Efficient paddy field mapping using Landsat-8 imagery and object-based image analysis based on advanced fractel net evolution approach
Efficient paddy field mapping using Landsat-8 imagery and object-based image analysis based on advanced fractel net evolution approach
复制标题
使用 Landsat-8 图像和基于先进分形网络演化方法的基于对象的图像分析进行高效稻田测绘
DOI：
10.1080/15481603.2016.1273438
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发表时间：
2017-05
影响因子：
6.7
通讯作者：
Su Tengfei
中科院分区：
文献类型：
--
作者：
Su Tengfei
This paper proposes an efficient paddy field mapping method using object-based image analysis and a bitemporal data set acquired by Landsat-8 Operational Land Imager. In the proposed approach, image segmentation is the first step and its quality has a serious impact on the accuracy of paddy field classification. In order to improve segmentation quality, a new segmentation algorithm based on a frequently used method, fractal net evolution approach, is developed, with improvement mainly in merging criteria. In order to automate the process of scale parameter determination, an unsupervised scale selection method is utilized to determine the optimal scale parameter for the proposed image segmentation approach. After segmentation, four types of object-based features including geometric, spectral, textural, and contextual information are extracted and input into the subsequent classification procedure. By using a random forest classifier, paddy fields and nonpaddy fields are separated. The proposed image segmentation method and the final classification result are both quantitatively evaluated. Our segmentation method outperformed two popular algorithms according to three supervised evaluation criteria. The classification result with overall accuracy of 91.00% and kappa statistic of 0.82 validated the effectiveness of the proposed framework. Further analysis on feature importance indicated that spectral features made the most contribution as compared to the other three types of object-based features.
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DOI：
10.1007/978-0-387-39940-9_3079
发表时间：
2009
期刊：
--
影响因子：
--
作者：
通讯作者：
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影响因子：
8.2
作者：
Cuizhen Wang;Jiaping Wu;Yuan Zhang-;G. Pan;J. Qi;W. Salas
通讯作者：
Cuizhen Wang;Jiaping Wu;Yuan Zhang-;G. Pan;J. Qi;W. Salas
利用 RADARSAT-2 四极化 SAR 数据监测华南地区的水稻作物
影响因子：
4.8
作者：
Bo Zhang;Yixian Tang;Hong Zhang;Chao Wang;Fan Wu
通讯作者：
Fan Wu
DOI：
10.1016/b978-0-12-818373-1.00002-0
发表时间：
2020
期刊：
Agricultural Internet of Things and Decision Support for Precision Smart Farming
影响因子：
--
作者：
Abdul M. Mouazen;T. Alexandridis;Henning Buddenbaum;Yafit Cohen;Dimitrios Moshou;David J. Mulla
通讯作者：
Abdul M. Mouazen;T. Alexandridis;Henning Buddenbaum;Yafit Cohen;Dimitrios Moshou;David J. Mulla
影响因子：
13.5
作者：
Sakamoto, Toshihiro;Wardlow, Brian D.;Arkebauer, Timothy J.
通讯作者：
Arkebauer, Timothy J.