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/6: REMOTE SENSING DATA FUSION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: LARGE AREA LAND COVER CLASSIFICATION WITH LANDSAT ETM+ IMAGES BASED ON DECISION TREE Liang ZHAI, Jinping SUN, Huiyong SANG, Gang YANG, Yi JIA

Document type:: Multivolume work

Structure type:: Chapter

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 LARGE AREA LAND COVER CLASSIFICATION WITH LANDSAT ETM+ IMAGES BASED ON DECISION TREE Liang ZHAI, Jinping SUN, Huiyong SANG, Gang YANG, Yi JIA Key Laboratory of Geo-Informatics of NASG, Chinese Academy of Surveying and Mapping, Beijing, China zhailiang@casm.ac.cn Commission VII, WG VII/6: Remote Sensing Data Fusion KEY WORDS: land cover classification, decision tree, C5.0, MLC ABSTRACT: Traditional land classification techniques for large areas that use LANDSAT TM imagery are typically limited to the fixed spatial resolution of the sensors. For modeling habitat characteristics is often difficult when a study area is large and diverse and complete sampling of environmental variables is unrealistic. We also did some researches on this field, in this paper we firstly introduced the decision tree classification based on C5.0, and then introduced the classification workflow. The study results were compared with the Maximum Likelihood Classification result. Victoria of Australia was as the study area, the LANDSAT ETM+ images were used to classify. Experiments show that the decision tree classification method based on C5.0 is better. 1. INTRODUCTION Detailed and accurate land cover data are widely used by various organizations, such as national, regional, local governments and private industries, as well as educational and research organizations because they are the basis for many environmental and socioeconomic applications (Perera and Tsuchiya, 2009; Heinl et al., 2009). The suitability of remote sensing for acquiring land cover data has long been recognised and land cover mapping with using satellite data has received growing attention in the last 20 years., but the process of generating land cover information from satellite data is still far from being standardised or optimised (Foody, 2002; Lu and Weng, 2007; Heinl et al., 2009). Currently, particularly in times of global change, global land cover mapping has drawn much attention to many countries or organization. Till now, there are a number of global land cover products exist, such as IGBP DISCover, the MODIS land cover product, UMD land cover product, Global Land Cover 2000 (GLC2000, Bartholomé & Belward, 2005) and GLOBCOVER (Loveland et al., 2000; Friedl et al., 2002; Hansen et al., 2000; Herold et al 2008). These maps have been developed in response to the need for information about land cover and land cover dynamics. They all have been produced from optical, moderate resolution remote sensing and thematically focused on characterizing the different vegetation types worldwide (Herold et al 2008).Large area land cover classification still has many difficulties. There are three key problems of classification: 1) given a set of example records, 2) build an accurate model for each class based on the set of attributes, 3) use the model to classify future data for which the class labels are unknown. Common classification models are: neural networks, statistical models, Decision tree and genetic models. Decision tree has many great advantages in the remote sensing classification, which has been successfully used in many situations. Liu Zhongyang adopted the decision tree classification method based on LANDSAT TM image to present coverage situation of Zhengzhou city and proved that the decision tree classification method has obvious advantages, such as exact classification, efficient, definite classification criterion, intuitive classification structure controllable classification precision automated classification, etc (Liu Zhongyang, 2010). There are also many researches on C5.0, for example, this algorithm used in NLCD 2000 to do land cover classification (Homer, Collin, 2000), to estimate tree canopy density (Chengquan Huang, 2000), and so on, these applications all got good results. This paper introduced the C5.0 algorithm, and provided a C5.0 land cover classification platform, this platform also been used to classify the images of Victoria. Further, the classification result was been compared with MLC classification result, it proved that the C5.0 classification method is excellent. 1.1 Study Area and Data We chose Victoria of Australia as our study Area. Victoria located in southeast of Australia, its location in Australia is as shown in figure i. Victoria is the smallest mainland state, and Australia’s second city of --- Melbourne is located in this state. Victoria's climate contains Mediterranean climate, temperate maritime climate, and some Savannah climate. The ecoregions that covered Victoria are Murray-Darling woodlands and mallee, Southeast Australia temperate savanna and Southeast Australia temperate forests. Various climate and terrain lead to rich land cover types in Victoria.

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