Proceedings, XXth congress: Proceedings, XXth congress

Altan, M. Orhan

Bibliographic data

Multivolume work

Persistent identifier:: 1663674213

Title:: Proceedings, XXth congress

Sub title:: Istanbul, 12 - 23 July 2004

Year of publication:: 2004

Place of publication:: Istanbul

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 1663674213

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist aus dem Copyrightjahr ermittelt.; Auch bezeichnet als XXth International Congress for Photogrammetry and Remote Sensing

Editor:: Altan, M. Orhan

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Document type:: Multivolume work

Volume

Persistent identifier:: 166368779X

Title:: Proceedings, XXth congress

Scope:: IV, 226 Seiten

Year of publication:: 2004

Place of publication:: Istanbul

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 166368779X

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(35,B8)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist aus dem Copyrightjahr ermittelt.

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

Editor:: Altan, M. Orhan

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: IMPROVEMENT OF IMAGE CLASSIFICATION WITH THE INTEGRATION OF TOPOGRAPHICAL DATA Deniz Gerçek

Document type:: Multivolume work

Structure type:: Chapter

bul 2004 19-226 tries. In: ) Signal Robust es. In: o Signal daptive, ematical g, Paris, Beijing, IMPROVEMENT OF IMAGE CLASSIFICATION WITH THE INTEGRATION OF TOPOGRAPHICAL DATA Deniz Gerçek Geodetic and Geographic Information Technologies, METU 06531 Ankara, Turkey denizger@metu.edu.tr KEY WORDS: Remote Sensing, Classification, Land cover, Integration, Accuracy. ABSTRACT: Remotely sensed data are essentially used for land cover and vegetation classification. However, classes of interest are often imperfectly separable in the feature space provided by the spectral data. One of the most common attempts to improve image classification is the integration of ancillary data into classification. In this study, an approach for integrating topographic data into land cover classification is presented. Integration is basically through selection of training set in order to provide additional sensitivity to topographical characteristics associated with each land cover class in the study area. Topographic data including elevation, slope and aspect are tested for their correlation with land cover classes and correlated topographic data are used as input. Signatures from topographical data are assumed to represent the topographical preferences of land cover classes and are extracted with respect to the spatial position of spectral signatures from the remotely sensed images. Initial set of topographical signatures is evaluated and refined statistically. A new training set covering both spectral and the topographical signatures is created. New training set is used to supervise the standard Maximum Likelihood classification where; topographical raster data together with images is used as input for the classification. Two products are derived. First product used remotely sensed data only as input and is trained by spectral information. Second product used bands and topographical data as input and it is trained with both spectral and topographical information. Comparison between two products conveyed that procedure provided an improvement of 10% in overall accuracy for the classification with the integration of topographical data over the one that depended on spectral data only. INTRODUCTION Use of ancillary data has long been acknowledged as a necessity obvious approach, Logical channel method introduced by Strahler in remotely sensed image classification especially when et al. (1978), aims to increase the number of attributes or channels discriminating between different types of information classes is of information used in the classification. The second is classifier difficult due to low spectral seperability. Image classification modification, which involves changing a priori probabilities converts image data into thematic information by categorizing according to areal composition of the expected product based on spectral data into classes with respect to statistical decision rules image statistics, ancillary data or a known relationship between introduced by classifier algorithm. However, information gathered classes and ancillary data (Harris and Ventura, 1995; Mesev, by the classification of remotely sensed data, based solely on 1998). spectral variability is often insufficient in accuracy (Janssen et al., 1990; Bruzzone et al., 1997). Accuracy of image classification can be improved with the integration of data and/or information other than the imagery (Westmoreland and Stow, 1992; Gahegan and Flack, 1996). The data and information, also known as “ancillary data” in the literature, are often composed of map-based thematic data, terrain data and non-spatial data. There have been numerous attempts to increase overall accuracy of classification during the period regarding the use of automated classification systems. Hutchinson (1982) categorized these attempts into three according to their proceeding before, during or after classification. Logical channel approach is advantageous for being simple and time saving compared to others. However without any modification or adjustment of conventional sampling routines before class statistics generation, method has obvious limitations. Logical channel approach covering simple addition of ancillary data as input into classification intuitively lacks the ability to handle data of different form and ranges. These limitations may cause problems in generating class statistics, furthermore; training samples selected conventionally based on spectral signatures can not sufficiently represent class properties associated with ancillary data. Integration before classification so called stratification involves Eiumnoh and Shresta (1997) attempted to explore the effect of segmentation of the image into smaller scenes before Digital Terrain Model in accuracy of image classification by classification takes place in order to provide spectrally similar simply adding it as a component into classification in a logical classes to be classified independently. Integration after channel manner, they achieved certain amount of improvement. A classification or post-classification sorting is based on the problem study by Richetti (2000) involves the use of slope map to add that a single class of objects may be assigned to more than one information to classification for geological purposes. Logical classes due to the fact that a particular class can show different channel and stratification methods were applied and compared to spectral characteristics. Integration of ancillary data during spectral classification. Results demonstrated increase in accuracy classification mainly has two approaches; first and the most for logical channel method. 53

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