High-quality aerosol optical depth(AOD)data derived from MODIS is widely used in studying spatiotemporal trends of fine particulate matter(PM2.5)concentrations in eastern ***,the differences of MODIS-AOD(3/10 km DT;10...
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High-quality aerosol optical depth(AOD)data derived from MODIS is widely used in studying spatiotemporal trends of fine particulate matter(PM2.5)concentrations in eastern ***,the differences of MODIS-AOD(3/10 km DT;10 km db)under four pollution situations(No-Po;Sl-Po;Mo-Po;Se-Po)are rarely *** this study,the MODIS-AOD and AODDifference spatial distributions from 2008 to 2017 are analyzed through annual/seasonal mean AOD maps generated at 0.1°×0.1°*** MODIS-AOD performances are validated using AERONET AOD data for various pollution situations and aerosol *** validations indicate that the 10-km db algorithm provides the best performance,followed by 3-km DTand 10 km *** db algorithm can provide spatially continuous AOD data for all seasons,whereas the DT algorithm often fails to yield valid data during *** validations under different pollution conditions illustrate that severe pollution significantly affects the validity of data obtained by the db ***,the accuracy of DT-derived AOD data is robust against *** the same pollution conditions,the correlation coefficient of the db algorithm is smaller than that of the DT *** quantity of valid data in the db product is higher than those in DT products for all pollution conditions,especially under Se-Po.
In recent years, due to the increasingly application of machine vision in various aspects, the topic of text recognition in actual scenes has gradually become a research hot spot of machine vision research. For images...
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ISBN:
(数字)9781665458641
ISBN:
(纸本)9781665458641
In recent years, due to the increasingly application of machine vision in various aspects, the topic of text recognition in actual scenes has gradually become a research hot spot of machine vision research. For images with complex backgrounds, the first thing to do is to accurately locate the position of the target text, and then the text content can be efficiently identified. However, as far as the current text detection algorithms based on deep learning are concerned, there are still problems such as incomplete extraction of text feature regions, and wrong detection of images as text ***, this paper proposes an improved db algorithm to solve the problems of the variable shape and complex background of the text area in the label text detection task, so that the algorithm can achieve better detection effect and better performance in complex scenes. The content of the article is mainly from the following main aspects:firstly, the current situation of text detection algorithms is introduced, then the improvement of the db algorithm with ResNet-50 as the backbone network is proposed, and finally mPA (mean Average Precision) is used as the evaluation of text detection. By comparing the various detection algorithms, it is found that the improved algorithm has significantly improved the detection accuracy and recall rate, and the model speed is also faster.
Aerosol optical depth (AOD) is one of the most crucial parameters for reflecting aerosol characteristics. This study systematically evaluated daily AOD from Modern-Era Retrospective Analysis for Research and Applicati...
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Aerosol optical depth (AOD) is one of the most crucial parameters for reflecting aerosol characteristics. This study systematically evaluated daily AOD from Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2), Visible Infrared Imaging Radiometer Suite (VIIRS), and Moderate-resolution Imaging Spectroradiometer (MODIS) daily gridded products over China from 2012 to 2019. Both the VIIRS aerosol Deep Blue (db) and MODIS Deep Blue algorithm products had significant missing values in areas, for example, the Qinghai-Tibet Plateau, Xinjiang, and Northeast China. High AODs were mainly concentrated in areas closely related to economic development and population density geomorphology factors, such as the North China Plain, Yangtze River Delta, Pearl River Delta, and Sichuan Basin. Notably, VIIRS products captured higher AOD values in desert regions than the other products. In overall accuracy, MODIS db product was characterized by a correlation coefficient (R) of 0.82, root mean square error (RMSE) of 0.21, and mean absolute error (MAE) of 0.14, and VIIRS (RMSE = 0.23 and MAE = 0.15) and MERRA-2 (RMSE = 0.27 and MAE = 0.17) had a slightly larger bias. MODIS performed best, with 55% of matched samples falling within the expected error (within EE), higher than the 53% of VIIRS. However, the MERRA-2 product performed worst, with only 48% of matched samples falling within the EE. Based on the accuracy comparison of the same number of matched samples, MODIS db product still performed best, with R = 0.88, RMSE = 0.17, MAE = 011 and 64% of matched samples falling within the EE. Moreover, snow cover and the complex surface features in South-West areas may limit the performance of db algorithm products, but MODIS db products are available in most other areas of China. Land cover type applicability analysis demonstrated that db algorithm products performed well in low vegetation cover and impervious surface types. In the analysis of elevation applicabilit
This study compares the precision of the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (db) Collection-51 (C-51) and Collection-06 (C-06) Aerosol Optical Depth (AOD, at 550 nm) produc...
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This study compares the precision of the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (db) Collection-51 (C-51) and Collection-06 (C-06) Aerosol Optical Depth (AOD, at 550 nm) products with surface-based aerosol robotic network (AERONET) observations for the period 2002-2013 at the Solar Village, Saudi Arabia. In general, MODIS captures the patterns of AERONET AOD although C 51 tends to underestimate them while C-06 overestimates them. We found a slightly higher correlation for C-06 (0.79) than for C-51 (0.74) over the Solar Village. The C-06 retrievals are typically of better quality than those of C-51 with a smaller root mean square error (RMSE) and mean absolute error (MAE) and more AODs fall within the expected error range and relative mean bias. Overall, both the C-51 and C-06 MODIS db algorithms show significant uncertainties and errors. The errors in AOD measurements arise due to imperfect aerosol model schemes and underestimation of surface reflectance over the Solar Village. This study suggests that further quantitative research is required to provide better estimates of satellite-based AOD over Saudi Arabia.
Land surface reflectance (LSR) and aerosol types are the two main factors that affect aerosol inversions over land. According to LSR determination methods, Moderate resolution Imaging Spectroradiometer (MODIS) aerosol...
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Land surface reflectance (LSR) and aerosol types are the two main factors that affect aerosol inversions over land. According to LSR determination methods, Moderate resolution Imaging Spectroradiometer (MODIS) aerosol products are produced using the Deep Blue (db) and Dark Target (DT) algorithms. Five aerosol types that are determined from Aerosol Robotic Network (AERONET) ground measurements are used to describe the global distribution of aerosol types in each algorithm. To assess the influence of LSR and the method used to determine aerosol type from aerosol retrievals, 10-km global aerosol products that cover 2013 are selected for validation using Level 2.0 aerosol observations from 175 AERONET sites. The variations in the retrieval accuracy of the db and DT algorithms for different LSR values are analyzed by combining them with a global 10-km LSR database. Meanwhile, the adaptability of the MODIS products over areas covered with different aerosols is also explored. The results are as follows. (1) Compared with DT retrievals, the db algorithm yields lower root mean squared error (RMSE) and mean absolut error (MAE) values, and a greater number of appropriate sample points fall within the expected error (EE). The db algorithm shows higher overall reliability;(2) The aerosol retrieval accuracy of the db and DT algorithms decline irregularly as the surface reflectance increases;the db algorithm displays relatively high accuracy;(3) Both algorithms have a high retrieval accuracy over areas covered by weak absorbing aerosols, whereas dust aerosols and continental aerosols produce a low retrieval accuracy. The db algorithm shows good retrieval results for most aerosols, but a lower accuracy for strong absorbing aerosols.
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