Disclosed in the present invention are a bi-color duplex printing method and device. The method includes: acquiring two pages of original four color page dot-matrix according to a printing order every time, extracting effective first color data and second color data in the two pages of original four color page dot-matrix, respectively, and writing the first color data and second color data extracted respectively in first color data, second color data, third color data and fourth color data of a new page of four color page dot-matrix, respectively. The device includes a printing controller, which comprises an original page acquiring module (10) for acquiring two pages of original four color page dot-matrix according to a printing order every time, an extracting module (20) for extracting effective first color data and second color data in the two pages of original four color page dot-matrix, respectively, and a new page generating module (30) for writing the first color data and second color data extracted respectively in first color data, second color data, third color data and fourth color data of a new page of four color page dot-matrix, respectively. The printing efficiency is improved by the method and the device.
The study of flood inundation is significant to human life and social economy. Remote sensing technology has provided an effective way to study the spatial and temporal characteristics of inundation. Remotely sensed images with high temporal resolutions are widely used in mapping inundation. However, mixed pixels do exist due to their relatively low spatial resolutions. One of the most popular approaches to resolve this issue is sub-pixel mapping. In this paper, a novel discrete particle swarm optimization (DPSO) based sub-pixel flood inundation mapping (DPSO-SFIM) method is proposed to achieve an improved accuracy in mapping inundation at a sub-pixel scale. The evaluation criterion for sub-pixel inundation mapping is formulated. The DPSO-SFIM algorithm is developed, including particle discrete encoding, fitness function designing and swarm search strategy. The accuracy of DPSO-SFIM in mapping inundation at a sub-pixel scale was evaluated using Landsat ETM + images from study areas in Australia and China. The results show that DPSO-SFIM consistently outperformed the four traditional SEIM methods in these study areas. A sensitivity analysis of DPSO-SFIM was also carried out to evaluate its performances. It is hoped that the results of this study will enhance the application of medium-low spatial resolution images in inundation detection and mapping, and thereby support the ecological and environmental studies of river basins. (C) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
Shappee, B. J.
Holoien, T. W.-S.
Drout, M. R.
Auchettl, K.
Stritzinger, M. D.
Kochanek, C. S.
Stanek, K. Z.
Shaya, E.
Narayan, G.
Brown, J. S.
Bose, S.
Bersier, D.
Brimacombe, J.
Chen, Ping
Dong, Subo
Holmbo, S.
Katz, B.
Muñoz, J. A.
Mutel, R. L.
Post, R. S.
Prieto, J. L.
Shields, J.
Tallon, D.
Thompson, T. A.
Vallely, P. J.
Villanueva, S.
Denneau, L.
Flewelling, H.
Heinze, A. N.
Smith, K. W.
Stalder, B.
Tonry, J. L.
Weiland, H.
Barclay, T.
Barentsen, G.
Cody, A. M.
Dotson, J.
Foerster, F.
Garnavich, P.
Gully-Santiago, M.
Hedges, C.
Howell, S.
Kasen, D.
Margheim, S.
Mushotzky, R.
Rest, A.
Tucker, B. E.
Villar, A.
Zenteno, A.
Beerman, G.
Bjella, R.
Castillo, G.
Coughlin, J.
Elsaesser, B.
Flynn, S.
Gangopadhyay, R.
Griest, K.
Hanley, M.
Kampmeier, J.
Kloetzel, R.
Kohnert, L.
Labonde, C.
Larsen, R.
Larson, K. A.
McCalmont-Everton, K. M.
McGinn, C.
Migliorini, L.
Moffatt, J.
Muszynski, M.
Nystrom, V.
Osborne, D.
Packard, M.
Peterson, C. A.
Redick, M.
Reedy, L. H.
Ross, S. E.
Spencer, B.
Steward, K.
Cleve, J. E. Van
de Miranda Cardoso, J. Vinícius
Weschler, T.
Wheaton, A.
Bulger, J.
Chambers, K. C.
Flewelling, H. A.
Huber, M. E.
Lowe, T. B.
Magnier, E. A.
Schultz, A. S. B.
Waters, C. Z.
Willman, M.
Baron, E.
Chen, Zhihao
Derkacy, James M.
Huang, Fang
Li, Linyi
Li, Wenxiong
Li, Xue
Mo, Jun
Rui, Liming
Sai, Hanna
Wang, Lifan
Wang, Lingzhi
Wang, Xiaofeng
Xiang, Danfeng
Zhang, Jicheng
Zhang, Jujia
Zhang, Kaicheng
Zhang, Tianmeng
Zhang, Xinghan
Zhao, Xulin
Brown, P. J.
Hermes, J. J.
Nordin, J.
Points, S.
Sódor, A.
Strampelli, G. M.
Zenteno, A.
Sub-pixel mapping of flood inundation (SMFI) is one of the hotspots in remote sensing and relevant research and application fields. In this study, a novel method based on the integration of Bayesian regulation back-propagation neural network (BRBP) and particle swarm optimization (PSO), so-called IBRBPPSO, is proposed for SMFI in river basins. The IBRBPPSO-SMFI algorithm was developed and evaluated using Landsat images fromthe Changjiang river basin in China and the Murray-Darling basin in Australia. Compared with traditional SMFI methods, IBRBPPSO-SMFI consistently achieves the most accurate SMFI results in terms of visual and quantitative evaluations. IBRBPPSO-SMFI is superior to PSO-SMFI with not only an improved accuracy, but also an accelerated convergence speed of the algorithm. IBRBPPSO-SMFI reduces the uncertainty in mapping inundation in river basins by improving the accuracy of SMFI. The result of this study will also enrich the SMFI methodology, and thereby benefit the environmental studies of river basins.
Urban flooding is a serious natural hazard to many cities all over the world, which has dramatic impacts on the urban environment and human life. Urban flooding mapping has practical significance for the prevention and management of urban flood disasters. Remote sensing images with high temporal resolutions are widely used for urban flooding mapping, but have a limitation of relatively low spatial resolutions. In this study, a new method based on a generalized regression neural network (GRNN) is proposed to achieve improved accuracy in super-resolution mapping of urban flooding (SMUF) from remote sensing images. The GRNN-SMUF algorithm was proposed and then assessed using Landsat 5 and Landsat 8 images of Brisbane city in Australia and Wuhan city in China. Compared to three traditional methods, GRNN-SMUF mapped urban flooding more accurately according to both visual and quantitative assessments. The results of this study will improve the accuracy of urban flooding mapping using easily-available remote sensing images with medium-low spatial resolutions and will be propitious to the prevention and management of urban flood disasters.
The rapid development of global industrialization and urbanization has resulted in a great deal of electric power consumption (EPC), which is closely related to economic growth, carbon emissions, and the long-term stability of global climate. This study attempts to detect spatiotemporal dynamics of global EPC using the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime stable light (NSL) data. The global NSL data from 1992 to 2013 were intercalibrated via a modified invariant region (MIR) method. The global EPC at 1 km resolution was then modeled using the intercalibrated NSL data to assess spatiotemporal dynamics of EPC from a global scale down to continental and national scales. The results showed that the MIR method not only reduced the saturated lighted pixels, but also improved the continuity and comparability of the NSL data. An accuracy assessment was undertaken and confined that the intercalibrated NSL data were relatively suitable and accurate for estimating EPC in the world. Spatiotemporal variations of EPC were mainly identified in Europe, North America, and Asia. Special attention should be paid to China where the high grade and high-growth type of EPC covered 0.409% and 1.041% of the total country area during the study period, respectively. The results of this study greatly enhance the understanding of spatiotemporal dynamics of global EPC at the multiple scales. They will provide a scientific evidence base for tracking spatiotemporal dynamics of global EPC. (C) 2016 Elsevier Ltd. All rights reserved.
China's rapid industrialization and urbanization have resulted in a great deal of CO2 (carbon dioxide) emissions, which is closely related to its sustainable development and the long term stability of global climate. This study proposes panel data analysis to model spatiotemporal CO2 emission dynamics at a higher resolution in China by integrating the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime stable light (NSL) data with statistic data of CO2 emissions. Spatiotemporal CO2 emission dynamics were assessed from national scale down to regional and urban agglomeration scales. The evaluation showed that there was a true positive correlation between NSL data and statistic CO2 emissions in China at the provincial level from 1997 to 2012, which could be suitable for estimating CO2 emissions at 1 km resolution. The spatiotemporal CO2 emission dynamics between different regions varied greatly. The high-growth type and high-grade of CO2 emissions were mainly distributed in the Eastern region, Shandong Peninsula and Middle south of Liaoning, with clearly lower concentrations in the Western region, Central region and Sichuan-Chongqing. The results of this study will enhance the understanding of spatiotemporal variations of CO2 emissions in China. They will provide a scientific basis for policy-making on viable CO2 emission mitigation policies. (C) 2015 Elsevier Ltd. All rights reserved.
Super-resolution mapping of urban flood (SMUF) is one of the hotspots in remote sensing and urban environment research. In this letter, a new SMUF method based on the fusion of support vector machine and general regression neural network (FSVMGRNN) was proposed to achieve enhanced performance. An SVM-SMUF algorithm was developed and a fusion criterion was formulated. Then, the FSVMGRNN-SMUF algorithm was developed. The results of FSVMGRNN-SMUF were evaluated using Landsat 8 OLI imagery of two representative cities in China. FSVMGRNN-SMUF yielded the most accurate SMUF results among the five SMUF methods according to visual comparisons and quantitative comparisons. The mapping accuracy of FSVMGRNN-SMUF related to the kernel functions was also analyzed and discussed. The results of this letter will help to boost practical applications of median-low resolution remote sensing images in urban flooding mapping, and to strengthen the means for monitoring and assessing urban flooding disasters.
Creat membership
Sweep WeChat yards
