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Remote Sens. 2016, 8(8), 691; doi:10.3390/rs8080691

Consistent Classification of Landsat Time Series with an Improved Automatic Adaptive Signature Generalization Algorithm

1
Department of Geography, University of North Carolina, Chapel Hill, NC 27599, USA
2
Curriculum for the Environment and Ecology, University of North Carolina, Chapel Hill, NC 27599, USA
*
Authors to whom correspondence should be addressed.
Academic Editors: Soe W. Myint, Xiaofeng Li and Prasad S. Thenkabail
Received: 27 June 2016 / Revised: 25 July 2016 / Accepted: 15 August 2016 / Published: 24 August 2016
View Full-Text   |   Download PDF [2446 KB, uploaded 24 August 2016]   |  

Abstract

Classifying land cover is perhaps the most common application of remote sensing, yet classification at frequent temporal intervals remains a challenging task due to radiometric differences among scenes, time and budget constraints, and semantic differences among class definitions from different dates. The automatic adaptive signature generalization (AASG) algorithm overcomes many of these limitations by locating stable sites between two images and using them to adapt class spectral signatures from a high-quality reference classification to a new image, which mitigates the impacts of radiometric and phenological differences between images and ensures that class definitions remain consistent between the two classifications. We refined AASG to adapt stable site identification parameters to each individual land cover class, while also incorporating improved input data and a random forest classifier. In the Research Triangle region of North Carolina, our new version of AASG demonstrated an improved ability to update existing land cover classifications compared to the initial version of AASG, particularly for low intensity developed, mixed forest, and woody wetland classes. Topographic indices were particularly important for distinguishing woody wetlands from other forest types, while multi-seasonal imagery contributed to improved classification of water, developed, forest, and hay/pasture classes. These results demonstrate both the flexibility of the AASG algorithm and the potential for using it to produce high-quality land cover classifications that can utilize the entire temporal range of the Landsat archive in an automated fashion while maintaining consistent class definitions through time. View Full-Text
Keywords: Landsat; land cover and land use change (LCLUC); classification; random forest; signature extension; topography; National Land Cover Database (NLCD); time series Landsat; land cover and land use change (LCLUC); classification; random forest; signature extension; topography; National Land Cover Database (NLCD); time series
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Dannenberg, M.P.; Hakkenberg, C.R.; Song, C. Consistent Classification of Landsat Time Series with an Improved Automatic Adaptive Signature Generalization Algorithm. Remote Sens. 2016, 8, 691.

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