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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (2) : 1-9     DOI: 10.6046/gtzyyg.2019.02.01
A review of per-field crop classification using remote sensing
Yanxin HAN1,2, Jihua MENG1()
1.Key Laboratory for Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
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Crop classification using remote sensing is the key to monitoring crop planting acreage and has great significance in further thematic monitoring. As field contains more accurate information of location and acreage than object which is the result of clustering similar pixels, it has been applied to crop classification using remote sensing increasingly. This paper summarizes the progress of per-field crop classification using remote sensing systematically, including its theories, methods and applications. Furthermore, a series of problems are analyzed and future study directions are viewed. Studies show that digitalization and image segmentation are the main approach to obtaining field boundary and more nationwide field database and bringing per-field classification a new opportunity. The strategies of per-field classification can be divided into two categories:using field features as input for the classifier and assigning field class based on per-pixel classification. The progress of features and classifiers in classification with remote sensing data are summarized further. It is indicated that combined application of multi-source data, development of field boundary detection, new features selection and improving implementation capacity of remote sensing image classification will be the crucial issues in per-field classification using remote sensing.

Keywords per-field      crop      remote sensing      classification     
:  TP79  
Corresponding Authors: Jihua MENG     E-mail:
Issue Date: 23 May 2019
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Yanxin HAN,Jihua MENG. A review of per-field crop classification using remote sensing[J]. Remote Sensing for Land & Resources, 2019, 31(2): 1-9.
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Fig.1  Reflectance spectra of crop
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