China Institute of Water Resources and Hydropower Research

Yizhu Lu

Wenlong Song

Li Ni Zhang

Long Chen

Xuejun Zhang

Yun Liu

Lihua Zhao

Hongjie Liu

Juan Lv

Lang Yu

Changjun Liu

Wenjing Lu

Pu Zhou

Xiuhua Chen

Mengyi Li

Jingxuan Lu

Zhicheng Su

Rui Tang

Yuanyuan Duan

A rapid monitoring and discrimination method for drought conditions in summer maize based on chlorophyll content includes: 1) Obtaining multi-spectral imagery through UAV multi-payload low-altitude remote sensing technology and measuring chlorophyll content on the ground. Additionally, calculating vegetation indices including Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Renormalized Difference Vegetation Index (RENDVI). 2) Selecting vegetation indices and constructing regression equations with measured chlorophyll content during different growth stages. The regression equation with the highest correlation for each growth stage is chosen as the optimal model equation for that particular stage. 3) Using the optimal model equations to retrieve chlorophyll content for each period and determining thresholds for chlorophyll content across different drought levels through calibration. 4) Calculating the required vegetation indices from real-time multi-spectral imagery of the field under test, retrieving chlorophyll content, and comparing it with the established thresholds to assess the real-time drought level.

Rapid UAV-based monitoring and discrimination method for drought in summer maize based on chlorophyll content

A Region Merging image segmentation algorithm based on boundary extraction is disclosed, comprising the steps of calculating a gradient image, extracting a boundary and carrying out initial segmentation and Region Merging, wherein each pixel is regarded as one region when initial segmentation is not conducted. In the Region Merging process, the portion of the common boundary of two adjacent regions that lies on the boundary image is taken as the merging cost, the regions are merged according to the ascending order of the mean gradient value inside the region, and a texture difference evaluation mechanism is introduced to reduce the wrong segmentation. The algorithm solves the problems of the other current segmentation algorithms, such as over-segmentation, easy to be influenced by noise and illumination, large in computation and memory consumption, in need of a large number of samples being marked manually and the like. Besides, all regions or all categories achieve the best segmentation result on one final segmentation result. These advantageous features can reduce the computational resource consumption of subsequent tasks and improve their results.

Region merging image segmentation algorithm based on boundary extraction

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Beijing, China

Yizhu Lu