Comparison of performance of object-based image analysis techniques available in open source software (Spring and Orfeo Toolbox/Monteverdi) considering very high spatial resolution data

• Type: Article in International Scientific Journal
• Date: 2016

Authors

Ana Teodoro (Author) FCUP; Araujo, R (Author) Other

Journal

Title: Journal of Applied Remote Sensing

Vol. 10

Final page: 016011

ISSN: 1931-3195

Publisher: SPIE

Indexing

ISI Web of Knowledge - 42 Citations

Scopus - 46 Citations

Other information

Authenticus ID: P-00K-7ET

DOI: 10.1117/1.jrs.10.016011

Abstract (EN): The use of unmanned aerial vehicles (UAVs) for remote sensing applications is becoming more frequent. However, this type of information can result in several software problems related to the huge amount of data available. Object-based image analysis (OBIA) has proven to be superior to pixel-based analysis for very high-resolution images. The main objective of this work was to explore the potentialities of the OBIA methods available in two different open source software applications, Spring and OTB/Monteverdi, in order to generate an urban land cover map. An orthomosaic derived from UAVs was considered, 10 different regions of interest were selected, and two different approaches were followed. The first one (Spring) uses the region growing segmentation algorithm followed by the Bhattacharya classifier. The second approach (OTB/Monteverdi) uses the mean shift segmentation algorithm followed by the support vector machine (SVM) classifier. Two strategies were followed: four classes were considered using Spring and thereafter seven classes were considered for OTB/Monteverdi. The SVM classifier produces slightly better results and presents a shorter processing time. However, the poor spectral resolution of the data (only RGB bands) is an important factor that limits the performance of the classifiers applied. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 22