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[IEEE 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) - Beijing (2018.8.19-2018.8.20)] 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) - An Elegant End-to-End Fully Convolutional Network (E3FCN) for Green Tide Detection Using MODIS Data
摘要： Using remote sensing (RS) data to monitor the onset, proliferation and decline of green tide (GT) has great significance for disaster warning, trend prediction and decision-making support. However, remote sensing images vary under different observing conditions, which bring big challenges to detection missions. This paper proposes an accurate green tide detection method based on an Elegant End-to-End Fully Convolutional Network (E3FCN) using Moderate Resolution Imaging Spectroradiometer (MODIS) data. In preprocessing, RS images are firstly separated into subimages by a sliding window. To detect GT pixels more efficiently, the original Fully Convolutional Neural Network (FCN) architecture is modified into E3FCN, which can be trained end-to-end. The E3FCN model can be divided into two parts, contracting path and expanding path. The contracting path aims to extract high-level features and the expanding path aims to provide a pixel-level prediction by using a skip technique. The prediction result of whole image is generated by merging the prediction results of subimages, which can also improve the final performance. Experiment results show that the average precision of E3FCN on the whole data sets is 98.06%, compared to 73.27% of Support Vector Regression (SVR), 71.75% of Normalized Difference Vegetation Index (NDVI), and 64.41% of Enhanced Vegetation Index (EVI).
关键词： green tide,Elegant End-to-End Fully Convolutional Network (E3FCN),deep learning,remote sensing,Moderate Resolution Imaging Spectroradiometer (MODIS)
更新于2025-09-23 15:21:01
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Screw-Shaped Plastic Optical Fibers for Refractive Index Sensing
摘要： This paper reports a novel nonlinear algorithm for retrieving near surface air temperature over a large area using support vector machines with satellite remote sensing and other types of data. The steps include the following. 1) Establish the 1st sub model learning dataset and validation dataset, then obtain the 2nd to f th sub model learning datasets and validation datasets, using unmanned weather station data and prede?ned in?uential variables. 2) Retrieve Ta of the target area. 3) Correct the generated Ta images based on prediction errors using the inverse distance weighting interpolation. The novelty of this algorithm is to apply multiple sources of remote sensing data combined with data of unmanned weather stations, topography, ground cover, DEM, and astronomy and calendar rules. The results indicated that the model has high accuracy, reliability, and generalization ability. Factors such as cloudiness, ground vegetation, and water vapor show little interference, so the model seems suitable for large area retrieving under natural conditions. The required high-performance computation was achieved by a CPU + GPU isomery and synergy parallel computation system that improved computing speed by more than 1000-fold, with easily extendable computing capability. We found that the current algorithm is superior to seven major split-window algorithms and their best combined algorithms based on prediction errors, root-mean-square errors, and the percentage of data points with <3 ?C absolute error. Our SVM approach overcomes shortcomings of classical temperature remote sensing technologies, and is the ?rst report of such application.
关键词： high-performance computation (HPC),moderate-resolution imaging spectroradiometer (MODIS),digital elevation model (DEM),Area-wide retrieving,GIS spatial analysis,remote sensing,satellite,multivariable analysis,support vector machine (SVM)
更新于2025-09-16 10:30:52
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A Long-Term Historical Aerosol Optical Depth Data Record (1982-2011) Over China From AVHRR
摘要： A long-term historical aerosol optical depth (AOD) data (15–45° N; 75–135° E) with 0.1 spatial resolution has been produced from Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres—Extended level-2B data. The spatial distribution pattern shows that high AOD values are found in central and eastern China over the entire period with AODs larger in summer and spring than in autumn and winter. As the high-quality products from AERONET were absent for this period over mainland China, AOD data obtained using the broadband extinction method from solar radiation stations have been used to verify the quality of the AVHRR AOD data set over China. The intercomparison results show that the interannual variation of AOD has been well captured in the variation curve of the AOD monthly mean and the variation trend is also consistent over the whole period. The correlation coefficient of the monthly mean is mostly larger than 0.55, the agreement index is larger than 0.57, and the relative error is less than 21%. Both AVHRR and visibility data sets show high values in regions with rapid economic development. Using Moderate Resolution Imaging Spectroradiometer AOD data as references, it is found that AVHRR AOD from this paper has better accuracy in general than that from Deep Blue (DB) algorithm over China, especially over eastern and southern China, while DB provides more coverage especially over bright surface such as northwest China. This long-term historic AOD data set can be used together with other AOD data sets to study the climate and environmental changes, especially in the 1980s and 1990s.
关键词： Aerosol optical depth (AOD),Advanced Very High Resolution Radiometer (AVHRR),solar radiation,multiple regression method,Moderate Resolution Imaging Spectroradiometer (MODIS)
更新于2025-09-10 09:29:36