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:: 1 Visible and infrared data. Chairman: F. Quiel, Liaison: N J. Mulder

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Per-field classification of a segmented SPOT simulated image. J. H. T. Stakenborg

Document type:: Multivolume work

Structure type:: Chapter

73 Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 Per-field classification of a segmented SPOT simulated image J.H.T.Stakenborg Commission of the European Communities, Joint Research Centre, Ispra Establishment, Italy ABSTRACT: A per-field classification has been applied to a SPOT simulated image of an area in southern France with mainly agricultural landuse. Using edge preserving smoothing, edge extraction, and edge tracking techniques the field boundaries are derived from the panchromatic channel. The segmented image is replaced by a property table in which the spectral signatures, position, and simple form describing factors of all segments are stored. RESUME: Une méthode de classification 'par champs' a été appliquée à une image de simulation SPOT dans une région à prédominance agricole du sud de la France. Les limites de champs ont été déterminées sur le canal panchromatique par des techniques de lissage conservant les contours, puis l'extraction et le suivi de ces contours. On substitue à l'image ainsi segmentée une table d'attributs où sont mémorisées, pour chaque segment, les signatures spectrales, la position et de simples facteurs de forme. 1 INTRODUCTION New satellite platforms provide us with more detailed information due to the higher spectral and spatial resolution. For research of the spectral properties of objects methods will be used which have proven their use in the past, only shrinked to a space with less dimensions. However higher spatial resolution will lead to detection of more objects in a scene. Analysing a satellite image becomes more and more a matter of spatial analysis. Research of the spatial relationship of objects to for instance landuse can not refer to established methods. Not many attempts have been made partly due to the fact that much effort has to be put in co-operation between computer scientists and experts. Only recent commercial software packages are distributed on the common market which contain the required basic processes needed for spatial analysis, therefore opening possibilities for experts to get involved in spatial analysis. When the development of methods for spatial analyses and "reasoning" gets a higher priority in research programs, better results in this field are to be expected. What I have tried in this direction is to design an 'automatic' per-field classification system to analyse the spectral and spatial characteristics in a complex simulated satellite scene and learn from it. Automatic stands for the field boundaries detection by segmentation and is placed in quotation marks because parameters have to be fed to the system which can influence the result considerably. Image classification in this paper must be seen as a display of intermediate results. To learn from the analysis is more important. The image under investigation is a subset from a SPOT simulated image consisting of 231 x 216 pixels in the panchromatic band of the "Les Vans" area of the department of Ardeche in southern France(fig. la). It is part of a least favoured area as defined by the Commission of the European Communities. It is a rather flat area with mainly agricultural landuse and some outcrops of hard rocks surrounded by woodlands. It was previously investigated by Megier (1984) who with the help of digitized field borders tested some parameters for the per-field classification. These fields are used in the following to measure the performance of a classification and to analyse form parameters i.e. a measure for elongatedness, irregularity, and rotation of known fields. The SPOT satellite works well at this time and will produce images from the 'Les Vans' area soon. Probably this article is the last one dealing with simulated images. 2 SEGMENTATION Segmentation of the image is required to obtain a list of polygons describing the fields of landuse. There are many ways to segment an image. The procedure to prepare an image for segmentation proposed here includes an edge preserving smoothing technique. Edge extraction produces an edge-map. This edge-map has to be cleaned before tracking. Edge tracking and labeling is needed for using the segments as individual and separated regions. Fully aware of the existence of better segmentation procedures I would like to propose a filtering method based on the detection of local features. Other methods have been tested. For the near future segmentation by contouring will be developed but at the time it is not ready. 2.1 Edge preserving smoothing The edge preserving smoothing used is described in Stakenborg(1985). A short explanation will be given. Smoothing takes place with a 3 by 3 mask. Thresholding of the differences from the central pixel divides the kernel into a homogeneous and a non-homogeneous part consequently creating a bit pattern. If this bit pattern is found in a set of pre-defined edge patterns the pixel is left alone otherwise only the mean of the pixels under the pattern is taken. This is a slight improvement of the method proposed by Lev et.al.( 1977) . Threshold selection is critical. By defining a threshold range and counting the number of selected patterns from the set for each pixel and each threshold value, the 'ideal' threshold can be determined and lies around the peak of the threshold histogram. Tests have shown that a threshold from 4 to 6 for the Landsat Thematic Mapper channel 3 and threshold 10 for the SPOT simulated data channel 4 work well. Blockdiagrams in figure 2 show the effect of the edge preserving smoothing of the subset in figure la seen from the N-E. Other methods for edge preserving smoothing work

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