Methods of Hyperspectral Image Processing

Methods of hyperspectral image processing and the analysis of the data from the first Hungarian airborn imaging spectroscopy campaign

DOCTORAL THESIS, ENGLISH VERSION

HARGITAI, Henrik

Advisors: KARDEVÁN Péter, MARI László

Eötvös Loránd University, Faculty of Sciences Doctoral School of Earth Sciences Geography Program 2006

1 1. Introduction, goals Present paper delas with one of the latest field of remote sensing, namely hyperspectral remote sensing. It presents the realization of the first Hungarian hyperspectral remote sensing campaign, made in the frame of the international Hysens 2002 program, on 17-18 August 2002, and analyses its results step by step on the example of three field area. During the campaign we used the DAIS imaging spectrometer which produced data cubes of 6 m/px resolution, in 79 spectral bands. For field spectrometer, we used the 646 band GER 3700. Imaging spectroscopy—or hypsrspectral remote sensing—uses a data cube, which represents an area split to pixels, where each pixel contains a full spectrum of the particular ground area of that pixel. Hyperspectral images contain tens or hundrends of contiguous bands, in contrast with multispectral images which are composed of 5-10 bands. Laboratory based spectroradiometers divide the c. same spectrum to hundreds of narrow bands. Thus, fine details of the spectrum become visible that used to be invisible using previous technologies. (Fig 1-2).

Fig 1. 646 bands of a field spectrometer (B) contrasting 7 bands of LANDSAT TM (A). Healthy vegetation. Y axis: reflectance

Fig 2. Characteristic spectra from the 2002 spectroradiometes measurements. Talaj=Soil, Növényzet=Vegetation, Virág=Flower, Tó=Lake water

2 The basic goal of the reseach was to prepare a spectral library that contains various vegetation types (from species to „spectral landscapes” of mixed vegetation), and using this library to define the areas of the different deciduous forest types of the Recsk and Gyöngyösoroszi area (both in the Matra mountain). This task is not possible using multispectral imagery. Biochemical characteristics of vegetation is more or less the same, regardless of the particular species (in the case of minerals, chemical characteristics vary). Scattering and reflectance from leaves is an other problem. What makes them possible to differentiate is the pigment concentration, unique biochemical characteristics, leaf and tree crown structure, making the reflectance also different. Broader vegetation units as forests (decidous/conifers), fields, bushes could be differentiated earlier with good precision, but in other cases, and especially within the aforementionned categories, more detailed differentiation can not be automatized safely. An other problem is the heterogenity of the forests on pixel level: it is not easy to find a homogene pixel regarding the species imaged. A Central European vegetation spectral library would serve as a basis for all future work on this field.

* This paper being the first such summary, its other aim is to provide a Guidebook for the future Hungarian users, for preparing, making and analysing hyperspectral campaigns and images. A airborn hyperspectral service is now being prepared for regular commercial use in Hungary. For achieving this goal, the paper presents a detailed plan of the processing and analysing of hyperspectral images, emphesizing the possible traps and errors to avoid. In a speparate listing it presents a English-Hungarian correllational dictionnary of the most frequently used terms in hyperspectral imaging, for a better and may be standardized use of the terms in Hungarian, avoiding chaos due to various translations. Finally, its last but not least important goal is to document how this first imaging spectroscopy campaign was organized and realized, also documenting the first Hungarian or Hungarian-related results of the analysis of the images of the Hyses 2002 campaign.

2. Methods The hyperspectral images (data cubes) have been taken on 17-18 Aug 2002 in the frame of Hysens 2002 project. The images had been pre-processed at DLR (Germany). Later I used ENVI 3,5-4,0 for image processing. Since the preprocessing at DLR was not without errors,

3 the data can not be regarded flawless. Despite this it was possible to achieve some results using the raw and the partly pre-processed images. I have participated in both the preparing and executing phase of the work. Whenever I had the opportunity, I have made measurements using spectroradiometer. Later I have purchased a GPS unit for my work. In a later phase my own research was carried out in the Gyöngyösoroszi and Recsk areas, where my goal was to identify and differentiate the vegetation cover. One of the most important phase of the image processing was ground truth collection, since a spectral library containing species of Hungary was not available. In the field work I used three methods. 1. Identifying homogenious areas in the image.– it is visible in the pre-processed images that different spectral areas can be identified within the decidous forests. The goal is to find these areas on the field and identify them. 2. Identifying homogenious areas in „real life”.– such spots can be used as endmembers in the image classification phase. 3. Spots of Mixed species which are otherwise homogenious spectrally. They can also be used as endmembers or target spectra in the classification process.

3. Results and conclusions 3.1. (literature review) 1. I have summarized the history and organization of first Hungarian hyperspectral campaign 2. I have summarized the Hungarian results dealing with hyperspectral imaging, especially using the images from this campaign 3. I have compiled an English-Hungarian glossary of terms 4. I have summarized the basis of hypespectral remote sensing and image processing (Fig 4.) This was a novelty 6 years ago, when I have decided to work on this area, however, today, in the Western World hyperspectral imaging have become a common method.

3.2. (Field work) 5. I have compiled a spectral libraray containing spectra of agricultural plants (from the Tedej area) and species of the natural vegetation cover (in the Gyöngyösoroszi and Recsk area), using field and laboratory measurements. These data can be extracted from the electronic version of the doctoral thesis. The new categories can be defined easily in the case of those categories which are easy to define in the case of multispectral images. The categories that can not be differentiated in multispectral images are more problematic. The

4 spectral categories of decisuous forests reflects species differencies only partly: ground truth proves that other factors play an equally important rule (topography, age of the forest, closure of the crown level and other unknown factors) which in cases can be more important that the species difference. 6. I have concluded that the results from Forest Documentations (databases) and field ground truth collection (from other ground points) yielded in more or less similar results. Thus, the spectral classes reflect real spectral landscapes. However, the resulting spectra can only be used for later reference: more ground verification and research is needed. 7. I have concluded that field and laboratory spectra differs in the case of vegetation. The vegatation cover is different in real life and laboratory, thus for reference spectra, a spectroraiometer must be placed high above the particular target material. Field spectra must be atmospherically corrected. The field view and spatial resolution also important parts of the particular spectrum in order to be comparable in various spektrometer data. 8. I have concluded that for a usable reference spectral library it is needed to follow the change of the spectra in a whole vegetational period as is done in the case of agricultural works. Such work could not yet be done in the case of natural vegetation due to the novelty of the technology in Central Europe. Selecting a target area it is important to find a heterogenious one (regarding target features) where this material can be found in several different—but homogenious—spots. If it was successful to find a period when the target materials’ spectra differs the most, this should be used for any future works. Various spectrally different but as a forest type (species), similar vegetation covers must be included. 9. To differentiate oak from beech, I have found the following DAIS bands useful (most different; indicative), using average spectra. At the maximal reflectance (1.035 micon), oak had a 25-28, beech 30-35% reflectance, thus making the spectra of these two forest types, at least those areas visible in this image, easy to differentiate (Fig 3-4.,Table 1). Spectral types Bands (micrometer)

“Pine-Deciduous” 0,869

“ Young-other 0,832; 0,86-0,938; 1,66-1,72 deciduous”

“Oak-Beech” 0,623; 0,78, 0,851 1,727 2,08, 2,115, 2,13, 2,145, 2,158, 2,23 Table 1. Bands of spectra of vegetation indicative absorption bands

5 Fig 3: „tölgy=Oak” (black line) and „bükk=Beech” (light line) categories’ spectral curves reveal their differences after continnum removal (encircled) X axis: wavelenght, in micrometers

A recski terület átlagolt célspektrumai erdészeti adatbázis (A) illetve terepi (B) adatok alapján homogén felszínborítású területeken kijelölt tanítómezőkre 400 Fiatalos bükk (B)

Bükk (A) 350 Bükk (B) 300 Tölgy (B) ] %

[ Tölgy (A)

) 250 0 Fenyő (A) 1 * (

i Fenyő (B)

c 200 n a t k e l

f 150 e víz elnyelés R 100

Klorofill elnyelési tartomány Levélszerkezet és összetétel jellemző 50 elnyelési sávjai víz elnyelés

0 0 1 3 5 7 9 1 2 4 6 8 9 1 3 4 6 8 0 1 3 5 7 9 0 2 4 6 7 9 1 2 4 4 7 0 4 7 0 3 6 8 0 2 3 5 6 7 9 0 2 3 5 5 5 5 5 5 5 6 6 6 6 6 6 7 7 7 7 7 8 8 8 8 8 8 9 9 9 9 9 9 0 0 0 5 5 6 6 6 7 7 7 0 1 1 1 1 1 1 1 2 2 2 2 , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 Hullámhossz (mikrométer) Fig 4. Average spectra of young beech (fiatalos bükk), beech (bükk), oak (tölgy) and pine (fenyő). X axis: wavelenght, Y axis: reflectance

10. I used matched filtering (partial unmixing) for differentiation of various land cover types (Fig 5).

Fig. 5. (from left): Artificial, Oak, Grass, Beech, Pine category maps after partial unmixing (Matched filtering) (bright: good fitting with target spectrum; dark: bad fitting with target spectrum). Upper right: city of Recsk, upper left: Parádfürdő

11. The field spectra contains several bands that can not be used due to their high degree of noise even after filtering. I have made the noise reduction and found the bad bands in the case of the DAIS spectrometer, thus after this work I used only 52 of the 79 DAIS bands. 12. I have prepared a multi-step pathway for processing and analysis of hyperspectral images for ENVI that can be used by future workers 13. I have compared a multispectral LANDAT and a hyperspectral image: the classification of the hyperspectral images have not given far better results than a multispectral one, at least with the methods I used. 14. I have concluded that hyperspectral image analysis is only usable economically if the task is to diffrentiate between land cover types that can not be differentiated using existing multispectral images. Such work, if used regulary, must be preceeded by the definition of a reference spectral library. Since a hyperspectral image have high amount of redundancy, data quantity must be reduced. This can be done automatically (e.g. in a PCA method) or manually. In the later case those bands should be kept, which are displaying a characteristic and unique absorption feature, or it can also be the band of the red edge area in the spectrum. Combining these bands, surface cover classes can be defined. Those indicative bands should be selected in which spectra are most different between the target spectra. Spectral curves have different features that reflect different aspects of the ground truth: the curve, absorption bands, abolute reflectance values. For proper classification, the appropriate spectral feature must be used for revealing the appropriate ground truth feature.

7 Oak and beech spectra in Present work reflects that on 2002 August, in the area of Recsk- Parádfüred-Domoszlói kapu, spectra derived from regions of interest with oak and beech ground truth, can be differentiated. But concerning the exact characteristics of these spectra (why were there different at that point) requires future multitemporal and multispatial work.

4. References of this work: HARGITAI H.-VEKERDY ZOLTÁN-ULANBEK TURDUKULOV-KARDEVÁN PÉTER: Képalkotó spek- trométeres távérzékelési kísérlet Magyarországon. Térinformatika, 2004/6. pp12-15

KARDEVÁN P.–VEKERDY Z.–RÓTH L.–SOMMER ST.–KEMPER TH.–JORDAN GY.–TAMÁS, J.

PECHMANN I.–KOVÁCS E.–HARGITAI H.–LÁSZLÓ F. 2003: Outline of scientific aims and data processing status of the first Hungarian hyperspectral data acquisition flight campaign, 3rd EARSeL Workshop on Imaging Spectroscopy in Oberpfaffenhofen, 2003. május 13-16.

KARDEVÁN P.–FÜGEDI U.–STEFAN S.–TAMÁS J.–GRUIZ K.–THOMAS K.–JORDÁN GY.–RÓTH L.–

HARGITAI H.–ZELENKA T. 2003b: Légi hiperspektális távérzékelési módszerek alkalmazása korábbi bányászati tevékenységek környezeti felmérésében In: Proc.Lippay - Ormos - Vas Tudományos Ülés, Szent István Egyetem, Budapest, Budai Karok, November 6-7.

HARGITAI H.–KARDEVÁN P.–HORVÁTH F. 2006: Az első magyarországi képalkotó spektrométeres repülés eredményeinek összegzése. III. Magyar Földrajzi Konferencia, Budapest, 2006. szeptember 6-7. Magyar Tudományos Akadémia

HARGITAI H.–KERESZTURI Á– SIK A. (2005?): A Mars hidrográfiája. Földrajzi Közlemények, (accepted).

HARGITAI H: Az első magyarországi képalkotó spektrométeres repülés (Geodézia és Kartográfia, submitted after revision)

HARGITAI H: Képalkotó spektrometria: új technológia a hazai távérzékelésben (Földrajzi Közlemények, submitted)

HARGITAI H. 2007: Planetary Maps: Visualization and Nomenclature. Cartographica (accepted for publication)

5. Other publications related to this work HARGITAI H. 2002: Hiperspektrális távérzékelés. In: Karátson D. (szerk.): Pannon enciklopédia. Magyarország földje, Kertek 2000, Budapest. 2. kiadás

8 HARGITAI H. 2005: Távérzékelés. in: Hargitai H, Kereszturi Á, Lazányi J: Csillagászat. In: Nagy Világatlasz, 2. kiadás. TOPOGRÁF, Nyír-Karta.

HARGITAI H. 2006: The Realization of ICA Commission Projects on Planetary Cartography. Cartographica 40/4

HARGITAI H. 2006: Szemek a bolygókutatásban – A szivárványon túl. Profiterol, szeptember.

PAUL SCHENK, HENRIK HARGITAI, RONDA WILSON, ALFRED MCEWEN AND PETER THOMAS (2001): The Mountains of Io: Global and geological perspectives from Voyager and Galileo, Journ. Geophys. Res., Vol 106, E12, Pp 33201-33222

HARGITAI H.-KERESZTURI Á. 2002: Javaslat magyar bolygótudományi szaknyelvi norma létrehozására - Geodézia és Kartográfia LIV, 2002/9, pp. 26-32.

HARGITAI H. 2004: Multilingual Planetary Maps for Non-professional Audience: Visualization and nomenclature. INTERCARTO-10, Vlagyivosztok

HARGITAI H.–BÉRCZI SZ. 2004: Planetary Science Outreach: Old methods, new packaging. Education via DVD and Planetary Maps. International Astronautical Congress, Vancouver IAC-04-IAF-4.P4

On the cover: hyperspectral data cube of the Recsk area (Mátra Mts)