AIDEDNet: anti-interference and detail enhancement dehazing network for real-world scenes

The haze phenomenon seriously interferes the image acquisition and reduces image quality. Due to many uncertain factors, dehazing is typically a challenge in image processing. The most existing deep learning-based dehazing approaches apply the atmospheric scattering model (ASM) or a similar physical model, which originally comes from traditional dehazing methods. However, the data set trained in deep learning does not match well this model for three reasons. Firstly, the atmospheric illumination in ASM is obtained from prior experience, which is not accurate for dehazing real-scene. Secondly, it is difficult to get the depth of outdoor scenes for ASM. Thirdly, the haze is a complex natural phenomenon, and it is difficult to find an accurate physical model and related parameters to describe this phenomenon. In this paper, we propose a black box method, in which the haze is considered an image quality problem without using any physical model such as ASM. Analytically, we propose a novel dehazing equation to combine two mechanisms: interference item and detail enhancement item. The interference item estimates the haze information for dehazing the image, and then the detail enhancement item can repair and enhance the details of the dehazed image. Based on the new equation, we design an anti-interference and detail enhancement dehazing network (AIDEDNet), which is dramatically different from existing dehazing networks in that our network is fed into the haze-free images for training. Specifically, we propose a new way to construct a haze patch on the flight of network training. The patch is randomly selected from the input images and the thickness of haze is also randomly set. Numerous experiment results show that AIDEDNet outperforms the state-of-the-art methods on both synthetic haze scenes and real-world haze scenes.     

1001 Acquisition viewpoints: efficient and versatile view-dependent modeling of real-world scenes

Modeling is a severe bottleneck for computer graphics applications. Manual modeling is time consuming and fails to capture the complexity of real-world scenes. Automated modeling based on acquiring color and depth data is a promising alternative. However, the usual approach of densely sampling the scene from a few viewpoints suffers from long acquisition times, high data redundancy, and lack of robustness, leading to incomplete models. We propose automated modeling based on sampling the scene sparsely from a dense set of viewpoints. We show that the sparse data quickly accumulates to generate models with good scene coverage. The sparse depth is acquired efficiently and robustly, which enables an interactive, operator-in-the-loop acquisition pipeline. We describe a modeling system that implements this approach. The system acquires scenes with complex geometry and complex reflective properties from thousands of viewpoints in minutes. The resulting models are compact and support photorealistic rendering at interactive rates.     

Saharan dust in nighttime thermal imagery: Detection and reduction of related biases in retrieved sea surface temperature

Guided by radiative transfer modelling of the effects of Saharan dust (aerosol) on brightness temperatures at 3.9, 8.7, 11 and 12 μm measured by the Spinning Enhanced Visible and Infrared Imager (SEVIRI), we propose an indicator of the presence of Saharan dust in nighttime (infrared-only) SEVIRI imagery. Radiative transfer modelling is performed using a fast atmospheric transmittance model and a delta-Eddington approximation for scattering. Using aerosol single scattering properties appropriate to simulation of Saharan dust gives qualitatively good simulations of dust-affected brightness temperatures. From these simulations, we show that aerosol-free brightness temperatures are tightly clustered around the principal axis (explaining 99% of variance) in a 3-dimensional brightness-temperature difference space (the axes being 3.9 μm minus 8.7 μm, 3.9 μm minus 12 μm, and 11 μm minus 12 μm); in contrast, brightness temperatures affected by aerosol are simulated to be significantly off-axis when transformed into this space. Although the detailed effects of aerosol on brightness temperatures are not quantitatively reproduced by our simulations, this off-axis characteristic is empirically found to be also true of Saharan-dust-affected brightness temperatures in observations. The second principal component of the brightness-temperature difference space is identified as a useful index for Saharan dust. Comparisons with independent satellite measurements of aerosol optical depth (AOD, at 0.55 μm) show that the new Saharan dust index (SDI) is loosely correlated with AOD (r = 0.34). The correlation is loose because the SDI is also sensitive to the height distribution of aerosol, which affects the aerosol's thermal emission. The SDI is used to develop an empirical correction scheme for SST retrievals affected by Saharan dust. Application of the SDI correction scheme removes an independent error of 0.2 K from SEVIRI SSTs validated against buoys from all latitudes.     

气象卫星夜间微光云图和红外云图的融合技术研究

将多分辨率分析融合方法和多尺度几何分析融合方法应用于DMSP气象卫星夜间微光云图和红外云图的融合中,并对各种融合算法的性能进行了客观评价.融合实验结果显示,融合图像与源图像相比取得了良好的视觉效果.从平均互信息和Xydeas-Petrovic指标看,拉普拉斯金字塔融合算法和非下采样Contourlet融合算法的融合效果较其他方法优越.     

Fusion of visible and infrared imagery for night color vision

A combined approach for fusing night-time infrared with visible imagery is presented in this paper. Night color vision is thus accomplished and the final scene has a natural day-time color appearance. Fusion is based either on non-negative matrix factorization or on a transformation that takes into consideration perceptual attributes. The final obtained color images possess a natural day-time color appearance due to the application of a color transfer technique. In this way inappropriate color mappings are avoided and the overall discrimination capabilities are enhanced. Two different data sets are employed and the experimental results establish the overall method as being efficient, compact and perceptually meaningful.     

Deep-PHURIE: deep learning based hurricane intensity estimation from infrared satellite imagery

Hurricanes are among the most destructive natural phenomena on Earth. Timely prediction and tracking of hurricane intensity is important as it can help authorities in emergency planning. Several manual, semi and fully automated techniques based on different principles have been developed for hurricane intensity estimation. In this paper, a deep convolutional neural network architecture is proposed for fully automated hurricane intensity estimation from satellite infrared (IR) images. The proposed architecture is robust to errors in annotation of the storm center with a smaller root-mean-squared error (RMSE) (8.82 knots) in comparison to the previous state of the art methods. A web server implementation of Deep-PHURIE and its pre-trained neural network model are available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#Deep-PHURIE.

     

Pedestrian detection and tracking in infrared imagery using shape and appearance

In this paper, we present an approach toward pedestrian detection and tracking from infrared imagery using joint shape and appearance cues. A layered representation is first introduced and a generalized expectation-maximization (EM) algorithm is developed to separate infrared images into background (still) and foreground (moving) layers regardless of camera panning. In the two-pass scheme of detecting pedestrians from the foreground layer: shape cue is first used to eliminate non-pedestrian moving objects and then appearance cue helps to locate the exact position of pedestrians. Templates with varying sizes are sequentially applied to detect pedestrians at multiple scales to accommodate different camera distances. To facilitate the task of pedestrian tracking, we formulate the problem of shot segmentation and present a graph matching-based tracking algorithm that jointly exploits the shape, appearance and distance information. Experimental results with both OSU Infrared Image Database and WVU Infrared Video Database are reported to demonstrate the accuracy and robustness of our algorithm.     

Situational awareness ability and cognitive skills training in a complex real-world task

Successful performance in complex dynamic environments depends on domain-dependent factors, such as situational awareness (SA). Underlying SA in a domain are domain-independent cognitive abilities in perception, memory, attention and executive control. Individuals with lower underlying ability perform relatively poorly in complex dynamic real-world tasks. The first experiment examined whether cognitive skills training could overcome limitations in underlying SA ability that impact on complex dynamic task performance. Participants were taught a mix of cognitive management strategies (e.g. divided and focused attention and visual search) in a simulated air traffic control task. A second experiment investigated the link between underlying SA ability, TRACON and SAGAT, a widely used measure of domain-specific SA. In a third experiment, the focus was on encouraging participants to plan ahead and consider the interrelations of elements (aircraft) in the environment. Whilst both training methods ameliorated the negative impact that lower SA ability had on complex dynamic task performance, the results of the third study indicated that this may have been achieved through improved planning behaviour. Finally, participants with higher underlying SA ability performed well irrespective of training condition.     

Situational awareness ability and cognitive skills training in a complex real-world task

Successful performance in complex dynamic environments depends on domain-dependent factors, such as situational awareness (SA). Underlying SA in a domain are domain-independent cognitive abilities in perception, memory, attention and executive control. Individuals with lower underlying ability perform relatively poorly in complex dynamic real-world tasks. The first experiment examined whether cognitive skills training could overcome limitations in underlying SA ability that impact on complex dynamic task performance. Participants were taught a mix of cognitive management strategies (e.g. divided and focused attention and visual search) in a simulated air traffic control task. A second experiment investigated the link between underlying SA ability, TRACON and SAGAT, a widely used measure of domain-specific SA. In a third experiment, the focus was on encouraging participants to plan ahead and consider the interrelations of elements (aircraft) in the environment. Whilst both training methods ameliorated the negative impact that lower SA ability had on complex dynamic task performance, the results of the third study indicated that this may have been achieved through improved planning behaviour. Finally, participants with higher underlying SA ability performed well irrespective of training condition.     

Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with Gaussian and bilateral filters

In order to achieve perceptually better fusion of infrared (IR) and visible images than conventional pixel-level fusion algorithms based on multi-scale decomposition (MSD), we present a novel multi-scale fusion method based on a hybrid multi-scale decomposition (hybrid-MSD). The proposed hybrid-MSD transform decomposes the source images into multi-scale texture details and edge features by jointly using multi-scale Gaussian and bilateral filters. This transform enables to better capture important multi-scale IR spectral features and separate fine-scale texture details from large-scale edge features. As a result, we can use it to achieve better fusion result for human visual perception than those obtained from conventional multi-scale fusion methods, by injecting the multi-scale IR spectral features into the visible image, while preserving (or properly enhancing) important perceptual cues of the background scenery and details from the visible image. In the decomposed information fusion process, three different combination algorithms are adaptively used in accordance to different scale levels (i.e., the small-scale levels, the large-scale levels and the base level). A regularization parameter is introduced to control the relative amount of IR spectral information injected into the visible image in a soft manner, which can be adjusted further depending on user preferences. Moreover, by testing different settings of the parameter, we demonstrate that injecting a moderate amount of IR spectral information with this parameter can actually make the fused images visually better for some infrared and visible source images. Experimental results of both objective assessment and subjective evaluation by human observers also prove the superiority of the proposed method compared with conventional MSD-based fusion methods.     

Integrating visible, near-infrared and short-wave infrared hyperspectral and multispectral thermal imagery for geological mapping at Cuprite, Nevada

This study investigated the potential value of integrating hyperspectral visible, near-infrared, and short-wave infrared imagery with multispectral thermal data for geological mapping. Two coregistered aerial data sets of Cuprite, Nevada were used: Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data, and MODIS/ASTER Airborne Simulator (MASTER) multispectral thermal data. Four classification methods were each applied to AVIRIS, MASTER, and a combined set. Confusion matrices were used to assess the classification accuracy. The assessment showed, in terms of kappa coefficient, that most classification methods applied to the combined data achieved a marked improvement compared to the results using either AVIRIS or MASTER thermal infrared (TIR) data alone. Spectral angle mapper (SAM) showed the best overall classification performance. Minimum distance classification had the second best accuracy, followed by spectral feature fitting (SFF) and maximum likelihood classification. The results of the study showed that SFF applied to the combination of AVIRIS with MASTER TIR data are especially valuable for identification of silicified alteration and quartzite, both of which exhibit distinctive features in the TIR region. SAM showed some advantages over SFF in dealing with multispectral TIR data, obtaining higher accuracy in discriminating low albedo volcanic rocks and limestone which do not have unique, distinguishing features in the TIR region.     

Integrating visible,near-infrared and short-wave infrared hyperspectral and multispectral thermal imagery for geological mapping at Cuprite,Nevada: a rule-based system

Previous research has shown that integrating hyperspectral visible and near-infrared (VNIR) / short-wave infrared (SWIR) with multispectral thermal infrared (TIR) data can lead to improved mineral and rock identification. However, inconsistent results were found regarding the relative accuracies of different classification methods for dealing with the integrated data set. In this study, a rule-based system was developed for integration of VNIR/SWIR hyperspectral data with TIR multispectral data and evaluated using a case study of Cuprite, Nevada. Previous geological mapping, supplemented by field work and sample spectral measurements, was used to develop a generalized knowledge base for analysis of both spectral reflectance and spectral emissivity. The characteristic absorption features, albedo and the location of the spectral emissivity minimum were used to construct the decision rules. A continuum removal algorithm was used to identify absorption features from VNIR/SWIR hyperspectral data only; spectral angle mapper (SAM) and spectral feature fitting (SFF) algorithms were used to estimate the most likely rock type. The rule-based system was found to achieve a notably higher performance than the SAM, SFF, minimum distance and maximum likelihood classification methods on their own.     

On the interpretation and classification of mesoscale cyclones from satellite infrared imagery

Subjective interpretation of cloud imagery is commonly used to identify mesoscale cyclones in cold air streams (mesocyclones) over the southern oceans. To determine mesocyclone attributes, and evaluate the classification of their cloud vortex signatures, Defense Meteorological Satellite Program ( DMSP) Infrared (IR) imagery is analysed for selected transition and winter season months of 1988 and 1989. Mesocyclones occupy a statistically smaller size range compared with synoptic scale ( frontal) vortices, and have maximum frequencies of occurrence in transition season months. The mesocyclone classification scheme separates the different signature types most reliably in winter, which is also the season when they are most frequently represented on hemispheric-scale synoptic chart analyses. Characteristic patterns of cloud form and level associated with vortex types, provide insights into mesocyclone dynamics that are now being evaluated using microwave techniques.     

Variability of the Columbia River plume observed in visible and infrared satellite imagery

Abstract

Variability of the Columbia River plume in coastal waters off the northwestern United States, 1979-1985, was observed in sea surface temperature and phytopiankton pigment images derived from Advanced Very High Resolution Radiometer and Coastal Zone Colour Scanner data. The orientation, shape, intensity and relative temperature of the plume vary in response to coastal winds and wind-driven surface currents. From October to April, plume water is oriented northward along the coast. Following the spring transition in April or May, the plume is oriented southward, either adjacent to the coast or offshore. Transition between the winter and summer forms can be observed in the satellite imagery. Brief reversals of the prevailing seasonal winds cause rapid changes in the orientation and shape of the plume. Remote sensing of the Columbia River plume offers valuable information for oceanographic studies and fisheries management in the region. Derivation of an appropriate visible-infrared signature for plume waters and tracking of tidal pulses in the plume is suggested as a promising direction for future research.     

Hydrological fronts seen in visible and infrared MODIS imagery of the Black Sea

This article presents an examination of hydrological fronts in the Black Sea based on visible and infrared Aqua Moderate Resolution Imaging Spectrometer (MODIS) imagery obtained during 2008–2013. First the images were preliminarily treated (e.g. via applying a median filter) and then the fronts were detected using the Sobel operator. Further processing of the fields of the gradient norm thus obtained helped determine the generalized results of the front density for different years and seasons. On analysis of such results it was shown that the strongest thermal and optical fronts in the Black Sea are located in the near-coastal area. Nevertheless, analysis of the front density contrasts in the open sea was discovered to be very helpful as well, because it provided important information on the trajectories of the main macro- and mesoscale circulation patterns in the basin such as the Rim Current, Batumi eddy, and eddies of the Anatolian coast.     

On the analysis of thermal infrared imagery: The limited utility of apparent thermal inertia

A spectral window in the thermal infrared permits observations of surface temperature by satellite radiometry. The Heat Capacity Mapping Mission (HCMM) acquired 10–12 μm data at times of day favorable for estimation of surface thermal properties and the surface energy budget. Two variables, surface wetness, which controls evaporation and hence mean surface temperature, and thermal inertia, which relates the diurnal excursion of surface temperature to ground heat flux, are responsible for most observed temperature variability. These variables may be estimated from the mid night (2:30 a.m.) and early afternoon (1:30 p.m.) data from the HCMM or from the afternoon NOAA satellites. However, the HCMM data product, “apparent thermal inertia,” is potentially misleading in agricultural areas because surface evaporation reduces the amplitude of the soil heat flux compared to the amplitude in dry areas. Thus apparent thermal inertia should not be used in regions having variability in surface moisture.     

Detection and tracking of oil slicks on sun-glittered visible and near infrared satellite imagery

The use of the visible and near infrared (VNIR) bands of MODIS and MERIS imaging sensors acquired in sunglint conditions to reveal smoothed regions such as those affected by oil pollution is investigated. The underlying physical mechanism that enables oil slick detection is based on the modification of the surface slope distribution composing the wind-roughened sea due to the action of mineral oils. The role of sunglint as the chief mechanism that allows the imaging of oil slick features with VNIR wavelengths is assessed for selected case studies in the Mediterranean Sea. The high rate of acquisition and the frequent occurrence of MODIS and MERIS imagery affected by sunglint, especially in low-latitude seas, can thus significantly contribute to increase the actual oil slick detection capability offered by synthetic aperture radar (SAR) systems. We also show how the combined observations from any of the microwave and optical sensors permit the slick to be followed during its movement. Finally, a simulation study specific to the Mediterranean Sea was carried out in order to demonstrate the feasibility of such an approach supporting SAR observations.     

Challenges of real-world reinforcement learning: definitions,benchmarks and analysis

Machine Learning - Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is beginning to show some successes in real-world scenarios. However, much of the research...     

Non-stationary eddies in the Black Sea as seen by satellite infrared and visible imagery

Examination of non-stationary eddies in the Black Sea is presented based on satellite visible and infrared imagery. The images were obtained by the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectrometer (MODIS) between September 2004 and December 2010. As a result of the analysis performed, it was discovered that there are four main non-stationary eddy types which can be frequently observed in such imagery: near-shore anticyclonic eddies, mushroom-like currents, eddies of the Anatolian coast, and eddy chains. For each type of eddy, spatio-temporal parameters were retrieved such as areas of the most frequent generation and typical length scale, as well as their seasonality. Every non-stationary eddy type was shown to have its own area of location. Analysis of eddy spatial scale revealed that anticyclonic eddies of all types were greater in size than cyclonic ones (on average 52 km for anticyclones and 36 km for cyclones).