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Object-based Land Cover Classification with Orthophoto and LIDAR Data
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Image classification based on remotely sensed data is the primary domain of automatic mapping research. With the increasing of urban development, keeping geographic database updating is imminently needed. Automatic mapping of land cover types in urban area is one of the most challenging problems in remote sensing. Traditional database updating is time consuming and costly. It has usually been performed by manual observation and visual interpretation, In order to improve the efficiency as well as the accuracy, new technique in the data collection and extraction becomes increasingly necessary. This paper studied an object-based decision tree classification based on orthophoto and lidar data, both alone and integrated. Four land cover types i.e. Forest, Water, Openland as well as Building were successfully extracted. Promising results were obtained with the 89.2% accuracy of orthophoto based classification and 88.6% accuracy of lidar data based classification. Both lidar data and orthophoto showed enough capacity to classify general land cover types alone. Meanwhile, the combination of orthophoto and lidar data demonstrated a prominent classification results with 95.2% accuracy. The results of integrated data revealed a very high agreement. Comparing the process of using orthophoto or lidar data alone, it reduced the complexity of land cover type discrimination. In addition, another classification algorithm, support vector machines (SVM) classification was preformed. Comparing to the decision tree classification, it obtained the same accuracy level as decision tree classification in orthophoto dataset (89.2%) and integration dataset (97.3%). However, the SVM results of lidar dataset was not satisfactory. Its overall accuracy only reached 77.1%. In brief, object-based land cover classification demonstrated its effectiveness in land cover map generation. It could exploit spectral and spatial features from input data efficiently and classifying image with high accuracy. 

Place, publisher, year, edition, pages
2015. , 75 p.
Series
TRITA-GIT EX, 15-001
Keyword [en]
Object-based; Land cover classification; Orthophoto; LIDAR
National Category
Remote Sensing
Identifiers
URN: urn:nbn:se:kth:diva-160134ISRN: KTH/GIT/EX--15/001-SEOAI: oai:DiVA.org:kth-160134DiVA: diva2:788855
External cooperation
Lantmäteriet
Subject / course
Geoinformatics
Educational program
Degree of Master - Geodesy and Geoinformatics
Presentation
2014-06-24, seminar room 3074, Drottning Kristinas väg 30, KTH Campus., Stockholm, 13:30 (English)
Supervisors
Examiners
Available from: 2015-02-20 Created: 2015-02-16 Last updated: 2015-02-20Bibliographically approved

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