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Hyperspectral Image Processing and Analysis System (HIPAS) and Its Applications

Blng Zhang, Xlanglun Wang, Jlangul Uu, Lanfen Zheng, and Qlngxl Tong

Abstract A hyperspectral image processing and analysis system (HLPAS) has been developed b y the Institute of Remote Sensing Ap- plications of the Chinese Academy of Sciences. The HEAS, built on Interactive Data Language (LDL) and implemented on Windows NT workstations, meets the requirements for the rapid preprocessing of imaging spectrometer data and easy proto- typing of algorithms. Integrated with a spectral library, which was implemented on the FoxPro, a popular database environment in the Windows NT platform, the spectral analysis model was established to support hyperspectral image analyses. Based on the HIPAS, some hyperspectral remote sensing application studies were completed in China. These included mineral identification, agriculture investigation, urban mapping, and the study of wetland vegetation.

Introduction The trend in the development of remote sensing has been, with the increase of the spectral resolution, moved hom the panchromatic multispectral to the hyperspectral, and then to the ultra- spectral. During the last 15 years, studies on hyperspectral remote sensing have been carried out intensively in China. In the Chinese airborne remote sensing system, the hyperspectral sensor is already in place as one of the basic systems. In the past ten years, several imaging spectrometers, such as the Modular Air- borne Imaging Spectrometer (MAIS) and Push-broom Hyperspec- tral Imager (PHI), have been developed in China. In addition, a new model visible to the thermal infrared 128-channel, called the Operational Modular Imaging Spectrometer (OMIS), is being developed at the Shanghai Institute of Technical Physics of the Chi- nese Academy of Sciences. The technical characteristics of some hyperspectral imagers in the world are presented in Table 1.

With the increased availability of airborne imaging spectrometers for studying and monitoring the environment and managing the Earth's resources over the years, the volume of data has increased steadily (Kruse et al., 1993). Given the high volume and complexity of data acquired with hyperspectral instruments, specialized image processing software in the areas of data handling, preprocessing, visualization, and information extraction are required. Several commercial software packages have been available since the beginning of the early 90s. With scientific advances in hyperspectral remote sensing over the last decade, the functionality of commercial systems has increased. PCI's EASIIPACE (PCI, 1997), a traditional image analysis system, has also expanded some hyperspectral processing capabilities. Some stand-alone systems, such as the Hyperspectral Image Processing System (HIPS) (Susner et al., 1994) and the Environment for Visualizing Images (ENVI) (RSI, 1997), have also been developed. In order to expand the capa- bility of hyperspectral data processing and analysis, and especially to meet the data processing needs of MAIS, PHI, OMIS, and

Laboratory of Remote Sensing Information Sciences, Institute of Remote Sensing Applications, Chinese Academy of Sci- ences, P.O. Box 9718, Beijing 100101, P.R. China.

their applications, the HPAS was put under development in 1995. This system satisfies the following design goals:

It should be implemented on a readily available computer platform which has adequate processing power but remains finan- cially affordable for general users and science researchers; It should be able to preprocess the original data obtained by MAIS, PHI, and OMIS developed in China and remain compatible with the popular hyperspectral imaging data around the world; and It should establish an environment for the rapid and easy development of new algorithms, which can then be added to the system.

Based on this hyperspectral image processing and analysis system, some analysis and application models were developed and achieved successful study results in areas such as mineral exploration, precise vegetation classification, urban investigation, and global change.

System Environment and Basic Concept The HIPAS system runs in a Windows NT environment on any personal computer operating with the Intel CPU. HIPAS uses the Interactive Data Language (DL) as the basic developmental environment. DL is a complete computing environment for the interactive analysis and visualization of data (RSI, 1995). mL integrates a powerful, array-oriented language with numerous mathematical analysis and graphical display techniques.

Using DL can greatly save time when developing a data visualization and processing tool. Some modules that require high-speed performance are programmed directly using the C+ + language, and are compiled as Windows Dynamic Link- ing Library (DLL). The CAUEXTERNAL function of DL is used to call the DLL models. The preprocessing models of HIPAS are especially designed for processing large amounts of hyperspectral data quickly. Those models are programmed with the C + + language.

HIPAS is also integrated with a spectral database attached with some easy spectral analysis tools. FoxPro, a widely used database system in personal computer systems, is used to construct a spectral database in the HPAS system, and it can com- municate with DL through the Dynamic Data Exchange mecha- nism of the Windows NT system. The database is designed for storing newly measured spectral data. To meet the demands of advanced spectral analysis, more information about spectral measurement objects and relative backgrounds can also be stored in the database. Based on such a spectral database and available tools, it is easy to create a new spectral database or modify existing spectrum data. It also has some spectral-feature extracting functions.

Photogrammetric Engineering & Remote Sensing Vol. 66, No. 5, May 2000, pp. 605-609.

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and Remote Sensing

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TABLE 1. SOME TECHNICAL CHARACTERISTICS OFTHE HYPERSPECTRAL IMAGERS

Spectral Number of Spectral IFOV FOV Available Name Coverage (fim) Bands Interval (nm) (mrad) (degree) Developer Date

MAIS 0.44-11.8 71 201600 3 90 Shanghai Institute of Technical Physics(SITP), China 1991 PHI 0.4-1.0 244 5 1.5 21 SITP, China 1997 OMIS 0.4-12.5 128 10/60/15/500 1.5 70 SITP, China 1999 AVIRIS 0.38-2.5 224 10 1 30 JPL, U.S.A 1987 GERIS 0.4-2.5 63 25/120/16 2.5 90 GER Corp. U.S.A 1986 CASI 0.4-1.0 288 2.9 1 35 ITRES Research, Canada 1989 MIVIS 0.43-12.7 102 20150181400 2.0 70 Daedalus Enterprise Inc., U.S.A 1993 HyMap 0.45-2.48 126 1511511 3/17 2.012.5 61.3 Integrated Spectronics, Australia 1997

The design of the HIPAS is based on the Object-Oriented method. Through analyzing the procedures of data processing and information extraction, some basic data objects are ab- stracted for constructing the whole system. There are two sets of objects-the data object and the user interface object. Cur- rently some basic data objects have been defined. They are ID spectral data, the 2D spectral slice, the 3D image cube, vector data, annotation data, and the interface object. ID spectral data is spectral reflectance data combined with corresponding wavelength data. The 2D spectral slice is some spectrums expanding in a two-space direction. The 3D image cube is defined as 2D image data in a spectral space as the third dimen- sion. Vector data is a set of x, ycoordinate data. Annotation data contains user-defined text, symbols, and other marks for objects description on an image display. The system uses the interface objects to exchange information with its users. Figure 1 shows the main framework of the HIPAS. Basic objects define the data structure and encapsulate functions to access the data. The encapsulated objects can be used by other models without interfering with each other. User interface objects are con- structed with the IDL widgets. The data and attribute attached with the widget can be modified through the mL function call. It ensures interface stability and makes it easy to be modified.

The system has following data objects:

Image object Lookup table object Pseudo color table Ground control points (GCP) object Filter kernel

Mathematics formula Spectral data

System Management I

Information Extraction I

Data Processing 1 User Interface

Basic Object Accessing

The system uses some mL basic widgets, including widget-base, widget-button, widget-draw, widget-droplist, widget-lable, widget-list, widget-slider, widget-table, and so on, to construct the interfaces for the main menu, the display of images, spectra, status information, and the inputting of parameters inputting. FoxPro is used to construct the spectral database interface because it is more flexible.

There are many data objects that need to be processed, and each object has a different disk file format. The HIPAS system defines a common object database as a disk file in a unique format. All the data objects are then stored in the same file. The HIPAS file is separated into two parts, the file header and object data area. The file header contains the file description and the index of object data. Each index item has a description of the object type, and a pointer gives the location of the object data area. Each HIPAS data file can contain more than one object. Each object is defined and stored in the object data area. The object data have two parts, the object description and the object data. All the information needed to access the object data is held in the object description data.

3-D Image 2-D Spectral Annotation Cube Slice Data

Main Functions The HPAs functions can be divided into seven main modules: data input and output, data preprocessing, conventional image processing, spectral analysis, interactive analysis tools, spectral database tools, and advanced tools. An overview of the functions is listed in Table 2.

2-D Vector

Hyperspectral Remote Sensing Applications Based on HIPAS One of the key features of HIPAS is that it is very easy to add new analysis models or algorithms. The hyperspectral applications based on HIPAS covered mineral identification, urban land-use investigation, as well as vegetation and crop classification.

I + v 1 -D Spectral Data Interface Object

Figure 1. Main framework of HIPAS.

Mineral ldentificatlon Mineral identification is the basic applicational area of hyperspectral remote sensing. In these applications, the MAIS has been used and flown over some test and exploration areas in the Zhunger and Tarim Basins of China. The field spectral measure- ments show that the distinguishable spectral features for different minerals can be seen clearly in the short-wave infrared region (Figure 2). This is due to the bending-stretching features of OH-, CO:-, A12+-OH, MgC-OH, and SO;- bearing minerals. The airborne hyperspectral data acquired in the Tarim area of Xinjiang, China have been processed and analyzed for geologi- cal application. Very exciting results were obtained in strati- graphic mapping of the studied area by the hyperspectral technique. The different strata from the Cambian-Ordovician, Silurian, Devonian, Carboniferous, and Permain periods in the Keping area of West Tarim were clearly separated and classified for the dominant minerals of each stratrum. Data from the lime- stone in the Cambian-Ordovician and Permian strata had

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TABLE 2. OVERVIEW OF THE HIPAS FUNCTIONS

Modules Tools Function

Data Input/Output Data Import Data Export Tape Utility

Data Preprocessing

Conventional Image Processing

Spectral Analysis

Geometric Correction System Radiometer Calibration for

MAISIPHI Noise Removal Tool Image Browse Tool Spectral Simulation Image transform

Image filtering

Image Classification

Registration Tools

Spectrum Filtering and Transform

Unmixing Spectral Matching Quantitative Parameter

Estimation Interactive Analy- X,Y,Z profile and Spectral Slicing

sis Tools Annotation Tools ROI Tools.

Spectral Database Basic function Tools

Advanced Tools Image Fusion

Import image files to HIPAS format files Export HZPAS files to raw data or general format files Read/Write files from/to a Windows NT system supported tape

driver Correct the raw image using the synchronous GPS data Calibrate the image using the MAISIPHI instrument calibration

parameters Remove instrument noise and fix bad lines automatic or manually Browse user selected image cube quickly Spectrally simulates data for a pre-defined sensors Transform image in spatial or spectral domain, including data

stretching FFT, PCA, etc.

Filter image in spatial domain, including low pass, high pass, user defined filter, etc.

Classify image using supervised or unsupervised method, including maximum likelihood classification and ISODATA.

Reference images to geographic coordinates or correct them to match base image geometry.

Filter spectrum with a low pass filter. Transform reflection image cube into derivative spectron

Perform linear spectral unmixing Classify image using spectral characteristic Retrieves quantitative parameters from image

Extract spectrum curve and spectrum slice &om image cube interactively

Add and edit annotation to image Define region of interest Create, modify, query spectral database and extract spectral features

Fuse images with a series of combined functions

almost the same display as the wide-band remotely sensed the above two minerals in the two strata of Cambian-Ordovi- data such as Landsat and SPOT, and they could hardly be distin- cian and Permian were clearly separated, even though the dif- guished from each other. This is due to the different dominant ference in absorption bands of those minerals is very small in minerals in these two strata, i.e., calcite for the Carnbian-Ordov- wavelength. ician stratum and dolomite for the Permian stratum. By using The depth of the absorption band is closely related to the the mineral absorption index in these three bands, i.e., 2.331, amount of the minerals in the rocks. By an analysis of the wave- 2.347, and 2.364 pm, the characterized spectral absorption of length location and intensity of the absorption, the distribution

of clay and carbonate minerals in the area has been identified and then mapped (Tong et al., 1998).

I Cakite 2 Dolomite 3 Serpintine 4 Chlorite 5 farosire 6 Liomite 7 Monrmorillo~te 8 Illitite 9 KaoPite LO Sericite

2.0 2.1 2.2 2.3 2.4 2.5

Wanlcngth(pm)

Figure 2. The reflectance spectra of some minerals.

Urban LandUse Investigation At present, the spectral features that are diagnostic of some materials are commonly used in the identification of the hyperspectral image. These methods require a regular reflectance calibration and high signal-to-noise ratio. It is clear from signal theory principles that features which uniquely define the classes are already present in the uncalibrated radiance spectra even though these features are not observable manually (Joseph et al., 1996). Due to the complicated urban environment and relatively disorderly earth objects in the city, it is not easy to get a satisfactory classification result when one depends only on a pixel-to-pixel spectral analysis, especially when the materials lack strong absorption features and the remote sensing data have a low signal-to-noise ratio (Kruse et al., 1990). In particu- lar, it is not suitable for image classification on a large scale, such as in urban mapping. In our study, two kinds of airborne data were acquired. One was hyperspectral data, which provided us with very fine spectral profiles. Another was a panchromatic aerial photo of the same region at 1:3,000 scale. After being digitized at a 600-dpi resolution, the photograph data produced fine geometric forms of the object classes. A new image classification method was provided by HIPAS. All spectral analyses and assemblies were not based on pixels but on polygons which are geometrically extracted from the digital


photograph. These polygons in the image represent some of the kinds of independent objects which have relatively even radiance value, such as streets, buildings, squares, lawns, etc. This approach has the advantage of reducing the negative effect of the noise-spots in an image and hence is more suitable for the geography in urban area (Zhang et al., 1998).

In addition, the other advantages of HIPAS is its fast image mosaic function, which makes it possible and realistic to gen- erate a hyperspectral image of a large urban area. Figure 3 is a mosaicked PHI hyperspectral image map from the stripe images of 17 flight lines in Beihai City in South China. For this image map the precise geometric correction and high speed processing of large volume of hyperspectral data is necessary. Image fusion functions in HIPAS are used to integrate a lower spatial resolution multispectral image with a higher spatial resolution panchromatic image, so as to benefit from the advantages of both.

Wetland Environment investigation Wetlands are a sensitive indicator of global environmental change. Based on the MAIS data and HIPAS tools, the derivative spectral analysis technique was used for wetland vegetation classification, identification, and vegetation biomass mapping (Tong et al., 1997). The derivative spectral model has been pro- posed as a measurement of vegetation spectral red-edge ampli- tude to reduce the background effect, which often occurs with a broad-band vegetation index. Based on some sampling analyses, the Derivative Spectral Vegetation Index (DSVI) is highly correlated with vegetation cover percentage and biomass. And it is effective for reducing soil influence and eliminating atmo- spheric effects on vegetation, and indicating the vegetation's movement or vigor of vegetation. The derivative spectra-based matching and classification in HIPAS were also applied to this project. It achieved good results, with most wetland vegetation types classified, with the overall accuracy of the vegetation biomass map reaching 89.5 percent for 18 samples.

Crop ldentlflcation by Their Spectral Red Eedge For typical growing vegetation, there are sharp reflectance changes in the spectral region between 680 and 750 nm, usually called the "red edge" (Horler et al., 1983). Broad-band spectral data (s 100-nm band width) is of limited value for the description of these features. The features of red edge is often used for the discrimination of vegetation species, the biochemical analysis of vegetation, and the extrapolation of the relevant parameters. Usually, two parameters are employed to describe the red- edge features. One is Ared; it is defined as the wavelength of maximum slope, and found to be dependent on chlorophyI1 concentration, with additional effects of species, development

-- Figure 3. PHI image map of Beihai City.

- - - 2-Sw - - 27-sap

0 Wavelength (nm)

Figure 4. Derivative spectra of cotton in different growing seasons.

stage, layering, and leaf water content. Another one is the maximum slope, (dRIdA),, found to be independent of simulated ground area coverage (Horler et al., 1983). The reflectance (R), first-order or high-order derivatives of reflectance, or their transformations in a continuous spectral wave bands can be used to identify different vegetation, for example pine tree species (Pu et al., 1993). The vegetation index with a continuous series of narrow bands across the red-edge region can also be used to monitor green vegetation (Elvidge and Chen, 1995). In addition, the parameter, Ared, wavelength of maximum slope can be used to estimate LAI (Gilabert et al., 1996).

For this "red-edge" feature, there are some vegetation spectral analysis and related image processing models in HIPAS. From Figures 4 and 5, we can see the surrounded area of derivative spectral wave change associated with different growing seasons and different crops. Hence, the derivative and integrative analysis model was used to identify the vegetation growing situation and to classify different crops(Zhang, 1998). Figure 6 shows the crops classification results, also based on such a model.

Conclusion A comprehensive hyperspectral image analyzing system, the Hyperspectral Image Processing and Analysis System (HIPAS), has been presented. HPAS is built on the mL, a commercially available graphics and data processing software product, and can be implemented on personal computers. This software is

Xklyricr 5 July

1 - Soybeat

I Figure 5. Derivative spectra of different crops.


Figure 6. Vegetation classification based on spectral derivative and integrative model.

designed according to the Object-Oriented method for the easy addition and modification of hyperspectral processing functions. HIPAS has tools for inputloutput, preprocessing, data visualization, information extraction, conventional image analysis, advanced tools, and integration with a spectral database. It has been successfully used in the application of hyperspectral remote sensing in China, both in areas of environmental study and resource inventory and in studies of urban environ- ments and wetlands. An English version of the HIPAS will soon be developed and released.

Acknowledgments The whole project was supported by the Chinese High-Technol- ogy Developing Program and the National Natural Science Foundation.

References Elvidge, C.D., and Z. Chen, 1995. Comparison of broad-band and nar-

row-band red and near-infrared vegetation indices, Remote Sens- ing of Environment, 54:38-48.

Horler, D.N.H, M. Dockray, and J. Barber, 1983. The red edge of plant leaf reflectance, Int. J. Remote Sensing, 4273-288.

Joseph, P.H., and A.L. David, 1996. Classification of remote sensing images having high spectral resolution, Remote Sensing of Envi- ronment, 57:119-126.

Kruse, F., A.B. Lefkoff, J.W. Boardman, [other author's names], 1993. The Spectral Image Processing System (SIPS)-Interactive visualization and analysis of imaging spectrometer data, Remote Sensing of Environment, 44:145-163.

Kruse, F.A., K.S. Careen-Young, and J.W. Boardman, 1990. Mineral mapping at Cuprite, Nevada with a 63 channel imaging spectrometer, Photogmmmetric Engineering 6 Remote Sensing, 56:83-92.

Gilabert, M.A., S. Gandia, and J. Melia, 1996. Analyses of spectral- biophysical relationships for a corn canopy, Remote Sensing of Environment, 55:ll-20,.

PCI, 1997. Using PCI Software, PC1 Corp., Richmond Hill, Ontario, Canada, 551 p.

Pu, R., P. Gong, and J. Miller, 1993. Regression analysis between leaf area index of Pine tree and CASI data of high resolution, Remote Sens. Envi. China, 8:112-125,.

RSI, 1995. D L Interactive Data Language User's Guide, Version 4.0, Research Systems Inc., Boulder, Colorado.

, 1997. ENVI User's Guide, Version 2.6, Research Systems Inc., Boulder, Colorado.

Susner, N.J., J.T. Lo, and T.B. McCord, 1994. A Hyperspectral Image Processing System-HIPS, Proceedings of the International Sympo- sium on Spectral Sensing Research, San Diego, California, p. 496.

Tong, Qingxi, Lanfen Zheng, and Yongqi Xue, 1998. Development and application of hyperspectral remote sensing in China, SPIE, 3502~2-9.

Tong, Qingxi, Lanfen Zheng, Jinnian Wang, and Bing Zhang, 1997. Studv on the wetland environment bv airborne hwers~ectral remdte sensing, Proceedings of the ~ i i r d ~nternatiinal krborne Remote Sensing Conference and Exhibition, Copenhagen, 1:67-74.

Zhang, Bing, ~ian&i Liu, Xiangjun Wang, and chLgsh& Wu, 1998. Study on the classification of hyperspectral data in urban area, SPE, 3502:169

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(Note: The customary western practice of listing author's family names last, except in the list of references where only the first author's name is listed family name first, is followed herein.)

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