Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

baltsavias, emmanuel p.

Bibliographic data

Monograph

Persistent identifier:: 856473650

Author:: Baltsavias, Emmanuel P.

Title:: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

Sub title:: Joint ISPRS/EARSeL Workshop ; 3 - 4 June 1999, Valladolid, Spain

Scope:: III, 209 Seiten

Year of publication:: 1999

Place of publication:: Coventry

Publisher of the original:: RICS Books

Identifier (digital):: 856473650

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

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

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:: Monograph

Collection:: Earth sciences

Chapter

Title:: INTERACTIVE SESSION 1 IMAGE CLASSIFICATION

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: AN AUTOMATED APPROACH FOR TRAINING DATA SELECTION WITHIN AN INTEGRATED GIS AND REMOTE SENSING ENVIRONMENT FOR MONITORING TEMPORAL CHANGES. Ulrich Rhein

Document type:: Monograph

Structure type:: Chapter

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 154 AN AUTOMATED APPROACH FOR TRAINING DATA SELECTION WITHIN AN INTEGRATED GIS AND REMOTE SENSING ENVIRONMENT FOR MONITORING TEMPORAL CHANGES Ulrich Rhein ISPA, University of Vechta, P.O. Box 1553, D-49364 Vechta, Germany, urhein@ispa.uni-vechta.de KEYWORDS: Remote Sensing, GIS, Knowledge-Base, Change Detection, Classification, Training Data. ABSTRACT Environmental monitoring analysis within an integrated GIS and remote sensing environment will be a main requirement for past and future projects. There is a continuously increasing synergy between GIS and remote sensing because there is a growing demand for spatial information, especially of high accuracy and currency. Major developments in the analysis process will be the automated extraction of accurate information from high resolution remote sensing imagery. New techniques, such as the use of knowledge- based systems, hierarchical processing, artificial neural network analysis or combinations of these techniques have shown great potential in a number of environmental monitoring studies. This paper presents the development of a robust classification technique, which is based to a large degree on standard or operational components. Any supervised classification approach needs excellent ground truth data to train the algorithm. It is a well-known fact that training set characteristics have a significant - if not the most significant - effect on the performance of any image classification. Ground truth campaigns over time series, however, can be very time consuming and expensive. The ultimate goal is the automated detection of changes in our environment. Regarding this goal, our 1 strategy is not to detect areas where changes have occurred but rather those without changes. These areas will be stored in a GIS t database and will provide the basis for a hierarchical knowledge-based classification. This project utilises common robust e classification algorithms to discriminate between the major landcover types. An automated GIS process algorithm (buffer, mask, c overlay, etc.) will be used to eliminate or modify prospective training areas with multimodal histograms. The evaluation of the results '<■ in a qualitative and quantitative manner will be part of future work. c t 1. INTRODUCTION We are modifying our environment at unprecedented rates and scales. We can, however, debate the specific spatial dimensions, rates and significance of these changes. Throughout history, technology has always been a key factor facilitating change. Current technology can create environmental changes at previously unknown spatial and temporal scales. Yet, it also offers us the ability to facilitate our investigations leading to a more complete understanding of human impact on our environment. Through appropriate use of technologies, we can move a significant step towards an environmentally sound management of the Earth’s natural resources (Ehlers, 1996). Classification strategies have often been highlighted themes of treatises. Argialas and Harlow (1990) gave an extensive overview of various approaches for image classification and image understanding within remote sensing. Beyond this, automation of image classification tasks is a dream of the image interpretation community. Various good but specific results were provided for example by Huang and Jensen (1997). They presented a machine-learning approach for acquiring knowledge. Another goal for various tasks, ranging from image acquisition to image classification, is their faster execution. The ultimate goal of this project is an automated change detection. 2. DATA The evaluation of robust monitoring analysis methods needs comparably exact databases with information on landuse classes and their respective changes. On the one hand, our approach has c to deal with change detection for agricultural areas. On the I other hand, we deal with changes of natural/agricultural areas s near urban settlements. The latter means that the investigated i areas may be rural/agricultural with embedded settlements. Besides this important fact, accurate training sites and data should be guaranteed. The main data source for our analysis are Landsat-TM scenes. Other satellite remote sensing data are SPOT-Pan and SPOT-XS images. For special test sites, scanned aerial colour infrared (CIR) images are available. These aerial images are used to support the definition of test areas and to check the classification accuracy. Topographic maps at a scale of 1:5000 are used as the geometric basis for mapping ground truth areas. In addition, digital data from the German Authoritative Topographic Cartographic Information System (ATKIS) are used not only for the visualisation of geocoding errors but also for defining ground control points for the rectification process. The use of ATKIS as a masking layer for individual classes is possible in most cases. In this approach, however, this procedure is not applicable for all cases. For example, farmland and pasture areas must have a minimum size of one hectare to be included into the ATKIS database. It is a well-known fact, that any supervised classification needs excellent ground truth data to begin with. It has been argued, that training set characteristics have the most significant effect on the performance of any image classification (Swain and Davis, 1978). Training data should be collected as close as possible to the date of remote sensing data acquisition. Table 1 gives an overview of our current database.

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