Dr. Yonglan Qian Profile

Dr. Yonglan Qian

at Chinese Meteorological Administration

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Publications (4)

SPIE Journal Paper | 25 May 2021 Open Access

Improved semantic segmentation method using edge features for winter wheat spatial distribution extraction from Gaofen-2 images

Hao Yin, Chengming Zhang, Yingjuan Han, Yonglan Qian, Tao Xu, Ziyun Zhang, Ailing Kong

JARS, Vol. 15, Issue 02, 028501, (May 2021) https://doi.org/10.1117/12.10.1117/1.JRS.15.028501

KEYWORDS: Image segmentation, Feature extraction, Edge detection, Convolution, Remote sensing, Detection and tracking algorithms, Data modeling, Computer programming, Agriculture, Roads

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In the final feature map obtained using a convolutional neural network for remote sensing image segmentation, there are great differences between the feature values of the pixels near the edge of the block and those inside the block; ensuring consistency between these feature values is the key to improving the accuracy of segmentation results. The proposed model uses an edge feature branch and a semantic feature branch called the edge assistant feature network (EFNet). The EFNET model consists of one semantic branch, one edge branch, one shared decoder, and one classifier. The semantic branch extracts semantic features from remote sensing images, whereas the edge branch extracts edge features from remote sensing images and edge images. In addition, the two branches extract five-level features through five sets of feature extraction units. The shared decoder sets up five levels of shared decoding units, which are used to further integrate edge features and deep semantic features. This strategy can reduce the feature differences between the edge pixels and the inner pixels of the object, obtaining a per-pixel feature vector with high inter-class differentiation and intra-class consistency. Softmax is used as the classifier to generate the final segmentation result. We selected a representative winter wheat region in China (Feicheng City) as the study area and established a dataset for experiments. The comparison experiment included three original models and two models modified by adding edge features: SegNet, UNet, and ERFNet, and edge-UNet and edge-ERFNet, respectively. EFNet’s recall (91.01%), intersection over union (81.39%), and F1-Score (91.68%) were superior to those of the other methods. The results clearly show that EFNET improves the accuracy of winter wheat extraction from remote sensing images. This is an important basis not only for crop monitoring, yield estimation, and disaster assessment but also for calculating land carrying capacity and analyzing the comprehensive production capacity of agricultural resources.

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Proceedings Article | 10 September 2008 Paper

Accuracy assessment on the crop area estimating method based on RS sampling at national scale: a case study of China's rice area estimation assessment

Yonglan Qian, Bangjie Yang, Xianfeng Jiao, Zhiyuan Pei, Xuan Li

Proceedings Volume 7085, 708518 (2008) https://doi.org/10.1117/12.792518

KEYWORDS: Remote sensing, Error analysis, Agriculture, Accuracy assessment, Landsat, Image classification, Global Positioning System, Classification systems, Curium, Earth observing sensors

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Proceedings Article | 11 January 2007 Paper

Crop monitoring using remote sensing orientated for government decision making and agricultural management: a case study of China's soybean planting area estimation

Bangjie Yang, Yonglan Qian, Zhiyuan Pei, Xianfeng Jiao

Proceedings Volume 6412, 64120T (2007) https://doi.org/10.1117/12.693679

KEYWORDS: Remote sensing, Agriculture, Landsat, Accuracy assessment, Image resolution, Image classification, Lithium, Environmental sensing, Yield improvement, Earth observing sensors

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Proceedings Article | 12 December 2006 Paper

Incorporating remote sensing data in crop model to monitor crop growth and predict yield in regional area

Jianmao Guo, Weisong Lu, Guoping Zhang, Yonglan Qian, Qiang Yu, Jiahua Zhang

Proceedings Volume 6411, 64111C (2006) https://doi.org/10.1117/12.692756

KEYWORDS: Data modeling, Remote sensing, Calibration, Vegetation, Agriculture, Landsat, Climatology, Reflectivity, Atmospheric modeling, Earth observing sensors

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