%A Houwen Zhu,Chong Luo,Haixiang Guan,Xinle Zhang,Jiaxin Yang,Mengning Song,Huanjun Liu %T Object-oriented Extraction of Maize Fallen Area based on Multi-source Satellite Remote Sensing Images %0 Journal Article %D 2022 %J Remote Sensing Technology and Application %R 10.11873/j.issn.1004-0323.2022.3.0599 %P 599-607 %V 37 %N 3 %U {http://www.rsta.ac.cn/CN/abstract/article_3520.shtml} %8 2022-06-20 %X

Maize lodging caused by wind disaster may lead to a large reduction in maize production. Using remote sensing technology to accurately monitor maize lodging area and spatial distribution information is very important for disaster assessment.In this paper, Planet and Sentinel-2 images are combined with object-oriented and pixel-based methods to extract maize lodging in the study area, and different image features (spectral features, vegetation index and texture features) and different classification methods (support vector machine SVM, Random forest method RF and maximum likelihood method MLC) influence on the extraction accuracy of corn lodging.The results show that: ① The accuracy of corn lodging extraction using Planet images with high spatial resolution is generally higher than that of Sentinel-2 images;② From the perspective of classification accuracy and area accuracy, the spectral features, vegetation index, and mean feature of Planet image combined with object-oriented RF classification, the overall accuracy and Kappa coefficient are 93.77% and 0.87, respectively, and the average area error is the lowest 4.76%;③The accuracy of extracting maize lodging using Planet and Sentinel-2 images combined with object-oriented classification is higher than that of pixel-based classification. This research not only analyzes the advantages of object-oriented methods, but also evaluates the applicability of using different image data combined with object-oriented methods, which can provide a certain reference for remote sensing to extract crop lodging related research.