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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Object Based Image Analysis and Data Mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas

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Author(s):
Vieira, Matheus Alves [1] ; Formaggio, Antonio Roberto [1] ; Renno, Camilo Daleles [1] ; Atzberger, Clement [2] ; Aguiar, Daniel Alves [1] ; Mello, Marcio Pupin [1]
Total Authors: 6
Affiliation:
[1] Natl Inst Space Res INPE, BR-12227010 Sao Jose Dos Campos, SP - Brazil
[2] Univ Nat Resources & Life Sci BOKU, Inst Surveying Remote Sensing & Land Informat IVF, A-1190 Vienna - Austria
Total Affiliations: 2
Document type: Journal article
Source: REMOTE SENSING OF ENVIRONMENT; v. 123, p. 553-562, AUG 2012.
Web of Science Citations: 127
Abstract

The aim of this research was to develop a methodology for contributing in the automation of sugarcane mapping over large areas, with time-series of remotely sensed imagery. To this end, two major techniques were combined: Object Based Image Analysis (OBIA) and Data Mining (DM). OBIA was used to represent the knowledge needed to map sugarcane, whereas DM was applied to generate the knowledge model. To derive the image objects, the segmentation algorithm implemented in Definiens Developer (R) was used. The data mining algorithm used was J48, which generates decision trees (DT) from a previously prepared training set. The study area comprises three municipalities located in the northwest of Sao Paulo state, all of which are good representatives of the agricultural conditions in the Southern and Southeastern regions of Brazil. A time series of Landsat TM and ETM+ images was acquired in order to represent the wide range of pattern variation along the sugarcane crop cycle. After training, the DT was applied to the Landsat time series, thus generating the desired thematic map with sugarcane ready to harvest. Classification accuracy was calculated over a set of 500 points not previously used during the training stage. Using error matrix analysis and Kappa statistics, tests for statistical significance were derived. The statistics indicated that the classification achieved an overall accuracy of 94% and a Kappa coefficient of 0.87. Results show that the combination of OBIA and DM techniques is very efficient and promising for the sugarcane classification process. (C) 2012 Elsevier Inc. All rights reserved. (AU)

FAPESP's process: 09/02037-3 - Agricultural crops mapping from medium spatial resolution data using object-based analysis
Grantee:Antonio Roberto Formaggio
Support Opportunities: Regular Research Grants