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

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.

Early Season Mapping of Sugarcane by Applying Machine Learning Algorithms to Sentinel-1A/2 Time Series Data: A Case Study in Zhanjiang City, China

“…The recording tools we used were a mobile app and a drone. The app was developed by our team and For sugarcane: (1) the vegetative period runs from mid-March through April; (2) the reproductive period lasts from May through September; (3) the ripening period (including sugar accumulation) extends from October to early December; and (4) the harvest begins in late December and lasts until March of the following year (with most sugarcane being reaped in February) [14].…”

“…The assumption behind early season sugarcane mapping using the S1A image time series is that the majority of the periods that are sensitive to sugarcane differentiation occur earlier than the insensitive periods. Moreover, the images obtained during the latter phenology stage after sugar accumulation may not be necessary [14].…”

“…Using six Chinese HJ-1 CCD images in 2013 and 2014, Zhou proposed an object-oriented classification method for mapping the extent of sugarcane in Suixi County, Zhanjiang City. The method employed spectral, spatial, and textural features that produced an overall classification accuracy of 93.6% [14].…”

Mapping Crop Planting Quality in Sugarcane from UAV Imagery: A Pilot Study in Nicaragua

“…Object-based recognition [46,47] could improve identifying sugarcane plants [48], and more sophisticated classification methods (notably Support Vector Machines [49,50]) will probably outperform LDA. It can be argued that requiring a training set of diagnosed sites could be seen as a drawback.…”

Mapping of sugarcane crop area in the Paraná State using Landsat/TM/OLI and IRS/LISS-3 images

“…The sensors Thematic Mapper (TM) and Operational Land Imager (OLI) of the Landsat satellite and Linear Imaging Self Scanning (LISS-3) of the IRS satellite are widely used because of their better spatial resolution (Landsat with 30 m and IRS with 23.5 m) when compared to the MODIS, which allows a better distinction of the terrestrial targets, as demonstrated by studies on the mapping of areas of sugarcane and other agricultural crops Vieira et al, 2012;Adami et al, 2012;Zhou et al, 2015).…”