Remote Sensing Applications and Decision Support

Potential of multitemporal Gaofen-1 panchromatic/multispectral images for crop classification: case study in Xinjiang Uygur Autonomous Region, China

[+] Author Affiliations
Pengyu Hao

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, The State Key Laboratory of Remote Sensing Science, CAS Olympic S&T Park, No. 20 Datun Road, P.O. Box 9718, Beijing 100101, China

University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China

Li Wang, Zheng Niu

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, The State Key Laboratory of Remote Sensing Science, CAS Olympic S&T Park, No. 20 Datun Road, P.O. Box 9718, Beijing 100101, China

J. Appl. Remote Sens. 9(1), 096035 (Jun 26, 2015). doi:10.1117/1.JRS.9.096035
History: Received February 9, 2015; Accepted June 3, 2015
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Abstract.  Gaofen-1 panchromatic/multispectral (GF-1 PMS) data have both high spatial and temporal resolutions, and this research aims at evaluating the potential of GF-1 PMS data for crop classification. Three PMS images (at days 110, 192, and 274) were acquired in Manas County of Xinjiang. The images were first segmented and all objects were then visually interpreted based on ground reference data. Some indices and textual features were then extracted at the object level. Subsequently, the Jeffries–Matusita (JM) distance was employed to estimate the class separability among all pair-wise comparisons of each time period. Afterward, a random forest algorithm was used to calculate importance scores of all features and classify crop types for every possible image combination. Additionally, to evaluate the influence of feature number on classification accuracy, features were added one by one based on the importance of scores. The result showed that GF-1 PMS images with high-spatial resolution had the potential to identify the boundary of the crop fields. Relatively high JM distance (above 1.5) and classification accuracy (above 90%) indicated that day 192 image contributed the most to the crop identification in the study area. For multi-image combinations, days 110 to 192 combination can achieve high overall accuracy (around 93%) and more images cannot substantially improve the classification performance. As for features, normalized difference vegetation index and near infrared (NIR) band had the highest importance scores and textual features contributed to distinguishing tree from crop land. Finally, classification accuracy increased together with the augmentation of feature number when only a few features were used. After accuracies reached saturation points, however, more features only slightly improved the classification performance.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Pengyu Hao ; Li Wang and Zheng Niu
"Potential of multitemporal Gaofen-1 panchromatic/multispectral images for crop classification: case study in Xinjiang Uygur Autonomous Region, China", J. Appl. Remote Sens. 9(1), 096035 (Jun 26, 2015). ; http://dx.doi.org/10.1117/1.JRS.9.096035


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