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:: Spatial feature extraction from radar imagery. G. Bellavia, J. Elgy

Document type:: Multivolume work

Structure type:: Chapter

99 Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 Spatial feature extraction from radar imagery G.Bellavia Computer Science, Aston University, Birmingham, UK J.Elgy Civil Engineering, Aston University, Birmingham, UK ABSTRACT: It is accepted that the major role of remote sensing as an information source will be in its contribution to geographical information systems. With the advances in remote sensing, images are being created at an increasing rate. The extraction of information from such data is traditionally done manually and is thus costly in both time and money. Therefore techniques need to be developed which automatically extract information from remotely sensed images. This paper considers the extraction of thin line features such as forest rides, dykes and streams from active microwave imagery. Because radar images are coherently created speckle is produced which renders traditional feature extraction methods virtually useless. It is assumed that global techniques such as generalised hough transforms or intelligent graph searching will be more successful than simple local methods. 1 INTRODUCTION This paper considers the extraction of thin line features such as forest rides, dykes and streams from remotely sensed active microwave imagery. In the context of this paper remote sensing is the aquisition of digital images of the Earth from either airborne or satellite sensor systems. Although digital remote sensing started as late as 1972 many satellite systems have been successfully initiated with a rapidly increasing variety of sensor types and specifications. Several problems arise from this rapid advance in remote sensing technology. The efficient use of image data obtained from satellites or aircraft relies on the availability of human expertise and sophisticated computer systems. In particular radar imagery with its virtual continous monitoring capability and high resolution has shown itself to be superior to more conventional imagery for a variety of applications. But to fully utilise the potential information in this form of data, processing techniques which overcome the inherent speckle noise need to be developed. In general, the problem is one of storage and processing of the data, which is not currently met by computers or human effort. Hardy(1985) states that the population of Earth resource satellites will generate something approaching 10 16 bits in 1986. Assuming an average scene size of 4000x4000 pixels and 8 bits/pixel radiometric resolution, a simple calculation shows this data rate to be equivalent to about 200,000 single channel images/day. On common Computer Compatible Tapes (CCT's) which can store approximately 33 million bytes (MB), this data would require about 10^ CCT's/day. This rate signifies an increase of the data volume/sensor/unit land area of more than an order of magnitude over the last ten years and results in large quantities of unused data. To alleviate this waste of data there is a need for the automatic interpretation of remotely sensed images by computer. It is accepted that the major role of remote sensing as an information source will be in its contribution to geographical information systems. This role requires the extraction of semantic information from remotely sensed images and therefore automatic interpretation. The two arguments concerning data rates and GIS bring us to the conclusion that automatic interpretation is needed to fully realise the potential of remote sensing. The automatic interpretation process entails several other processes. One of the first steps is segmentation which aims to partition the image into separate distinct regions. Feature extraction techniques are also used to enable the segmentation. The segmented scene can then undergo a pattern recognition process using world knowledge giving an interpreted scene. Knowledge may also be used at different stages in the interpretive process. For example to assist or iteratively refine feature extraction, (see fia. 1). Real world images can be considered as comprising of three major high level spatial feature types. Feature extraction Pattern fig. 1 different schemes for the automatic interpretation process.

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