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Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on

Zhen Zhou1, Jingfeng Huang2, Jing Wang1

  • 1Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou, China; Key Laboratory of Agricultural Remote Sensing and Information System, Zhejiang Province, Hangzhou, China.

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|November 4, 2015
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Summary
This summary is machine-generated.

A new remote sensing method accurately maps large sugarcane areas in China, overcoming cloudy weather and crop mixing challenges. This approach uses object-oriented methods and data mining for efficient and feasible crop classification.

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Area of Science:

  • Agricultural remote sensing
  • Geospatial analysis
  • Data mining applications

Background:

  • Sugarcane cultivation is concentrated in southern China, a region with persistent cloud cover.
  • Existing remote sensing methods for sugarcane are limited by data availability and spectral confusion with other crops.
  • Mapping large-scale sugarcane plantations is crucial for agricultural management and policy.

Purpose of the Study:

  • To develop an automated methodology for large-area sugarcane mapping using time-series middle-resolution remote sensing data.
  • To address limitations posed by cloudy climates and spectral mixing in sugarcane-growing regions.
  • To create a feasible and efficient remote sensing approach for crop classification in data-limited areas.

Main Methods:

  • Utilized time-series Chinese HJ-1 CCD images acquired during the sugarcane growing season.
  • Employed the object-oriented method (OOM) for generating image objects via multi-resolution segmentation.
  • Integrated data mining (DM), specifically the AdaBoost algorithm, to develop a predictive model for classification.

Main Results:

  • The developed prediction model was successfully applied to HJ-1 CCD time-series image objects.
  • A detailed map of sugarcane planting areas was produced with high classification accuracy.
  • Overall classification accuracy reached 93.6%, with a Kappa coefficient of 0.85, validated by field survey data.

Conclusions:

  • The proposed methodology is effective and efficient for automated, large-area sugarcane mapping.
  • This approach overcomes challenges associated with cloudy weather and spectral mixing.
  • The method demonstrates feasibility and applicability for classifying other crops in large areas where high-resolution data is impractical.