Comparing supervised algorithms in Land Use and Land Cover classification of a Landsat time-series

Thayse Nery de Figueiredo, Rohan Sadler, Maria Solis-Aulestia, Ben White, Maksym Polyakov, Morteza Chalak Haghighi

    Research output: Chapter in Book/Conference paperConference paperpeer-review

    14 Citations (Scopus)

    Abstract

    Machine learning algorithms (MLAs) are often applied to identify Land Use and Land Cover (LULC) changes, but typically to only a limited set of imagery. This leaves the consistency of MLAs performance through time poorly understood. The research objective was therefore to compare the performance of six MLAs across a time-series of Landsat imagery (1979, 1992, 2003, 2014), all processed in the same manner. Here Support Vector Machines (SVM), K-Nearest Neighbours (KNN), Random Forests (RF), Learning Vector Quantization (LVQ), Recursive Partitioning, Regression Trees (RPART) and Stochastic Gradient Boosting (GBM) were evaluated. The results demonstrated that SVM achieved higher overall accuracies and kappa coefficients, and a slightly improved fit at individual class level, than the second best classifier RF. Both classifiers clearly outperformed the other algorithms. These results suggest that SVMs (or RFs) should be prioritised when classifying time-series imagery for LULC change detection.

    Original languageEnglish
    Title of host publicationInternational Geoscience and Remote Sensing Symposium (IGARSS)
    EditorsJi Wu, Yaqiu Jin
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages5165-5168
    Number of pages4
    Volume2016-November
    ISBN (Electronic)9781509033324
    DOIs
    Publication statusPublished - 1 Nov 2016
    Event36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Beijing, China
    Duration: 10 Jul 201615 Jul 2016

    Conference

    Conference36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
    Country/TerritoryChina
    CityBeijing
    Period10/07/1615/07/16

    Fingerprint

    Dive into the research topics of 'Comparing supervised algorithms in Land Use and Land Cover classification of a Landsat time-series'. Together they form a unique fingerprint.

    Cite this