Accurate and timely large-scale paddy rice maps with remote sensing are essential for crop monitoring and management and are used for assessing its impacts on food security,water resource management,and transmission o...Accurate and timely large-scale paddy rice maps with remote sensing are essential for crop monitoring and management and are used for assessing its impacts on food security,water resource management,and transmission of zoonotic infectious diseases.Optical image-based paddy rice mapping studies employed the unique spectral feature during the flooding/transplanting period of paddy rice.However,the lack of high-quality observations during the flooding/transplanting stage caused by rain and clouds and spectral similarity between paddy rice and natural wetlands often introduce errors in paddy rice identification,especially in paddy rice and wetland coexistent areas.In this study,we used a knowledge-based algorithm and time series observation from optical images(Sentinel-2 and Landsat 7/8)and microwave images(Sentinel-1)to address these issues.The final 10-m paddy rice map had user’s accuracy,producer’s accuracy,F1-score,and overall accuracy of 0.91±0.004,0.74±0.010,0.82,and 0.98±0.001(±value is the standard error),respectively.Over half(62.0%)of the paddy rice pixels had a confidence level of 1(detected by both optical images and microwave images),while 38.0%had a confidence level of 0.5(detected by either optical images or microwave images).The estimated paddy rice area in northeast China for 2020 was 60.83±0.86×10^(3)km^(2).Provincial and municipal rice areas in our data set agreed well with other existing paddy rice data sets and the Agricultural Statistical Yearbooks.These findings indicate that knowledge-based paddy rice mapping algorithms and a combination of optical and microwave images hold great potential for timely and frequently accurate paddy rice mapping in large-scale complex landscapes.展开更多
The 3D object tracking from a monocular RGB image is a challenging task.Although popular color and edgebased methods have been well studied,they are only applicable to certain cases and new solutions to the challenges...The 3D object tracking from a monocular RGB image is a challenging task.Although popular color and edgebased methods have been well studied,they are only applicable to certain cases and new solutions to the challenges in real environment must be developed.In this paper,we propose a robust 3D object tracking method with adaptively weighted local bundles called AWLB tracker to handle more complicated cases.Each bundle represents a local region containing a set of local features.To alleviate the negative effect of the features in low-confidence regions,the bundles are adaptively weighted using a spatially-variant weighting function based on the confidence values of the involved energy terms.Therefore,in each frame,the weights of the energy items in each bundle are adapted to different situations and different regions of the same frame.Experiments show that the proposed method can improve the overall accuracy in challenging cases.We then verify the effectiveness of the proposed confidence-based adaptive weighting method using ablation studies and show that the proposed method overperforms the existing single-feature methods and multi-feature methods without adaptive weighting.展开更多
基金supported by grants from the US National Science Foundation(Nos.1911955,2200310)the National Key Research and Development Program of China(No.2023YFF0806900)the China Postdoctoral Science Foundation(Nos.2021TQ0072,2021M700835).
文摘Accurate and timely large-scale paddy rice maps with remote sensing are essential for crop monitoring and management and are used for assessing its impacts on food security,water resource management,and transmission of zoonotic infectious diseases.Optical image-based paddy rice mapping studies employed the unique spectral feature during the flooding/transplanting period of paddy rice.However,the lack of high-quality observations during the flooding/transplanting stage caused by rain and clouds and spectral similarity between paddy rice and natural wetlands often introduce errors in paddy rice identification,especially in paddy rice and wetland coexistent areas.In this study,we used a knowledge-based algorithm and time series observation from optical images(Sentinel-2 and Landsat 7/8)and microwave images(Sentinel-1)to address these issues.The final 10-m paddy rice map had user’s accuracy,producer’s accuracy,F1-score,and overall accuracy of 0.91±0.004,0.74±0.010,0.82,and 0.98±0.001(±value is the standard error),respectively.Over half(62.0%)of the paddy rice pixels had a confidence level of 1(detected by both optical images and microwave images),while 38.0%had a confidence level of 0.5(detected by either optical images or microwave images).The estimated paddy rice area in northeast China for 2020 was 60.83±0.86×10^(3)km^(2).Provincial and municipal rice areas in our data set agreed well with other existing paddy rice data sets and the Agricultural Statistical Yearbooks.These findings indicate that knowledge-based paddy rice mapping algorithms and a combination of optical and microwave images hold great potential for timely and frequently accurate paddy rice mapping in large-scale complex landscapes.
基金supported by Zhejiang Lab under Grant No.2020NB0AB02the Industrial Internet Innovation and Development Project in 2019 of China。
文摘The 3D object tracking from a monocular RGB image is a challenging task.Although popular color and edgebased methods have been well studied,they are only applicable to certain cases and new solutions to the challenges in real environment must be developed.In this paper,we propose a robust 3D object tracking method with adaptively weighted local bundles called AWLB tracker to handle more complicated cases.Each bundle represents a local region containing a set of local features.To alleviate the negative effect of the features in low-confidence regions,the bundles are adaptively weighted using a spatially-variant weighting function based on the confidence values of the involved energy terms.Therefore,in each frame,the weights of the energy items in each bundle are adapted to different situations and different regions of the same frame.Experiments show that the proposed method can improve the overall accuracy in challenging cases.We then verify the effectiveness of the proposed confidence-based adaptive weighting method using ablation studies and show that the proposed method overperforms the existing single-feature methods and multi-feature methods without adaptive weighting.
