Extraction of winter rapeseed from high-resolution remote sensing imagery via deep learning

doi:10.13474/j.cnki.11-2246.2020.0294

Abstract

Abstract: Recently, deep learning technology has been widely used in the crop extraction from high-resolution remote sensing data. This paper exploits the use of the spectral characteristics of rapeseed in the flowering period, and proposes a rapeseed extraction method from single-phase high-resolution remote sensing image based on deep learning theory. The paper employs GF-1 satellite data during the rapeseed flowering period, Shayang City, Hubei Province, as the research data. Firstly, rapeseed training samples are annotated on the image by manual labeling. Then, two deep learning framework models are built, including a one-dimensional convolutional neural network (1D-CNN) and a recurrent neural network (RNN), to conduct the rapeseed extraction with the help of annotated training samples. Finally, the rapeseed extraction results are evaluated by comparing with traditional support vector machine (SVM) and random forest (RF) methods. The experimental results show that the spatial distribution accuracy and area accuracy of winter rapeseed based on deep learning CNN and RNN models designed in this paper are better than the other two methods, which provides guidance for automation of large area winter rapeseed extraction.

Key words: extraction of winter rapeseed, deep learning, CNN, RNN, GaoFen-1

CLC Number:

P237

YANG Zeyu, ZHANG Hongyan, MING Jin, LENG Wei, LIU Haiqi, YOU Jiong. Extraction of winter rapeseed from high-resolution remote sensing imagery via deep learning[J]. Bulletin of Surveying and Mapping, 2020, 0(9): 110-113.

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