Abstract
Most areas planted with sugarcane are located in southern China. However, remote sensing of sugarcane has been limited because useable remote sensing data are limited due to the cloudy climate of this region during the growing season and severe spectral mixing with other crops. In this study, we developed a methodology for automatically mapping sugarcane over large areas using time-series middle-resolution remote sensing data. For this purpose, two major techniques were used, the object-oriented method (OOM) and data mining (DM). In addition, time-series Chinese HJ-1 CCD images were obtained during the sugarcane growing period. Image objects were generated using a multi-resolution segmentation algorithm, and DM was implemented using the AdaBoost algorithm, which generated the prediction model. The prediction model was applied to the HJ-1 CCD time-series image objects, and then a map of the sugarcane planting area was produced. The classification accuracy was evaluated using independent field survey sampling points. The confusion matrix analysis showed that the overall classification accuracy reached 93.6% and that the Kappa coefficient was 0.85. Thus, the results showed that this method is feasible, efficient, and applicable for extrapolating the classification of other crops in large areas where the application of high-resolution remote sensing data is impractical due to financial considerations or because qualified images are limited.
Highlights
Sugar is a major food additive and is one of the most important raw bioenergy materials
Considering the influences of climate and the affordability of high spatial resolution remote sensing data, this study aimed to demonstrate the feasibility of using object-oriented method (OOM) and the AdaBoost algorithm based on HJ-1 A/B data to classify sugarcane growing areas in regions with limited data and complex land cover
In the process of segmenting the multi-temporal HJ-1 CCD time-series images, image objects were generated based on several adjustable criteria of homogeneity or heterogeneity in color and shape
Summary
Sugar is a major food additive and is one of the most important raw bioenergy materials. Sugar made from sugarcane accounts for approximately 80% of the total sugar production in China [1]. The areas of sugarcane planting and production in China are ranked third in the world after those of India and Brazil. Regarding safety and policy making, it is important to quickly. Object-Oriented Sugarcane Classification design, data collection and analysis, decision to publish, or preparation of the manuscript
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
