XXII ISPRS Congress 2012: Technical Commission VII

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

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

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; 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:: [VII/4: METHODS FOR LAND COVER CLASSIFICATION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: POST-CLASSIFICATION APPROACH BASED ON GEOSTATISTICS TO REMOTE SENSING IMAGES : SPECTRAL AND SPATIAL INFORMATION FUSION N. Yao, J. X. Zhang, Z. J. Lin, C. F. Ren

Document type:: Multivolume work

Structure type:: Chapter

f T — (0 A International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia POST-CLASSIFICATION APPROACH BASED ON GEOSTATISTICS TO REMOTE SENSING IMAGES : SPECTRAL AND SPATIAL INFORMATION FUSION N. Yao**, J. X. Zhang*, Z. J. Lin®, C. F. Ren? * Remote Sensing information Engineering School, Wuhan University, 129 Luoyu Road, Wuhan, China nayao@foxmail.com; jxzhang@whu.edu.cn; ren_cf@163.com ® Chinese Academy of Surveying and Mapping, 16 Beitaiping Road, Beijing, China - lincasm@casm.ac.cn KEY WORDS: Classification, Information, Fusion, Spectral, Spatial, Geostatistics, Kriging ABSTRACT: Classification of remote sensing imagery provides an inexpensive yet efficient approach to land cover mapping. In supervised image classification, training samples are collected through certain sampling schemes, which are used to derive classification rules, aiming for adequate accuracy for the applications at hand. However, in conventional classification methods, the potential of training samples in terms of locational information is not tapped further, confounding the classification accuracy to the limited separability inherent to the given input feature vector. This paper explores two methods pertaining to geostatistics, i.e., simple kriging with local mean and cokriging, to predict class occurrences based on training samples’ indicator transforms (location and classes) and spectrally derived class probabilities, thus calibrating the a posterior class probability vectors derived from initial spectral classification. The results showed that classification accuracy is significantly increased by these two methods for utilizing spatial information contained in training samples and initial spectral classification, compared with those obtainable with spectral classification. Moreover, the proposed methods constitute a valuable strategy for making fuller use of information residing in training data for improving spectrally derived classification, which is independent of the specific classifiers initially adopted for image classification. 1. INTRODUCTION Land cover, as a spatial factor impacting and linking human life and natural environment, refers to the observed (bio)physical cover on the earth's surface (FAO, 2000). Remote sensing is an attractive data source for land cover mapping. Although remote sensing has been used successfully in mapping a range of land covers at a variety of spatial and temporal scales, the land cover maps derived are often judged to be of insufficient quality for operational applications (Foody, 2002). Therefore, how to improve the quality of land cover maps has been a hot issue all the time. Thematic mapping, exemplified by land cover mapping, from remotely sensed data is typically based on an image classification (Foody, 2002). Hence, the performance of a certain classifier would become a key factor which impacts on the classification accuracy and further impacts on the quality of the derived maps. An operating classifier can be considered as a system that reduces the initial uncertainty by consuming the information contained in the input vector (Battiti, 1995). Battiti further indicates that the final uncertainty will be zero in the ideal case (i.e., the class will be certain), while it can be higher in the actual applications for at least two different reasons, i.e., insufficient input information or suboptimal operation. Hence, the performance of a classifier is one possibility that relates to suboptimal operation. That is, even if sufficient input information is given, classification accuracy quantified by the confusion matrix may be lower than its potential value due to the insufficient training of the classifier. On the other hand, the information loss during the training period manifests a conceivable promotion space of classification accuracy. * Corresponding author. Therefore, if a strategy is capable of compensating or reducing the information loss, an accuracy promotion of classification would be foreseeable. Traditional spectral classification of remotely sensed images applied on a pixel-by-pixel basis ignores the potentially useful spatial information between the values of proximate pixels (Atkinson, 2000; Zhang, 2009). Geostatistical approaches, adopted in this paper, indeed aim to employ the spatial information inherent in remotely sensed images to enhance the spectral classification. Spatial information mainly serves two purposes that derive texture “wavebands” for subsequent use in classification or smooth the imagery prior to or after classification (Atkinson, 2000). This paper utilizes two kriging approaches, i.e., simple kriging with local and cokriging, to fusion the input (e.g., spectral ) and spatial information. In the following sections, first the principles of the two kriging paradigms are revisited, then an index which assesses the potential separability of a data set is introduced, and finally the experimental results are presented and analyzed. 2. METHOD Spectral response in each waveband of a remotely sensed image may be treated as a continuous variable, which is also defined as a regional variable in geostatistics. The variogram (or covariance function) is a quantitative model which reflects the relationship and spatial structure of the regional variables.

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