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Developments in Landsat Land Cover Classification Methods: A Review

New Zealand School of Forestry, University of Canterbury, Christchurch 8140, New Zealand
Author to whom correspondence should be addressed.
Received: 1 August 2017 / Revised: 1 September 2017 / Accepted: 13 September 2017 / Published: 19 September 2017
Land cover classification of Landsat images is one of the most important applications developed from Earth observation satellites. The last four decades were marked by different developments in land cover classification methods of Landsat images. This paper reviews the developments in land cover classification methods for Landsat images from the 1970s to date and highlights key ways to optimize analysis of Landsat images in order to attain the desired results. This review suggests that the development of land cover classification methods grew alongside the launches of a new series of Landsat sensors and advancements in computer science. Most classification methods were initially developed in the 1970s and 1980s; however, many advancements in specific classifiers and algorithms have occurred in the last decade. The first methods of land cover classification to be applied to Landsat images were visual analyses in the early 1970s, followed by unsupervised and supervised pixel-based classification methods using maximum likelihood, K-means and Iterative Self-Organizing Data Analysis Technique (ISODAT) classifiers. After 1980, other methods such as sub-pixel, knowledge-based, contextual-based, object-based image analysis (OBIA) and hybrid approaches became common in land cover classification. Attaining the best classification results with Landsat images demands particular attention to the specifications of each classification method such as selecting the right training samples, choosing the appropriate segmentation scale for OBIA, pre-processing calibration, choosing the right classifier and using suitable Landsat images. All these classification methods applied on Landsat images have strengths and limitations. Most studies have reported the superior performance of OBIA on different landscapes such as agricultural areas, forests, urban settlements and wetlands; however, OBIA has challenges such as selecting the optimal segmentation scale, which can result in over or under segmentation, and the low spatial resolution of Landsat images. Other classification methods have the potential to produce accurate classification results when appropriate procedures are followed. More research is needed on the application of hybrid classifiers as they are considered more complex methods for land cover classification. View Full-Text
Keywords: Landsat; land cover; classification methods; remote sensing; OBIA; pixel-based Landsat; land cover; classification methods; remote sensing; OBIA; pixel-based
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MDPI and ACS Style

Phiri, D.; Morgenroth, J. Developments in Landsat Land Cover Classification Methods: A Review. Remote Sens. 2017, 9, 967.

AMA Style

Phiri D, Morgenroth J. Developments in Landsat Land Cover Classification Methods: A Review. Remote Sensing. 2017; 9(9):967.

Chicago/Turabian Style

Phiri, Darius, and Justin Morgenroth. 2017. "Developments in Landsat Land Cover Classification Methods: A Review" Remote Sensing 9, no. 9: 967.

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