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

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Comparison of classification results of original and preprocessed satellite data. Barbara Kugler & Rüdiger Tauch

Document type:: Multivolume work

Structure type:: Chapter

Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 Comparison of classification results of original and preprocessed satellite data Barbara Kugler & Rüdiger Tauch Department of Photogrammetry and Cartography, Technical University of Berlin, FR Germany ABSTRACT: The production of satellite image maps is one of various objectives of image processing. Satellite image maps are very important tools as orientation maps in regions, where other topographic maps are either not available or out of date. They may also serve as basic information for thematic maps for landuse, geological and ecological purposes. The methods of multispectral classification are applied to obtain landuse maps from satellite image data. These methods are based on the characteristic spectral reflectance of objects that enables to extract feature sets for different classes. By means of the feature sets it is possible to assign every picture element (pixel) to one of the various classes. For digital image classification the spectral signatures are the substantial source of information. Therefore usually roughly preprocessed data are used. The classification of preprocessed data offers advantages if the concerned area is covered by several satellite image scenes. In this case the data undergo radiometrical and geometrical processing before classification takes place. The purpose of this paper is to investigate digitally classified original and preprocessed data. The classification results of the two data types are compared. 1 INTRODUCTION During the last years many remote sensing techniques were developed. The satellite-scanned-data is used as basic information for many applications like the production of image and thematic maps for urban planning, land resource management such as agriculture, etc. All applications require several stages of processing until satisfying results can be obtained. For the production of image maps it is necessary to take regard of radiometric and geometric effects, which are caused by sensors or spacecraft. There fore digital methods are developed, which take account of these disturbances. Radiometric corrections such as contrast enhancement, corrections of pixel-and line-interferences, destriping etc. improve image quality and are prerequisite for further steps of processing. In order to compile maps with high accuracy it is essential to relate the satellite-scanned-data to the ground coordinate system. For a comparison of data from different dates or sensors the geometric relation to each other must be guaranteed. These problems can be solved with rectification and resampling algorithms. For other purposes like landuse or forest damage investi gations multispectral classification is used in order to gain the desired information. The digital methods of image classification are based on the spectral reflectances of objects. In the images, these spectral reflectances are represented through the grey-values in every spectral channel of each pixel. Every kind of radiometric and geometric image processing methods influence these values in a different way, with the consequence that the feature sets for the different classes change. This causes an alteration of classification results. For that reason normal ly roughly preprocessed data is used. But sometimes it is advantageous or even necessary to deal with better prepro cessed data. For a supervised classification a contrast enhancement in every spectral channel makes the inter active definition of training areas easier. The multitempo ral or multisensoral classification assumes already rec tified data. Otherwise it is not possible to assign picture elements, which represent the same ground area, to each other. In the case of the multitemporal data the different dated images do not match together identically, in the case of data from different sensors there are possibly differ ences in the image resolution. All these influences on grey- values caused by preprocessing make it necessary to inves tigate classification results of data, which is preprocessed in different ways. In the following some usual image processing methods are described briefly. After processing the digital data with linear contrast enhancement and/or rectification and resampling with the methods of nearest neighbourhood and bilinear interpolation, the obtained images serve as input material for supervised multispectral classification. The classification results and their comparisons are presented subsequently. 2 CONTRAST ENHANCEMENT Normally satellite-scanned-data are available as digital data on CCT's. The quality of the original data is often very poor. The images are dark and have only low con trasts, which make a visual interpretation difficult. There fore the first step of preprocessing is a contrast enhance ment for every channel separately. The distribution of the grey values of original data only takes a small part of the possible values between 0 and 255. A linear contrast enhancement allows to stretch the values within the whole possible interval. The results are better in contrast and as a whole in the image quality. Other radiometric prepro cessings are often necessary; however our investigations are restricted to the effect of a linear contrast enhance ment. 3 RECTIFICATION AND RESAMPLING For many applications it is necessary to get a relation between the satellite-scanned-data and the map grid. This can be achieved by a rectification and resampling of the data. The rectification can be realized with the help of transformation polynomials, where the coefficients are evaluated by a least squares adjustment. For the compu tation of the grey-values of the new pixels the following resampling algorithms are possible: nearest neighbourhood, bilinear interpolation or cubic convolution. The last two methods determine the new values by considering the surrounding pixels, with the result of artificial grey-values, whereas the first method takes only values, which already exist in the original image. Some investigations (Kahler, Milkus, 1986) show, that a row- and column-doubling of the image before rectification and resampling improves the result, especially the contrast of small linear features. This means that the resolution has been artificially enlarged. One of our case-studies investi gates wether such a data handling will be meaningful for classification of rectified images.

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