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

Damen, M. C. J.

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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:: Identifying agricultural crops in radar images. P. Hoogeboom

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Structure type:: Chapter

20 24 IN DB feature. Peaks ion and :a from fig. 2 ¡plit and merge :d. This brings lse the field .n the manual r a small effect, i interpretation lows these ;d for optimizing >6-8 represent )t be considered C L A S S I F I E R L A В E L 1 2 3 4 5 6 7 8 T 1 33 1 1 R 2 36 U 3 53 E 4 1 1 5 1 5 2 1 1 1 L 6 3 1 A 7 3 В 8 1 4 E 9 3 1 1 L 10 1 Table 2. Classification result after automatic field segmentation of the testarea (see table 1 for legend of labels). REFERENCES Hoogeboom, P. 1983. Classification of Agricultural Crops in Radar Images. IEEE Trans GRS Vol GE-21, p. 329-336. Hoogeboom, P.; Binnenkade, P. and Veugen, L.M.M. 1984. An algorithm for radiometric and geometric correction of digital SLAR data. IEEE Trans GRS Vol GE-22, p. 570-576. Smit, M.K. 1979. Preliminary results of an investi gation into the potential of applying X-band SLR- images for croptype inventory purposes. IEEE Trans Geosci Electron Vol GE-17, p. 303-308. Loor, G.P. de; Hoogeboom, P. and Attema, E.P.W. 1982. The Dutch ROVE Program. IEEE Trans GRS Vol GE-19, p. 3-7. Gerbrands, J.J.; Backer, E. 1983. Segmentation of multitemporal side-looking airborne radar (SLAR) images. Proceedings SPIE volume 397, p. 173-179. (Geneva, April 19-22) Applications of digital image processing. Gerbrands, J.J. 1982. Introduction to digital image processing (in Dutch), lecture notes L73A, Delft University of Technology, November. Leeuwen, P.J. van, 1984. Analysis of SLAR images (MSC thesis, in Dutch), Delft University of Tech nology, Information Theory group, July. Class 8 (beans) is not planted until July, so in July these fields are still almost bare, and therefore easy to recognize (see fig. 2). Classes 9 and 10 are not considered in the classifier and therefore identified as other croptypes. 3 CONCLUSIONS In this paper a follow-on study into the possibilities of crop identification was presented. The goal was to improve the classification result from a previous study by adding early season SLAR flights and to enable crop identification as early as possible in the growing season. A hierarchic classification procedure is proposed. The success of this classifier is based on the separability of winterwheat or rather wintercrops at low grazing angles (5° - 15°) in the early growing season (April, May) and the ability to discriminate other croptypes in the mid-season on basis of their angular dependence in the grazing angle range 5° - 35°. Field averaged radar backscatter values are used. The test of the classifier was performed on the same dataset as was used for the design of the classifier, although for the test the fields were segmented in a different way (automatic instead of manual). Care should be exercised in the interpretation of the test results, since the success percentages may be over estimated in this situation. Further investigation should incorporate a test in ecologically different areas and areas with different and more varied crop distributions. Also the use of angular dependence should be further investigated. In the Netherlands a research project is running to cover these subjects. 4 ACKNOWLEDGEMENT This study has been perfprmed by members of the ROVE- team. Part of it was financed by the National Remote Sensing Steering Committee (BCRS). ' Contributions to the work at the Physics and Electronics Laboratory TN0 The Hague came from: National Aerospace Laboratory NLR in Amsterdam, image collection, Centre for agrobiological Research (CABO) in Wageningen, ground data collection, Information Theory Group of the Delft University of Technology, classification. Mr. R. Vlaardingerbroek of the Physics and Electronics Laboratory TNO is credited for the many calculations and plots he produced on the subject.

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