The area extraction of winter wheat in mixed planting area based on Sentinel-2 a remote sensing satellite images
The area extraction of winter wheat in mixed planting area based on Sentinel-2 a remote sensing satellite images
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DOI：
10.1080/17445760.2019.1597084
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
M. Wei;Baojun Qiao;Jianhui Zhao;Xianyu Zuo
中科院分区：
文献类型：
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作者：
M. Wei;Baojun Qiao;Jianhui Zhao;Xianyu Zuo
摘要 传统的区域提取方法主要依靠人工实地勘察方法，工作量大、速度慢、成本高。而遥感技术具有准确、快速、宏观、动态等优点，已成为提取农作物种植面积的有效手段。本文以河南省开封市为研究区域。首先，我们探讨了Sentinel-2A RENDVI在作物识别方面的优势。然后采用监督分类SVM、面向对象分类方法并辅助田间实测数据提取冬小麦种植面积，并对两种方法的特点进行比较分析。最后，我们结合上述两种分类方法，提出了一种新的分类方法V2OAE，以去除不必要的影响因素。实验结果表明，RENDVI在区分相似光谱植被方面比NDVI（归一化植被指数）具有更好的识别能力，面向对象分类的分类效果优于监督分类SVM，并且我们的分类方法去除了面向对象分类结果中不必要的影响因素，进一步提高了监测精度。首先对Sentinel-2A图像数据进行预处理，其步骤为：（1）第一步对遥感图像进行辐射定标，消除外界因素、数据采集传输系统等造成的图像畸变； (2)第二步，进行大气校正，消除大气吸收或散射引起的遥感影像光谱特征的变化； (3)第三步，进行波段重采样，统一遥感影像的分辨率，方便植被指数的数学组合运算； (4)第四步，进行拼接和裁剪，得到预处理后的开封市遥感影像。其次，我们分析了每个物体的光谱特征，并根据现场测量数据建立了解释标记。然后我们探索了基于NDVI（归一化植被指数）和RENDVI识别地物的能力。第三，采用基于规则的面向对象分类方法和SVM分类来提取研究区域的种植区域，SVM的输入定义是地物的光谱特征图像，SVM的输出定义是数据训练过程中地物的识别结果。然后分析了两种方法在分类结果上的优缺点。最后，为了更准确地提取冬小麦信息，我们将上述两种分类方法结合起来，提出了一种新的分类方法V2OAE（Vector Object Oriented Area Extraction），去除不必要的影响因素，统计得出开封市冬小麦种植面积。图解摘要
ABSTRACT The traditional area extraction method mainly depends on manual field survey methods, it is workload, slow and high cost. While remote sensing technology has the advantages of accuracy, rapidity, macroscopic and dynamic, which has become an effective means to extract crop growing area. In this paper, we took Kaifeng City in Henan Province as the study area. Firstly, we explored the advantages of Sentinel-2A RENDVI in crop identification. Then used the supervised classification SVM, object-oriented classification method and assisted with field measured data to extract the winter wheat planting area, the characteristics of the two methods were compared and analysed. Finally, we combined the above two classification methods and proposed a new classification method V2OAE to remove unnecessary influencing factors. The experiment results showed that RENDVI has better recognition ability than the NDVI (Normalized Difference Vegetation Index) in distinguishing vegetation with similar spectrum, the classification effect of object-oriented classification is better than supervised classification SVM, and our classification method removes unnecessary influence factors in the results of object-oriented classification, which is further improve the monitoring accuracy. Firstly, we have preprocessed the Sentinel-2A image data, its steps are: (1) In the first step, we made radiation calibration for remote sensing images to eliminate the image distortion caused by external factors, data acquisition and transmission systems and so on; (2) In the second step, we made atmospheric correction to eliminate changes in the spectral feature of remote sensing images caused by atmospheric absorption or scattering; (3) In the third step, we made band resampling to unify the resolution of remote sensing images and facilitate the mathematical combination operation of vegetation index; (4) In the fourth step, we made mosaic and cutting to get preprocessed remote sensing images of Kaifeng City. Secondly, we analysed the spectral features of each object and established the interpretation mark with the field measured data. then we explored the ability to identify the ground objects based on NDVI(Normalized Difference Vegetation Index) and RENDVI. Third, we used the rule-based object-oriented classification method and SVM classification to extract the planting area of the study area, the input definition of SVM is spectral feature images of ground objects and the output definition of SVM is the recognition result of ground objects in the process of data training. Then the advantages and disadvantages of the two methods in classification results were analysed. Finally, In order to extract winter wheat information more accurately, we combined the above two classification methods and proposed a new classification method V2OAE (Vector Object Oriented Area Extraction) to remove unnecessary influencing factors, then the winter wheat planting area in Kaifeng City was statistically obtained. GRAPHICAL ABSTRACT