and the limited

number of training samples available a priori, which results

in the Hughes phenomenon [14]. The sample size is often

limited and insufficient for most classifiers to give satisfying

performance. Hence, small sample size problem becomes the

main challenge to hyperspectral data analysis techniques.

Kernel methods and, in particular, support vector machines

(SVM) [1] have shown excellent performance in

multispectral/hyperspectral image classification [2], [3], [4],

[7, [14]. In order to obtain a robust classifier, the training set

needs to contain a sufficient amount of samples to

adequately represent the problem [1], [5], [12], [15]. The

traditional way to cope with the small training set problem is

interpolation, also known as noise injection [15]. By adding

noise to the real training vectors, one can generate virtual

samples to increase the size of the training lot and thus to

improve representational capability. At the same time,

enormous success has been achieved during the last decade

through modeling of biological and natural intelligence

under the umbrella of Computational Intelligence (CI),

whose main chapter corresponds to artificial neural

networks.

The proposed model called Virtual Sample Generation by

Concurrent Self-Organizing Maps (VSG-CSOM) is based on

the idea of substituting the original labeled training set by a new

set of synthetic samples generated with a system of Concurrent

SOMs [12] which best describe the patterns to build an

improved training set. For the hyperspectral pixel classifier, we

further use a SVM based on spatial-contextual information (SC-

SVM), taking into consideration both the interband and

intraband pixel correlation [7]. The experimental results of the

proposed model are given for two hyperspectral image data

bases: Indian Pines and Pavia University.

2. VIRTUAL SAMPLE GENERATION BY

CONCURRENT SELF-ORGANIZING MAPS

(VSG-CSOM)

The technique of Concurrent Self-Organizing Maps (CSOM)

has been proposed and developed by Neagoe et al for

pattern classification [9] and this has been applied for

multispectral image recognition [9], [10], [11]. Recently,

Neagoe et al have proposed the increasing of the training set

size for a facial expression recognition neural classifier, by

means of „Virtual” Sample Generation (VSG) using a set of

Concurrent Self-Organizing Maps (CSOM) [12]. The

novelty of the present paper based on the new model (VSG-

CSOM) by comparison to that given by the same authors in

[12] consists not only in applying now the mentioned model

for Earth Observation in hyperspectral imagery instead of

facial expression recognition, but also on the fact that

instead of using the synthetic sample generator to extend a

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given sample set, here the training set is composed by virtual

samples only that completely substitute the input original

samples. The steps of the proposed algorithm VSG-CSOM

are the following (Fig.1):

a. Building M pattern subsets

One splits the original labeled sample set into M

pattern subsets corresponding to each class (M being the

number of classes).

b. Training of each SOM(k) module (k=1,…,M)

One uses the SOM unsupervised algorithm [6] to train

each of the SOM modules. Namely, each SOM module has

been trained only with the samples having the same class

with the neural module label.

c. Virtual sample generation

After training, the weight vectors the SOM modules

have become virtual samples that substitute the input real

samples, building an improved training set to obtain a more

accurate classifier.

Fig. 1. Flowchart of the VSG-CSOM algorithm.

Fig. 2. Spatial-contextual representation of the K-band pixel

by the corresponding 9K-dimensional vector corresponding

to the 3x3xK hyper rectangle centered in the current pixel.

3. SPATIAL-CONTEXTUAL CLASSIFICATIN OF

HYPERSPECTRAL PIXELS WITH SUPPORT

VECTOR MACHINE (SC-SVM)

The Support Vector Machine is a pattern classification

technique, which attempts to minimize the upper bound of

the generalization error by maximizing the margin between

the separating hyperplane and the training data [1]. SVM

achieves greater empirical accuracy and better generalization

capabilities than other standard supervised classifiers.

Recent studies show that SVMs with both spectral and

spatial information achieve effective and stable multispectral

image classification [7]. To classify the hyperspectral pixels

(K bands), one chooses a spatial-contextual representation of

the K-band pixel as a 9K dimensional vector (using a

second-order neighborhood system in the original space).

Consequently, we have used the interband and the intraband

correlation by representing the K-band pixel by the

corresponding hyper rectangle centered in the current

hyperspectral pixel (see Fig.2). This hyper rectangle has a

square base of 3x3 and a height of K, corresponding to the

9K dimensional vector representation.

4. EXPERIMENTAL RESULTS

4.1. Image data bases

4.1.1. Indian Pines

This scene was gathered by AVIRIS sensor over the Indian

Pines test site in North-western Indiana and consists of 145

x145 pixels and 224 spectral reflectance bands. The ground

truth available is designated into sixteen classes and is not

all mutually exclusive. The image resolution is of 20

meters/pixel. The number of bands has been reduced to 200

by removing bands covering the region of water absorption.

4.1.2. Pavia University

The image is acquired by ROSIS sensor over Pavia, northern

Italy. The number of spectral bands is 103 and the image

size is 610x340 pixels, but some of the samples in both

images contain no information and have to be discarded

before the analysis. The geometric resolution is 1.3

meters/pixel. The image groundtruth differentiates 9 classes.

4.2. Experimental Results

The experimental results are given in Tables 1 and 2 as well

as in Figs. 3, 4, 5, and 6.

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Fig. 3. (a) Groundtruth of Indian Pines dataset. (b) Classified

hyperspectral pixel map (16 categories) with the model

(VSG-CSOM)-(SC-SVM), using a learning set containing

5% of the total pixels for each class.

Fig. 4. (a) Groundtruth of Pavia University dataset. (b)

Classified hyperspectral pixel map (9 categories) with the

model (VSG-CSOM)-(SC-SVM), using a learning set

containing 5% of the total.

Table 1. Maximum recognition score [%] for Indian Pines

hyperspectral image dataset (with a learning set of 5% of the

total pixels for each class)

Classifier SVM

(VSG-

CSOM)-

SVM

(VSG-

CSOM)-

(SC-SVM)

LDA-

SVM

Maximum correct

recognition score [%] 66.07 75.10 76.93 68.82

Fig. 5. Maximum recognition score [%] for Indian Pines

hyperspectral image dataset.

Table 2. Maximum recognition score [%] for

Pavia University hyperspectral image dataset (with a

learning set of 5% of the total pixels for each class)

Classifier SVM

(VSG-

CSOM)-

SVM

(VSG-

CSOM)-

(SC-SVM)

LDA-

SVM

Maximum correct

recognition score [%] 87.95 93.07 96.50 90.89

Fig. 6. Maximum recognition score [%] for Pavia University

hyperspectral image dataset.

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5. CONCLUDING REMARKS

a. The model proves its significant advantage of substituting

the original image training samples with the synthetic ones

in challenging classification cases (when the learning set

contains only 5% of the sample set of each class).

b. For the Indian Pines dataset, choosing a learning set

containing 5% of the total pixels of each class, one obtains

with the proposed model (VSG-CSOM)-SVM an increasing

of 9.03% of the recognition score over the SVM score; for the

(VSG-CSOM)-(SC-SVM) variant (adding spatial-contextual

information to the proposed model), one obtains an increasing

of 10.86% over SVM. As a benchmark, one also uses the

LDA – SVM cascade (where LDA=Linear Discriminant

Analysis). One remark the performance of LDA-SVM is with

6.28% less than that of (VSG-CSOM)-SVM.

c. For the Pavia University dataset, with a learning set

containing 5% of the total pixels of each class, the classifier

(VSG-CSOM)-SVM leads to the recognition score of

93.07%, representing an increasing of 5.12 % over the

SVM. For the (VSG-CSOM)-(SC-SVM) variant, one obtains

a recognition score of 96.50%, representing an increasing

of 8.55% over SVM. For comparison, the LDA-SVM proves

to be less successful than the proposed model

(VSG-CSOM).

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