Rapid, Robust, and Reliable Blind Deconvolution via Nonconvex Optimization

We study the question of reconstructing two signals $f$ and $g$ from their convolution $y = f\ast g$. This problem, known as {\em blind deconvolution}, pervades many areas of science and technology, including astronomy, medical imaging, optics, and wireless communications. A key challenge of this intricate non-convex optimization problem is that it might exhibit many local minima. We present an efficient numerical algorithm that is guaranteed to recover the exact solution, when the number of measurements is (up to log-factors) slightly larger than the information-theoretical minimum, and under reasonable conditions on $f$ and $g$. The proposed regularized gradient descent algorithm converges at a geometric rate and is provably robust in the presence of noise. To the best of our knowledge, our algorithm is the first blind deconvolution algorithm that is numerically efficient, robust against noise, and comes with rigorous recovery guarantees under certain subspace conditions. Moreover, numerical experiments do not only provide empirical verification of our theory, but they also demonstrate that our method yields excellent performance even in situations beyond our theoretical framework

The analytic network processes method & fuzzy cognitive map method in decision making: a comparative study

Decision making problems always puzzled managers and decision makers in enterprises or organizations. There are many useful soft operation research methods could solve the complicated problem. The paper raises two classic approaches ANP (Analytical network process) and FCM (Fuzzy cognitive map). Those two methods have some similarities and differences. In thus, according to different decision problems, people could compare the two approaches’ characteristics and choose the best tool to deal with the problems. At the end of the paper, a case study has been given for us

Improving super-resolution mapping through combining multiple super-resolution land-cover maps

Super-resolution mapping (SRM) is an ill-posed problem, and different SRM algorithms may generate non-identical fine spatial resolution land-cover maps (sub-pixel maps) from the same input coarse spatial resolution image. The output sub-pixels maps may each have differing strengths and weaknesses. A multiple SRM (M-SRM) method that combines the sub-pixel maps obtained from a set of SRM analyses, obtained from a single or multiple set of algorithms, is proposed in this study. Plurality voting, which selects the class with the most votes, is used to label each sub-pixel. In this study, three popular SRM algorithms, namely, the pixel swapping algorithm (PSA), the Hopfield neural network (HNN) algorithm, and Markov random field (MRF) based algorithm, were used. The proposed M-SRM algorithm was validated using two data sets: a simulated multi-spectral image and an airborne visible/infrared imaging spectrometer (AVIRIS) hyperspectral image. Results show that the highest overall accuracies were obtained by M-SRM in all experiments. For example, in the AVIRIS image experiment, the highest overall accuracies of PSA, HNN and MRF were 88.89%, 93.81% and 82.70% respectively, and increased to 95.06%, 95.37% and 85.56% respectively for M-SRM obtained from the multiple PSA, HNN and MRF analyses

A superresolution land-cover change detection method using remotely sensed images with different spatial resolutions

The development of remote sensing has enabled the acquisition of information on land-cover change at different spatial scales. However, a trade-off between spatial and temporal resolutions normally exists. Fine-spatial-resolution images have low temporal resolutions, whereas coarse spatial resolution images have high temporal repetition rates. A novel super-resolution change detection method (SRCD)is proposed to detect land-cover changes at both fine spatial and temporal resolutions with the use of a coarse-resolution image and a fine-resolution land-cover map acquired at different times. SRCD is an iterative method that involves endmember estimation, spectral unmixing, land-cover fraction change detection, and super-resolution land-cover mapping. Both the land-cover change/no-change map and from–to change map at fine spatial resolution can be generated by SRCD. In this study, SRCD was applied to synthetic multispectral image, Moderate-Resolution Imaging Spectroradiometer (MODIS) multispectral image and Landsat-8 Operational Land Imager (OLI) multispectral image. The land-cover from–to change maps are found to have the highest overall accuracy (higher than 85%) in all the three experiments. Most of the changed land-cover patches, which were larger than the coarse-resolution pixel, were correctly detected

Assessing a temporal change strategy for sub-pixel land cover change mapping from multi-scale remote sensing imagery

Remotely sensed imagery is an attractive source of information for mapping and monitoring land cover. Fine spatial resolution imagery is typically acquired infrequently, but fine temporal resolution systems commonly provide coarse spatial resolution imagery. Sub-pixel land cover change mapping is a method that aims to use the advantages of these multiple spatial and temporal resolution sensing systems. This method produces fine spatial and temporal resolution land cover maps, by updating fine spatial resolution land cover maps using coarse spatial resolution remote sensing imagery. A critical issue for sub-pixel land cover change mapping is downscaling coarse spatial resolution fraction maps estimated by soft classification to a fine spatial resolution land cover map. The relationship between a historic fine spatial resolution map and a contemporary fine spatial resolution map to be estimated at a more recent date plays an important role in the downscaling procedure. A change strategy based on the assumption that the change for each land cover class in a coarse spatial resolution pixel is unidirectional was shown to be a promising means to describe this relationship. This paper aims to assess this change strategy by analyzing the factors that affect the accuracy of the change strategy, using six subsets of the National Land Cover Database (NLCD) of USA. The results show that the spatial resolution of coarse pixels, the time interval of the previous fine resolution land cover map and the current coarse spatial resolution images, and the thematic resolution of the used land cover class scheme have considerable influence on the accuracy of the change strategy. The accuracy of the change strategy decreases with the coarsening of spatial resolution, an increase of time interval, and an increase of thematic resolution. The results also indicate that, when the historic land cover map has a 30 m resolution, like the NLCD, the average accuracy of the change strategy is still as high as 92% when the coarse spatial resolution data used had a resolution of ~1000 m, confirming the effectiveness of the change strategy used in sub-pixel land cover change mapping for use with popular remote sensing systems

CFD Simulation of Temperature Field Distribution of the Liquefied Hydrocarbon Spherical Tank Leaking

AbstractLiquefied hydrocarbon is normally stored under high pressure in overheating state in the spherical tank. Once leakage occurs, the liquefied hydrocarbon will quickly gasify and absorb a great deal of heat, making temperature of spherical tank decrease sharply. In order to investigate this process, physical model was established, and the Reynolds time averaged Navier-Stokes equation and k-ɛ turbulent model as the CFD simulation method were used in this study. The temperature distribution of the spherical tank and the environment after spherical tank pipeline leaking was analyzed. The influences of leakage location and leak area on the spherical tank temperature distribution were analyzed, and a meaningful conclusion was obtained. This study could provide theoretical basis and technical support for the safety control of liquefied hydrocarbon spherical tank leakage. © 2012 Published by Elsevier Ltd. Selection and/or peer-review (pre-review) under responsibility of the Capital University of Economics and Business, China Academy of Safety Science and Technology

Water bodies' mapping from Sentinel-2 imagery with Modified Normalized Difference Water Index at 10-m spatial resolution produced by sharpening the swir band

Monitoring open water bodies accurately is an important and basic application in remote sensing. Various water body mapping approaches have been developed to extract water bodies from multispectral images. The method based on the spectral water index, especially the Modified Normalized Difference Water Index (MDNWI) calculated from the green and Shortwave-Infrared (SWIR) bands, is one of the most popular methods. The recently launched Sentinel-2 satellite can provide fine spatial resolution multispectral images. This new dataset is potentially of important significance for regional water bodies' mapping, due to its free access and frequent revisit capabilities. It is noted that the green and SWIR bands of Sentinel-2 have different spatial resolutions of 10 m and 20 m, respectively. Straightforwardly, MNDWI can be produced from Sentinel-2 at the spatial resolution of 20 m, by upscaling the 10-m green band to 20 m correspondingly. This scheme, however, wastes the detailed information available at the 10-m resolution. In this paper, to take full advantage of the 10-m information provided by Sentinel-2 images, a novel 10-m spatial resolution MNDWI is produced from Sentinel-2 images by downscaling the 20-m resolution SWIR band to 10 m based on pan-sharpening. Four popular pan-sharpening algorithms, including Principle Component Analysis (PCA), Intensity Hue Saturation (IHS), High Pass Filter (HPF) and à Trous Wavelet Transform (ATWT), were applied in this study. The performance of the proposed method was assessed experimentally using a Sentinel-2 image located at the Venice coastland. In the experiment, six water indexes, including 10-m NDWI, 20-m MNDWI and 10-m MNDWI, produced by four pan-sharpening algorithms, were compared. Three levels of results, including the sharpened images, the produced MNDWI images and the finally mapped water bodies, were analysed quantitatively. The results showed that MNDWI can enhance water bodies and suppressbuilt-up features more efficiently than NDWI. Moreover, 10-m MNDWIs produced by all four pan-sharpening algorithms can represent more detailed spatial information of water bodies than 20-m MNDWI produced by the original image. Thus, MNDWIs at the 10-m resolution can extract more accurate water body maps than 10-m NDWI and 20-m MNDWI. In addition, although HPF can produce more accurate sharpened images and MNDWI images than the other three benchmark pan-sharpening algorithms, the ATWT algorithm leads to the best 10-m water bodies mapping results. This is no necessary positive connection between the accuracy of the sharpened MNDWI image and the map-level accuracy of the resultant water body maps