International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B6. Istanbul 2004
Persson, A, Holmgren, J., Sôderman, U. Detectiong and
measuring individual trees using an airborne laser scanner.
PERS 68:925-932.
Reese, H., Nilsson, M., Granqvist Pahlén, T., Hagner, O.,
Joyce, S., Tingelóf, U., Egberth, M., and Olsson, H. 2002.
Countrywide estimates of forest variables using satellite data
and field data from the national forest inventory. Ambio
32:542-548.
Wallerman, J. 2003. Remote Sensing Aided Spatial Prediction
of Forest Stem Volume. Acta Universitatis Agriculturae Suecia,
Silvestria 271.
6. CENTRE FOR IMAGE ANALYSIS - UU, SLU
6.1 Organisation
The Centre for Image Analysis (CBA) is a joint university
entity between Uppsala University (UU) and the Swedish
University for Agricultural Sciences (SLU). The main activities
at CBA are graduate education and research. Image analysis is
in its essence interdisciplinary, its foundations being in
mathematics, statistics, physics, and computer science, and its
applications — with respect to CBA — are ranging from shape
analysis of HIV viruses to detection of coral bleaching in
tropical seas.
6.2 Research
Forest inventory from air-borne sensors have been an active and
productive research field in the SLU Forest group since its
beginning in 1994. The aim is to make inventory from such data
so detailed and correct that it can replace field inventories,
except for small investigations to collect ground truth. Earlier
work has been dedicated to development and evaluation of
methods for extracting stand-wise forest parameters from
CARABAS VHF SAR images. Lately, accurate segmentation
methods for tree crowns, and species identification based on
this segmentation, has been developed. The main goal is to be
able to differentiate between spruce and pine. Even though they
have the same spectral signatures, they do have, on the average,
different shapes and internal structure.
There has been several projects at CBA that investigates the
possibility to use satellite data for agricultural analyses. During
2000 and 2001 the aim of these projects, within the SLU
agricultural group, were to develop general methods for
automatic field segmentation in satellite images, which is
important for mapping and for improving classification.
Multispectral edge detection was combined with regions
extraction and shape analysis.
The research of the UU Aquatic Remote Sensing group is
focused on different environmental applications of digital
remote sensing. The present activities vary from mapping and
monitoring of algae blooms and distribution of plumes to
mapping and monitoring of tropical coasts and sea bottoms.
One important area of research is our continued development of
image analysis methods for imaging spectrometry. Much effort
has been put into the procedures for pre-processing of remote
sensing data and the development of bio-optical modelling for
more operational monitoring of water quality from space. The
long-term goal here is using satellite, together with airborne
hyperspectral data, for algae bloom detection, eutrophication,
and pollution in Nordic waters. One aspect of the latter we have
worked on is detection of industrial plumes in lakes and seas.
The aquatic group at CBA also work on the detection of coral
266
bleaching from remote sensing sources. The work includes
sensors like IRS-LISS-III, SPOT, and IKONOS. A new project
in the group is focused on acquisition and colour correction of
underwater multi- or hyperspectral data (e.g., colour photos).
This can be important for many applications, such as marine
biology and underwater archaeology.
CBA also work theoretically on developing techniques for
analysis of hyperspectral image data. An important aspect is
developing linear transformations method, based on such
transforms as the familiar PCA (Principal Component Analysis)
and the more recent ICA (Independent Component Analysis.)
New techniques for information extraction using neuro-fuzzy
systems, i.e., so-called Weighted Neural Networks (WNN) are
also being developed.
6.3 Address
Centre for image analysis
Lägerhyddsvägen 3
SE-752 37 Uppsala, Sweden
Home page: www.cb.uu.se/index_eng.html
6.4 Key publications
Fransson, J.E.S.; Walter, F.; Ulander, L.M.H.; Estimation of
forest parameters using CARABAS-II VHF SAR data . IEEE
Trans. on Geoscience and Remote Sensing.- 2000
e
(38): 2, pp. 720-727.
Rydberg, A., Multispectral Image Analysis for Extraction of
Remotely Sensed Features in Agricultural Fields . 2001. Acta
Universitatis Agriculturae Sueciae. Agraria 296.
Óstlund. C; Flink P; Strómbeck N; Pierson D; Lindell T,
Mapping of the water quality of Lake Erken, Sweden, from
Imaging Spectrometry and Landsat Thematic Mapper. Science
of the total environment. - 2001 (268):1-3 , s. 139-154.
Ammenberg Petra, Flink Peter, Pierson Don, Lindell Tommy
and Strómbeck Niklas, Bio-optical Modelling Combined with
Remote Sensing to Assess Water Quality. International Journal
of Remote Sensing. - 2002 (23) 8, s. 1621-1638.
Brandtberg, Tomas, Individual Tree-based Species
Classification in High Spatial Resolution Aerial Images of
Forests using Fuzzy Sets. Fuzzy Sets and Systems.- 2002
(132):3, 5. 371-387:
Erikson, Mats, Segmentation of individual tree crowns in colour
aerial photographs using region growing supported by fuzzy
rules. Canadian Journal of Forest Research.- 2003
(33):8, s. 1557-1563.
Hamid Muhammed, H.; Larsolle, A., Feature Vector Based
Analysis of Hyperspectral Crop Reflectance Data for
Discrimination and Quantification of Fungal Disease Severity
in Wheat. Biosystems Engineering. - 2003 (86) : 2 , s. 125-134.
Philipson, Petra, Environmental applications of aquatic remote
sensing. 2003. Acta Universitatis Upsaliensis 812.
Ahlén Julia, Sundgren David, Bottom Reflectance Influence on
a Color Correction Algorithm for Underwater Images. 13th
Scandinavian Conference, SCIA 2003 Góteborg, Sweden, June
29-July 2, 2003.-Springer Verlag Berlin Heidelberg New
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