Remote sensing for resources development and environmental management: Remote sensing for resources development and environmental management

Damen, M. C. J.

Bibliographic data

Multivolume work

Persistent identifier:: 856342815

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856342815

Language:: English

Additional Notes:: Volume 1-3 erschienen von 1986-1988

Editor:: Damen, M. C. J.

Document type:: Multivolume work

Volume

Persistent identifier:: 856343064

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Scope:: XV, 547 Seiten

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856343064

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Damen, M. C. J.

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: 2 Microwave data. Chairman: N. Lannelongue, Liaison: L. Krul

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: The determination of optimum parameters for identification of agricultural crops with airborne SLAR data. P. Binnenkade

Document type:: Multivolume work

Structure type:: Chapter

4 DATA ACQUISITION 5 PREPROCESSING In order to ensure optimum effectiveness of an air borne campaign great attention is paid to internal and external calibration of the radar and choice of flight dates and -geometry. 4.1 Calibration Early in 1985 the Dutch digital SLAR was fitted with an internal calibration mechanism. In short, the procedure may be visualized as follows. A small part of every transmitted pulse is delayed and weakened, then positioned at the beginning of each received backscatter signal. From the amplitude of the recorded calibration pulse the amplitude of the ori ginal, transmitted pulse is calculated. Hence each received and recorded radar return signal may be corrected for variations in the corresponding trans mitted pulse. As an additional measure of validating the calibra tion external corner reflectors are placed in the target area. From the raw received signals the return of the corner reflectors may be calculated and compared with the theoretical reflection from a well-defined corner reflector. It is quite necessary for further processing and classification to start out with a calibrated system as the transmitted power of the X-band SLAR is ex pected to fluctuate with elapsed time. 4.2 Optimization of flight-date and -geometry The choice of the optimum flight days and the deter mination of the flight geometry is essential in planning an airborne campaign. The main purpose is to identify dates and angles whereby the possibility of discriminating various crops is the greatest. When identifying the ROVE ground truth data base it was found in 1983 and 1984 that an airborne SLAR campaign was to be conducted between May and August, as potatoes do not appear above the soil until late May and may be harvested as early as late July in The Netherlands. Furthermore the data base and crop growth parameters showed that winter wheat and potatoes tend to exhibit similar backscatter charac teristics in all months except May and that potatoes plus winter wheat can be discriminated from most other crops in July (Figure 2, 3). Thus, in 1983 and 1984, it was decided to conduct an airborne campaign with the following objectives: - distinguish potatoes from winter wheat in May and - distinguish potatoes from other crops in June/ July. The above objectives could only be achieved with specific viewing angles. Thus an aircraft altitude of 500 meters above ground to obtain 5°-14° and another run at 2700 meters above ground to enable 25°-45° grazing angles were deemed necessary. In 1985 the ROVE group "Crop Identification" chose to adhere more to expected satellite programmes, where steeper angles for satellite simulation are a prerequisite. Again the ROVE data base was consulted and this resulted in a flight altitude of app. 4000 meters in order to obtain grazing angles of 40°-70° for one track and 30°-48° (grazing) for a laterally trans posed track at the same altitude (Fig. 4). The corresponding appropriate flight dates of early June and early July 1985 gave good confidence to be able to adequately discriminate between the various crop types. In order to enable steep grazing angles the SLAR antenna had to be rotated by almost' 20° in order to make use of the linear part of the antenna gain function [Ref. 6, 7]. To overlay and register data from multitemporal air borne sensors may be very difficult due to random variations for attitude, velocity and position of the aircraft during the execution of the flight track. These deviations usually distort the resul ting data set sufficiently to disable further multi temporal processing. Also, several effects influence the signal received by the radar antenna; e.g. the antenna gain is a function of the depression angle, the received signal strength is dependent on slant range distance. Hence, the National Aero space Laboratory NLR in close collaboration with the Physics and Electronics Laboratory TNO, the Delft University of Technology and the Survey Dept, of Rijkswaterstaat, undertook to implement a preprocessing procedure (PARES) on the central computer of NLR. PARES enables raw acquired data to be corrected for variations in aircraft motion and -attitude through simultaneously recorded INS parameters in the NLR laboratory aircraft (INS = Inertial Navigation System). For every acquired line its exact position in a terrestrial coordinate system is calculated, thus enabling later multitemporal registration of data via an earth-bound coordinate system. The PARES-procedure also accounts for radiometric system corrections. The data preprocessed through PARES may now be multitemporally processed without further complica tions due to airborne data acquisition [Ref. 2, 3]. 6 PROCESSING AND CLASSIFICATION The inherent speckle of a radar image is a distur bing factor in further processing and classifi cation. Also, most advanced classification algo rithms and user-defined information extraction pro grammes do not require information on a pixel-by- pixel basis but rather on a field-by-field basis. Many microwave remote sensing groups have devised ways to minimize speckle through field averaging. Various filtering algorithms have been tried but The Netherlands ROVE-group has chosen for the "split- and-merge" algorithm based on the Pavlidis-concept which has been implemented on the image processing system RESEDA at the National Aerospace Laboratory NLR [Ref. 5]. The algorithm has been designed to process (in parallel) up to 7 multitemporal images with a number of user-identified parameters like minimum field size and variance to obtain field averages where original pixel-values have been replaced by a field- dependent mean value without loss of information. Care must be taken to exclude fields which contain only a few pixels as they are likely to contaminate the segmentation result. Other small objects like power-line masts, farms, etc. will also corrupt the resulting data set [Ref. 4, 5]. After this extensive (pre-)processing the final classification procedure can be comperatively simple. An interactive parallelepiped classifier may be applied to the data set. The classifier allows for interactive classification from data space into feature space and vice versa. It will be obvious that this procedure is usually very time-consuming when applied to standard imagery. The preceeding segmentation algorithm has, however, already greatly reduced the dynamic range of possible pixel values, so now near real-time classification is possible. Great care must be taken, to prevent the angular dependency of backscatter of agricultural crops to be introduced into the classification result. One way to eliminate this effect is to process suffi ciently narrow strips where the angular dependence may be neglected.

Access restriction

Copyright

fullscreen: Remote sensing for resources development and environmental management (Volume 1)

Multivolume work

Volume

Chapter

Chapter

Table of contents

Cite and reuse

Volume

Chapter

Image

Image fragment

Citation links

Citation recommendation