Northern Territory University
Summary | Aims | Key questions | Significance | Progress
| Publications | References | Supervisors
The potential synergy arising from the integration of remote
sensing and GIS is becoming increasingly likely. Given the ongoing
trends of increasing spatial and spectral resolution of remotely
sensed imagery (with increased complexity of analysis) and of the
increased uptake of GIS by a diverse range of users (with increased
demand for data), some degree of integration of RS-GIS is desirable
- It has the potential to facilitate the flow of information from
imagery into GIS
- Remotely sensed imagery can be analysed more quickly (Ahmad,
Optical remote sensing has been in use for over 20 years and
techniques of image classification, rectification and so on are
well established. These are commonly implemented by an experienced
remote-sensing analyst, on a "one-off" basis, for each new image,
and this potentially limits the flow of information to GIS.
A method of automating the processing of remotely sensed imagery
into a suitable form for import into GIS would have clear
advantages. Conversely, the information contained within the GIS
can possibly be made to inform the image processing with the view
to "piggy back" on previous image classification and ground truth
In contrast to optical remote sensing, the techniques of
processing RADAR remote sensing data are largely undeveloped as
yet. Radar images are increasingly attractive for their "year-round
monitoring" capacity, but are not particularly amenable to
"traditional" optical remote sensing data processing techniques.
The complexity of interpreting radar images invites the application
of "knowledge based" or "expert system" classification in which the
spatial context of a pixel is taken into account in the
This is a further arena in which the integration of digital
image processing with the spatial analysis functionality of GIS may
be of use.
Similarly, the image processing methods developed for optical
remote sensing data become increasingly difficult to implement when
applied to the problem of the ever-increasing number of bands in
hyperspectral imagery. The increased demand on the remote sensing
analyst may be ameliorated via automation of the RS-GIS
This project focuses on the integration of GIS and RS to
expedite processing of remote sensing data in the context of
environmental inventory and monitoring. The proposed study area in
general is the NATT with the object of utilising data already
available possibly supplemented (funds permitting) with RadarSat
- To extend RS-GIS integration in the use of GIS to facilitate
remote sensing digital image processing.
- To develop a scheme to enable GIS to be used to determine the
degree of spatial correlation between a classification mask and
cover type spatial distribution. Using this, determine what cover
types correspond to given spectral signatures with the view to
automating routine, repetitive classification and image
rectification, once a reliable base classification and registration
have been established.
- To investigate using GIS as means of implementing knowledge
based classification with the aim of integrating radar with other
remote sensing and non-remote sensing data.
- Is the possible integration of RS and GIS theoretically sound
and does it function correctly with simple artificial test
- Does the proposed integration enable classification of SPOT MSS
images and LandSat TM images to be informed by a GIS coverage of
ground truth data?
- What is the utility of Fourier analysis when applied to spatial
correlation, filtering and pattern recognition, in optical remote
sensing imagery and in Radar remote sensing imagery?
- Does the process of attempting to "piggy back" an image
classification by using a previous classification mask obscure or
highlight real temporal change?
- Can GIS be used to implement knowledge based classification of
optical remotely sensed imagery?
- Can a knowledge based classification scheme developed for
optical remotely sensed imagery be applied successfully to
hyperspectral imagery and to what extent is it extendable to Radar
Automating the processing of images for importing to GIS has
- If it were possible to enter remotely sensed data into GIS in a
routine economical fashion, then frequent updating of GIS data by
remote sensing imagery would improve data currency.
- Automatic image processing to provide a rapid (if not
foolproof) and application specific reconnaissance of an image,
enabling automatic identification of areas sufficiently similar to
known target areas, would extend the usefulness of remotely sensed
images to a wider pool of (possibly non-expert) users.
- The land managers' demand for increasing number of bands (eg.
Hyperspectral imagery) places growing demand on the remote sensing
analyst. A degree of automation through RS-GIS integration may
facilitate the adoption of hyperspectral data.
- The development of methods of processing and interpreting of
radar images is particularly useful for year round monitoring in
the tropics where cloud is endemic for half the year.
- There is a perception that GIS and RS may offer benefits to
indigenous natural resource management (Crerar et al 1998)
- "The interpretation of a remotely sensed image is facilitated
by using a GIS...to determine what if any cover types or physical
parameters correspond to given spectral signatures." (Carter et al
The automated integration of Remote Sensing and GIS is
approached from a DATA FUSION perspective. Data from several
sources (image and non-image) is used in combination (fused) with
the goal of producing output information of higher quality than
obtainable from a single source.
During 2000-1, fusion with GIS data was explored:
- The spatial correspondence between reference GIS data and
spectral classes from an unsupervised image classification were
estimated by GIS overlay and cross-tabulation.
- Cover types were characterised by a texture measure and the
similarity between a pixels immediate spatial neighbourhood and
cover type texture templates is estimated.
- Spatial properties of cover type (such as probability of
adjacency, average area, perimeter ,width) derived from reference
mapping within GIS were exploited in Image Classification.
- The use of Inductive Learning algorithms was commenced to
attempt to select from multiple data set those most discriminating
for the cover type mapping required and to infer possible
- The use of SAR imagery to attempt to map inundated areas and
thus shore lines on flood plains was commenced with a view to
generating effective contours for the generation of a DEM within a
Fegan, M. 2000, 'Beginning an
investigation into the use of fusion of image and non-image data
for the automation of classification of a tropical wetland',
Proceedings of the 10th Australasian Remote Sensing and
Photogrammetry Conference, Adelaide, 21-25 Aug. Fegan, M.,
Devonport, C., & Ahmad, W. 2000, 'Beginning an investigation
into the use of fusion of image and non-image data for the
automation of classification of a tropical wetland', Proceedings of
the 10th Australian Remote Sensing & Photogrammetry Conference
10ARSPC, Adelaide SA, 21-25 Aug.
Fegan, M., Devonport, C., &
Ahmad, W. 2001, 'Automation of image classification by fusion with
GIS data,' Proceedings of the Fifth North Australian remote sensing
and GIS conference, NARGIS 01 Darwin NT, 3-5 July.
Fegan, M., Devonport, C. &
Ahmad, W. 2001, 'Automating image classification, a preliminary
foray using archival GIS data to label pixels', Proceedings of the
IEEE International Geoscience and Remote Sensing Symposium, IGARSS
2001, Sydney NSW, 9-13 July.
Ahmad, W. 1992, "The
use of remotely sensed data in the context of a GIS for monitoring
temporal change in a forested region of Australia," Asian-Pacific
Remote Sensing Journal, 5(1): 133-143.
Carter, J.L., Devonport, C.C., Hill,
G.J.E. (1998) The application of remote sensing and GIS to coastal
habitats in northern Australia.
Crerar, J.M., Hill, G.J.E., Devonport, C.C. (1998) The use of
remote sensing and GIS by indigenous people for natural resource
Dr Dick Williams, CSIRO W&E;
Dr Waqar Ahmad, CDU;
Mr Chris Devonport, CDU