The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
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3) Select a certain number of individuals with high fitness value 
as “elitism” of the population and retain them in the next 
generation. It is in this way that the individuals with high fitness 
value are retained in the population and the principle of 
“survival of the fittest” of the Genetic Algorithm is conveyed. 
4) Check whether the termination conditions are satisfied. If so, 
the evolution stops and the optimal result represented by the 
best individual is returned. Otherwise, the evolution continues 
and the next generation is produced. The termination conditions 
can be either a predefined fitness threshold or number of 
generation evolved. 
5) If the population continue to evolve, the next generation are 
produced following procedure below. First, a certain number of 
individuals are selected randomly to compete the mating right. 
Two individuals of the highest fitness values are selected as a 
pair of parents. Crossover is operated on their chromosomes to 
produce two children individuals. Location of the crossover 
point on the chromosomes is also randomly determined; Second, 
a floating-point number in the range of 0.0 to 1.0 is generated 
randomly. If it is less than the predefined mutation possibility, 
mutation is operated for the two children individuals; Repeat 
the two steps before to produce all the children individuals 
(except the “elitism” individuals ) in the new generation. 
6) Repeat operations from step 2) to step 4). 
2.3.4 Realization of the GA-SVM 
The GA-SVM method was realized in the ENVI/IDL language. 
IDL (Interactive Data Language) is an array-oriented, 
interpreted, structured programming language that provides 
powerful data analysis and visualization capabilities. ENVI 
(Environment for Visualizing Images) is a software for the 
visualization, analysis, and presentation of all types of digital 
imagery. It is written in IDL, so most of its functionalities are 
provided to users in the form of functions that can be easily 
called in programming. 
In our research, we realized functionalities like opening and 
closing of the hyperspectral images, extraction data in the 
training ROI (Regions of Interest) and testing ROI, performing 
classification using the SVM, as well as evaluation of 
classification accuracy all through calling existing function 
provided by ENVI. The Genetic Algorithm was realized using 
the Object-Oriented programming technique of IDL. Two 
object classes, i.e. population and chromosome, were created 
with their properties defined respectively. Methods were then 
defined for these two object classes, respectively. Methods for 
the population class include population initialization, 
tournament selection, chromosome crossover and mutation etc. 
Methods for the chromosome class include coding and decoding 
of the chromosomes, setting and getting of chromosome 
properties etc. Finally, the GA-SVM method was programmed 
according to the steps described in section 2.3.3. 
3. EXPERIMENTS 
3.1 Data Sets 
The hyperspectral data used in this study is a cloudless 
Hyperion image taken on Dec. 18, 2005. The image centers at 
113° 20 ' 48 " E, 23° 5 ' 36 " N and covers part of the 
Guangzhou city, China. 
The Hyperion system on board the EO-1 platform provides a 
high resolution hyperspectral imager capable of resolving 220 
spectral bands (from 0.4 to 2.5 pm) with a 30-meter resolution. 
The instrument can image a 7.5 km by 100 km land area per 
image, and provide detailed spectral mapping across all 220 
channels with high radiometric accuracy. The Hyperion images 
has wide ranging applications in mining, geology, forestry, 
agriculture, and environmental management. Detailed 
classification of land assets through the Hyperion will enable 
more accurate remote mineral exploration, better predictions of 
crop yield and assessments, and better containment mapping. 
In this study, we selected part of a Hyperion image that covers 
the Haizhu district of Guangzhou city to test the GA-SVM 
method. Atmospheric correction and geometric correction of 
the image were first performed in order to eliminate 
atmospheric effects and compare to the ground truth data. 
3.2 Feature selection for the Hyperion image using the GA- 
SVM method 
The steps of feature selection for the Hyperion image using the 
GA-SVM method include: 
1) Create the training sets and testing sets needed by the GA- 
SVM method using the ENVI software. Land uses of the study 
area were categorized into six classes, namely built-up area, 
water body, grassland, forest and unused land. Training sets and 
testing sets were created for each land use class. 
2) Set parameters of the GA-SVM method. These parameters 
include: range of kernel parameter C and , bit lengths for the 
three chromosome parts, initial population size, population size 
of each generation, elitism size, number of generation to evolve, 
tournament size, crossover rate and mutation rate. These 
parameters were set as follow: min_C = 90.0 , max_C 
=.110.0 , miny = 0.9/bit length , maxy = 0.25 , 
initPopulationSize = 100 , populationSize - 50 » 
numGenerations = 40, offspringPerGen = 46. tournamentSize 
= 6, crossOverRate = 0.98, elitism = 4, mutationRate = 0.02. 
3) Run the GA-SVM model with the inputs of Hyperion image, 
training sets and testing sets. 
c 
Y 
Selected Bands (with their central 
wavelength, unit: mm) 
Classification 
Accuracy 
Kappa 
Coefficient 
No Feature Selection 
Feature Selection 
100.000 
0.000 
All 196 bands 
4(457.34), 12(538.74), 25(671.02), 
33(752.43), 61(1033.50), 67(1094.09), 
88.81% 
0.8619 
using the GA-SVM 
Method 
95.0297 
0.2021 
75(1174.77), 88(1305.96), 106(1487.53), 
109(1517.83), 117(1598.51), 127(1699.40), 
132(1749.79) 
92.51% 
0.9261 
Table 1. Performance of the GA-SVM Feature Selection