Exploring effect of segmentation scale on orient-based crop identification using HJ CCD data in Northeast China

35th International Symposium on Remote Sensing of Environment

地球科学;生态环境科学

Cao, Xin^1 ; Li, Qiangzi^2 ; Du, Xin^2 ; Zhang, Miao^2 ; Zheng, Xinqi^1

School of Land Science and Technology, China University of Geosciences, Beijing 100083, China^1

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100101, China^2

关键词: Class separation;    Crop identification;    Crop production;    Feature combination;    High spatial resolution images;    Multi-scale image segmentation;    Nearest-neighbour classifier;    Object oriented classification;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/17/1/012047/pdf
DOI  :  10.1088/1755-1315/17/1/012047

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Crop identification and acreage estimation with remote sensing were the main issues for crop production estimation. Object-oriented classification has been involved in crop extraction from high spatial resolution images. However, different imagery segmentation scales for object-oriented classification always yield quite different crop identification accuracy. In this paper, multi-scale image segmentation was conducted to carry out crop identification using HJ CCD imagery in Red Star Farm in Heilongjiang province. Corn, soybean and wheat were identified as the final crop classes. Crop identification features at different segmentation scale were generated. Crop separability based on different feature-combinations was evaluated using class separation distance. Nearest Neighbour classifier (NN) was then used for crop identification. The results showed that the best segmentation scale was 8, and the overall crop identification accuracy was about 0.969 at that scale.