+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery



A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery



Remote Sensing of Environment 118(none): 0-272



Pixel-based and object-based image analysis approaches for classifying broad land cover classes over agricultural landscapes are compared using three supervised machine learning algorithms: decision tree (DT), random forest (RF), and the support vector machine (SVM). Overall classification accuracies between pixel-based and object-based classifications were not statistically significant (p > .5) when the same machine learning algorithms were applied. Using object-based image analysis, there was a statistically significant difference in classification accuracy between maps produced using the DT algorithm compared to maps produced using either RF (p = .116) or SVM algorithms (p = .67). Using pixel-based image analysis, there was no statistically significant difference (p > .5) between results produced using different classification algorithms. Classifications based on RF and SVM algorithms provided a more visually adequate depiction of wetland, riparian, and crop land cover types when compared to DT based classifications, using either object-based or pixel-based image analysis. In this study, pixel-based classifications utilized fewer variables (15 vs. 3), achieved similar classification accuracies, and required less time to produce than object-based classifications. Object-based classifications produced a visually appealing generalized appearance of land cover classes. Based exclusively on overall accuracy reports, there was no advantage to preferring one image analysis approach over another for the purposes of mapping broad land cover types in agricultural environments using medium spatial resolution earth observation imagery.Pixel-based (PB) and object-based (OB) classifications are compared. Three machine learning algorithms (MLAs) are examined. No statistical difference between PB and OB classifications was found. For OB classifications, significant differences between MLAs were found..

Please choose payment method:






(PDF emailed within 0-6 h: $19.90)

Accession: 036299222

Download citation: RISBibTeXText

DOI: 10.1016/j.rse.2011.11.020


Related references

Object Features for Pixel-based Classi cation of Urban Areas Comparing Different Machine Learning Algorithms. Photogrammetrie - Fernerkundung - Geoinformation 2013(3): 149-161, 2013

A comparison of pixel-based decision tree and object-based Support Vector Machine methods for land-cover classification based on aerial images and airborne lidar data. International Journal of Remote Sensing 38(23): 7176-7195, 2017

Well site extraction from Landsat-5 TM imagery using an object- and pixel-based image analysis method. International Journal of Remote Sensing 35(23): 7941-7958, 2014

A comparative assessment between object and pixel-based classification approaches for land use/land cover mapping using SPOT 5 imagery. Geocarto International 29(4): 351-369, 2014

Evaluating pixel and object based image classification techniques for mapping plant invasions from UAV derived aerial imagery: Harrisia pomanensis as a case study. Isprs Journal of Photogrammetry and Remote Sensing 129: 1-11, 2017

Mapping trees outside forests using high-resolution aerial imagery: a comparison of pixel- and object-based classification approaches. Environmental Monitoring and Assessment 185(8): 6261-6275, 2013

Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. International Journal of Remote Sensing 25(24): 5655-5668, 2004

Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms. Isprs Journal of Photogrammetry and Remote Sensing 128: 86-97, 2017

Mapping seabed sediments: Comparison of manual, geostatistical, object-based image analysis and machine learning approaches. Continental Shelf Research 84: 107-119, 2014

Object-Based Image Classification of Summer Crops with Machine Learning Methods. Remote Sensing 6(6): 5019-5041, 2014

Gully Erosion Mapping Using Object-Based and Pixel-Based Image Classification Methods. Environmental and Engineering Geoscience 21(2): 101-110, 2015

Comparison of geo-object based and pixel-based change detection of riparian environments using high spatial resolution multi-spectral imagery. Pe&rs, Photogrammetric Engineering & Remote Sensing: 2, 123-136, 2010

Comparison of pixel-based and object-oriented image classification approaches - a case study in a coal fire area, Wuda, Inner Mongolia, China. International Journal of Remote Sensing 27(18/20): 4039-4055, 2006

An experiment-based quantitative and comparative analysis of target detection and image classification algorithms for hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing 38(2): 1044-1063, 2000

Mapping irrigated agriculture in complex landscapes using SPOT6 imagery and object-based image analysis - A case study in the Central Rift Valley, Ethiopia. International Journal of Applied Earth Observation and Geoinformation 75: 118-129, 2019