Crop Mapping of Complex Agricultural Landscapes Based on Discriminant Space

文献类型： 外文期刊

第一作者： You, Jiong

作者： You, Jiong;Pei, Zhiyuan;Wang, Dongliang

作者机构：

关键词： Complex agricultural landscapes;crop mapping;discriminant space;probability calibration

期刊名称：IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING （ 影响因子：3.784； 五年影响因子：3.734 ）

ISSN： 1939-1404

年卷期： 2014 年 7 卷 11 期

页码：

收录情况： SCI

摘要： Crop mapping is related to planting conditions, remote sensing data source, and performances of various classifiers. Considering the above key factors, this study discusses a crop mapping approach for complex agricultural landscapes based on a conceptual frame that is called discriminant space. The introduced discriminant space-based model follows the pattern and process rules in geosciences and is found by the variables closely related to crop growth. On this basis, a method of classification based on probability calibration algorithm (CPCA) is developed, which consists of several steps. First, discriminant variables were extracted and then the experimental discriminant space was founded. Spectral analysis was carried out in this process to select the dominant impact factors for crop mapping. Based on the founded discriminant space, we calculated the class-conditional probabilities for each category using kernel density estimation and separated the residuals of class-conditional probabilities from the structural component through a trend-surface analysis. Then, calibration of the class-conditional probabilities was implemented using the predicted residuals generated by simple kriging with varying local means. Classification was achieved according to the calibrated probabilities with respect to each category. Accuracy assessment and kappa analysis were used to evaluate the identification results. Statistical test procedure was applied to measure the differences between classification maps and the validation data based on individual categories. Results are discussed for an intensively cropped area in south China. Compared with two other supervised classifiers, the overall accuracy of the proposal method increases by more than 10% and is able to achieve 87.12%. Moreover, there are some substantial increases in the producer's accuracy and the user's accuracy of single-cropping rice and late rice, with a precision increase of more than 15%, effectively improves the identification accuracy of rice in the research region. The proposed method overcomes the limitations due to spectrum characteristics and considers the structural and the random properties of spatial distribution for categories in local regions. Therefore, a similar operation can usually be implemented for crop identification of complex agricultural landscapes.

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  • 相关文献

[1]Discriminant models for crop identification of complex agricultural landscapes. You, Jiong,Pei, Zhiyuan,Wang, Dongliang. 2013

[2]Evaluation of Orthomosics and Digital Surface Models Derived from Aerial Imagery for Crop Type Mapping. Wu, Mingquan,Niu, Zheng,Wang, Changyao,Li, Wang,Wu, Mingquan,Yang, Chenghai,Song, Xiaoyu,Hoffmann, Wesley Clint,Song, Xiaoyu,Huang, Wenjiang. 2017

[3]Mapping regional cropping patterns by using GF-1 WFV sensor data. Song Qian,Zhou Qing-bo,Wu Wen-bin,Hu Qiong,Lu Miao,Liu Shu-bin. 2017

[4]Error modeling based on geostatistics for uncertainty analysis in crop mapping using Gaofen-1 multispectral imagery. You, Jiong,Pei, Zhiyuan. 2015

[5]Winter Wheat Plant Area Monitoring using GF-1 WFV Imagery. You, Jiong,Pei, Zhiyuan,Wang, Fei. 2016

[6]In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest. Song, Qian,Hu, Qiong,Zhou, Qingbo,Xiang, Mingtao,Tang, Huajun,Wu, Wenbin,Hovis, Ciara. 2017

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