Keyword: hyperspectral, band selection

Abstract
Band selection was performed based on hyperspectral data taken at 400-900 nm spectral range for 25 subtropical tree species in Hong Kong. Stepwise discriminate analysis and hierarchical clustering were used to select bands with high discriminatory power. In addition to the blue, green, red and near-infrard bands, spectral bands along the blue-green, red and near-infrared bands, spectral bands along the blue-green edge, green red edge and red curves were found to have important information to discriminate the 25 tree species which could identify tree species with 89% overall accuracy.

Introduction
The increasing availability of hyperspectral data and image has enriched us with better data for environmental monitoring, forest tree species, geological exploration and many other applications as well. Currently, most studies focus on either using airborne hyperspectral sensors such as the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) or in situ collected field spectra. With hyperspectral scanner onboard satellites (e.g. Orbiview-4), hyperspectral image analysis will certainly be a major arena of research and applications.

The use of hyperspectral data analysis, however, brings along research issues. Price (1994) noted three major issues including (1) the trade-off between the number of spectral intervals (bands) and spatial resolution of the imagery; (2) the trade -off between higher spectral resolution and reduced signal-to-noise ratio and (3) appropriate positioning of the selected spectral bands in order to provide information about the objects of interest. Hyperspectral data undoubtedly possess a rich amount of information. Nevertheless, redundancy in information among the hundreds of bands opens and area for research to explore the optimal selection of bands for analysis. Price (1994) indicated that for natural materials, 15-25 spectral bands were sufficient whereas the number of bands had to be doubled for the studies of minerals. Petric and heasler (1998) examined optimal band selection strategies using spatial autocorrelation; spectral autocorrelation and optimization with distance metrics. Warner and Shank (1997) also examined the use of spatial autocorrelation for narrow band selection.

The objective of this studyis to investigate how many bands and in which spectral ranges these bands are needed to identify subtropical tree species based on their hyperspectral reflectance's. Tropical and subtropical environment is endowed with a rich diversity of flora which also renders it a difficult but challenging environment for monitoring. Under this project, we have earlier examined the effectiveness of subtropical tree identification using hyperspectral data (Fung et al., 1999). The results of using back propagation feed forward neural network and linear discriminate analysis for classification revealed that about 80% overall accuracy could be attained with autumn being the best season for hyperspectral data and suggest suitable bands for tree species recognition.

Hyperspectral data collection
A high spectral resolution spectrometer PSD2000 was used for taking hyperspectral data. The spectrometer was linked with a notebook computer for data acquisition and analysis. It took data ranging from 210 mm to 1050 nm with spectral resolution of approximately 2.6 nm. It had a field of view of 22o. During data collection, two references, a white and a dark, were used for calibration. Based on the illumination condition, an integration time for collecting photons was selected to adjust the master/slave sampling frequency to avoid saturationor shortage. Sample spectra could then be collected through dividing the sample radiance by that of the standard while reference under the same illumination condition. In this study, we only examined data from 400-900 nm to avoid bands with too much noise.

Subtropical trees were the prime focus of this study. Twenty-five tree species within the campus of the Chinese University of Hong Kong were selected fro the study. They included both native and exotic; needleleaf and broadleaf species. These trees were also commonly planted in the country parks and urban areas of Hong Kong. They are listed as followings:

Acacia confusa	Ficus variegata
Araucaria heterophylla	Hibiscus tiliaceus
Acacia mangium	Lophostemon conferta
Bauhinia variegate	Liquidambar formosana
Cinnamomum camphora	Lagerstroemia speciosa
Casuarina equisetifolia	Mealaleuca quanqueenervia
Castanopsis fissa	Macaranga tanarius
Cratoxylum ligustrinum	Pinus elliottii
Aleurites moluccana	Thuja orientalis
Dimocarpus longan	Schima superba
Delonix regia	Sapium sebiferum
Ficus microcarpa	Taxodium distichum
Firmiana simplex