Shadow Locations Research Articles

Warming impact of black carbon aerosols on the direct radiative forcing over a semi-arid location, Solapur in India

In this study, we investigated the aerosol radiative forcing (ARF) using ground-based measurements of PM2.5 and black carbon aerosols at a semi-arid, rain shadow location, Solapur in peninsular India. It is observed that aerosols caused a net cooling effect at top of the atmosphere (TOP) indicating that the aerosols reflect more solar radiation back to space than they absorb. At the surface, the aerosols caused a net cooling effect indicating more presence of scattering type aerosols. The resulting ARF of the aerosols was found to be ranging from +38 Wm-2 in monsoon to +53 Wm-2 in pre-monsoon indicating trapping of energy which resulted in a warming of the atmosphere. However, BC –only forcing indicated a significant warming effect at TOP as well as in the atmosphere which showed the potential of the absorbing carbonaceous aerosols. Overall, BC was responsible for 44% and 32% of the composite ARF, even though it formed only 7% and 2% of composite aerosol in the dry and wet periods, respectively. The warming impact of BC aerosols was also manifested in terms of their contribution to aerosol radiative forcing efficiency (ARFE) which was about four times more for BC-only than that for composite aerosols. More atmospheric heating rates were observed during dry periods for composite and BC-only aerosols than during wet period. These findings have important implications for aerosol-cloud-precipitation studies as well as the atmospheric thermodynamics and hydrological cycle over this semi-arid region where the total aerosol load is not significant and rainfall amount is scarce.

Distance mis-estimations can be reduced with specific shadow locations

Shadows in physical space are copious, yet the impact of specific shadow placement and their abundance is yet to be determined in virtual environments. This experiment aimed to identify whether a target’s shadow was used as a distance indicator in the presence of binocular distance cues. Six lighting conditions were created and presented in virtual reality for participants to perform a perceptual matching task. The task was repeated in a cluttered and sparse environment, where the number of cast shadows (and their placement) varied. Performance in this task was measured by the directional bias of distance estimates and variability of responses. No significant difference was found between the sparse and cluttered environments, however due to the large amount of variance, one explanation is that some participants utilised the clutter objects as anchors to aid them, while others found them distracting. Under-setting of distances was found in all conditions and environments, as predicted. Having an ambient light source produced the most variable and inaccurate estimates of distance, whereas lighting positioned above the target reduced the mis-estimation of distances perceived.

Analysis of the Effectiveness of Generative Adversarial Technology in Intelligent Buildings in terms of Design

Abstract This paper combines the training method of generative adversarial network and the robust row layout design method of building based on CGAN, and selects pix2pix model to carry out the spatial layout design of intelligent building, to explore the actual effect of intelligent building design. Using the building layout design of a generative adversarial network, different parameters and residual block numbers are set to compare the impact of building texture generation. The intelligent building using GAN technology is selected to carry out the spatial design characterization of both Light and shadow and luminance, and the SD spatial environment evaluation factor is used to refine the discussion of the spatial design experience of intelligent buildings designed using generative adversarial technology. The Light and shadow distribution of intelligent building design is mainly presented as four types, in which the light and shadow location types of reading room, hall, rest space and exhibition hall are primarily offered as horizontal division types. In a public intelligent building, the rest area is separated into 25% of the distance and 25% of the vertical division, with an improved design balance.

Development of Chest X-ray Image Evaluation Software Using the Deep Learning Techniques

Although the widespread use of digital imaging has enabled real-time image display, images in chest X-ray examinations can be confirmed by the radiologist’s eyes. Considering the development of deep learning (DL) technology, its application will make it possible to immediately determine the need for a retake, which is expected to further improve examination throughput. In this study, we developed software for evaluating chest X-ray images to determine whether a repeat radiographic examination is necessary, based on the combined application of DL technologies, and evaluated its accuracy. The target population was 4809 chest images from a public database. Three classification models (CLMs) for lung field defects, obstacle shadows, and the location of obstacle shadows and a semantic segmentation model (SSM) for the lung field regions were developed using a fivefold cross validation. The CLM was evaluated using the overall accuracy in the confusion matrix, the SSM was evaluated using the mean intersection over union (mIoU), and the DL technology-combined software was evaluated using the total response time on this software (RT) per image for each model. The results of each CLM with respect to lung field defects, obstacle shadows, and obstacle shadow location were 89.8%, 91.7%, and 91.2%, respectively. The mIoU of the SSM was 0.920, and the software RT was 3.64 × 10−2 s. These results indicate that the software can immediately and accurately determine whether a chest image needs to be re-scanned.

Significant abundances of alkaline components in the fine and coarse aerosols over a tropical rain shadow location in peninsular India

Significant abundances of alkaline components in the fine and coarse aerosols over a tropical rain shadow location in peninsular India

SD-CapsNet: A Siamese Dense Capsule Network for SAR Image Registration with Complex Scenes

SAR image registration is the basis for applications such as change detection, image fusion, and three-dimensional reconstruction. Although CNN-based SAR image registration methods have achieved competitive results, they are insensitive to small displacement errors in matched point pairs and do not provide a comprehensive description of keypoint information in complex scenes. In addition, existing keypoint detectors are unable to obtain a uniform distribution of keypoints in SAR images with complex scenes. In this paper, we propose a texture constraint-based phase congruency (TCPC) keypoint detector that uses a rotation-invariant local binary pattern operator (RI-LBP) to remove keypoints that may be located at overlay or shadow locations. Then, we propose a Siamese dense capsule network (SD-CapsNet) to extract more accurate feature descriptors. Then, we define and verify that the feature descriptors in capsule form contain intensity, texture, orientation, and structure information that is useful for SAR image registration. In addition, we define a novel distance metric for the feature descriptors in capsule form and feed it into the Hard L2 loss function for model training. Experimental results for six pairs of SAR images demonstrate that, compared to other state-of-the-art methods, our proposed method achieves more robust results in complex scenes, with the number of correctly matched keypoint pairs (NCM) at least 2 to 3 times higher than the comparison methods, a root mean square error (RMSE) at most 0.27 lower than the compared methods.

Analyzing a series of thermal infrared images to identify defects using a hybrid approach that combines robust principal component analysis and image segmentation

Thermography is widely used to identify defects; however, analyzing just the images containing important information instead of analyzing each individual image from a series of thermal infrared images is an important capability. Principal component analysis (PCA) is the traditionally used dimensionality reduction approach for projecting a given dataset onto a low-dimensional space. In doing so, several images can be extracted from this low-dimensional space that contain significant information about the given data. The problem with this is that PCA is sensitive to noise and shadows. Robust PCA (RPCA), Sparse PCA (SPCA), and Kernel PCA (KPCA) have therefore been developed and employed for defect detection. The question then arises as to which method can preserve the most information from a series of thermal infrared images after the data are projected onto the low-dimensional space. This study analyzed a series of thermal infrared images covered with shadows using different PCAs. By comparing the results, it was found that RPCA can preserve the most information of the given data. Image segmentation was employed to segment the extracted images such that the thermal patterns could be easily identified from the segmented regions. The shadow locations could be identified by selecting the areas whose shadow effects estimated using image segmentation were less than 1.0. The results were encouraging and the proposed scheme can be feasibly employed for defect detection.

ShadowDeNet: A Moving Target Shadow Detection Network for Video SAR

Most existing SAR moving target shadow detectors not only tend to generate missed detections because of their limited feature extraction capacity among complex scenes, but also tend to bring about numerous perishing false alarms due to their poor foreground–background discrimination capacity. Therefore, to solve these problems, this paper proposes a novel deep learning network called “ShadowDeNet” for better shadow detection of moving ground targets on video synthetic aperture radar (SAR) images. It utilizes five major tools to guarantee its superior detection performance, i.e., (1) histogram equalization shadow enhancement (HESE) for enhancing shadow saliency to facilitate feature extraction, (2) transformer self-attention mechanism (TSAM) for focusing on regions of interests to suppress clutter interferences, (3) shape deformation adaptive learning (SDAL) for learning moving target deformed shadows to conquer motion speed variations, (4) semantic-guided anchor-adaptive learning (SGAAL) for generating optimized anchors to match shadow location and shape, and (5) online hard-example mining (OHEM) for selecting typical difficult negative samples to improve background discrimination capacity. We conduct extensive ablation studies to confirm the effectiveness of the above each contribution. We perform experiments on the public Sandia National Laboratories (SNL) video SAR data. Experimental results reveal the state-of-the-art performance of ShadowDeNet, with a 66.01% best f1 accuracy, in contrast to the other five competitive methods. Specifically, ShadowDeNet is superior to the experimental baseline Faster R-CNN by a 9.00% f1 accuracy, and superior to the existing first-best model by a 4.96% f1 accuracy. Furthermore, ShadowDeNet merely sacrifices a slight detection speed in an acceptable range.

Research on moving object shadow removal algorithm based on video surveillance

In order to solve the problem of accurate shadow location for moving objects, a shadow removal algorithm based on cross correlation and local maximum information entropy is proposed. Firstly, the background is established by using the mixed Gaussian model to obtain the moving object; Then, the dark part of the moving object is detected by cross correlation; Finally, the local maximum information entropy is used to analyze the texture characteristics of the dark part, and then realization the shadow remove of the moving object. The experimental results show that the algorithm is robust to some extent.

Cloud shadow detection removal for satellite supportive health care systems: research solution towards Australian Bushfire

Shadows limit numerous remote detecting applications, for example, characterization, target identification, and change discovery. Shadow recognition in high spatial goals remote detecting image is basic for finding land targets. Most current shadow location techniques use the histogram edge of unearthly qualities to recognize the shadows and nonshadows legitimately called hard parallel shadow. In this paper, we proposed another shadow identification calculation utilizing the HSI shading model and Daubechies complex wavelet change (DCWT). Since the pixel grid is a largescale framework, in the event that we apply calculation legitimately on the crude pixel space, it will be calculation escalated to compute the likeness network. Clearly, the exhibition of edge put together techniques vigorously depend with respect to the chose limit. All the while, these limit based strategies do not consider any spatial data. To beat these weaknesses, a delicate shadow portrayal strategy is created by bringing the idea of darkness into shadow identification, and technique is proposed so as to utilize neighborhood data. To take care of this issue, we isolate the lattice into a few squares and afterward applying calculation to distinguish shadows in H, S and I segments individually. At that point, three identified images are melded to get a last shadow recognition result. Near tests are performed for Kmeans and edge division techniques. The trial results show that higher discovery precision of the proposed approach is acquired, and it can take care of the issues of bogus excusals of K-means and limit division technique. Tests on remote detecting images have demonstrated that the proposed technique can acquire progressively precise identification results.

Automatic Segmentation and Visualization of Choroid in OCT with Knowledge Infused Deep Learning.

The choroid provides oxygen and nourishment to the outer retina thus is related to the pathology of various ocular diseases. Optical coherence tomography (OCT) is advantageous in visualizing and quantifying the choroid in vivo. However, its application in the study of the choroid is still limited for two reasons. (1) The lower boundary of the choroid (choroid-sclera interface) in OCT is fuzzy, which makes the automatic segmentation difficult and inaccurate. (2) The visualization of the choroid is hindered by the vessel shadows from the superficial layers of the inner retina. In this paper, we propose to incorporate medical and imaging prior knowledge with deep learning to address these two problems. We propose a biomarker-infused global-to-local network (Bio-Net) for the choroid segmentation, which not only regularizes the segmentation via predicted choroid thickness, but also leverages a global-to-local segmentation strategy to provide global structure information and suppress overfitting. For eliminating the retinal vessel shadows, we propose a deep-learning pipeline, which firstly locate the shadows using their projection on the retinal pigment epithelium layer, then the contents of the choroidal vasculature at the shadow locations are predicted with an edge-to-texture generative adversarial inpainting network. The results show our method outperforms the existing methods on both tasks. We further apply the proposed method in a clinical prospective study for understanding the pathology of glaucoma, which demonstrates its capacity in detecting the structure and vascular changes of the choroid related to the elevation of intra-ocular pressure.

Refined UNet: UNet-Based Refinement Network for Cloud and Shadow Precise Segmentation

Formulated as a pixel-level labeling task, data-driven neural segmentation models for cloud and corresponding shadow detection have achieved a promising accomplishment in remote sensing imagery processing. The limited capability of these methods to delineate the boundaries of clouds and shadows, however, is still referred to as a central issue of precise cloud and shadow detection. In this paper, we focus on the issue of rough cloud and shadow location and fine-grained boundary refinement of clouds on the dataset of Landsat8 OLI and therefore propose the Refined UNet to achieve this goal. To this end, a data-driven UNet-based coarse prediction and a fully-connected conditional random field (Dense CRF) are concatenated to achieve precise detection. Specifically, the UNet network with adaptive weights of balancing categories is trained from scratch, which can locate the clouds and cloud shadows roughly, while correspondingly the Dense CRF is employed to refine the cloud boundaries. Eventually, Refined UNet can give cloud and shadow proposals sharper and more precisely. The experiments and results illustrate that our model can propose sharper and more precise cloud and shadow segmentation proposals than the ground truths do. Additionally, evaluations on the Landsat 8 OLI imagery dataset of Blue, Green, Red, and NIR bands illustrate that our model can be applied to feasibly segment clouds and shadows on the four-band imagery data.

Deep Learning to Forecast Solar Irradiance Using a Six-Month UTSA SkyImager Dataset

Distributed PV power generation necessitates both intra-hour and day-ahead forecasting of solar irradiance. The UTSA SkyImager is an inexpensive all-sky imaging system built using a Raspberry Pi computer with camera. Reconfigurable for different operational environments, it has been deployed at the National Renewable Energy Laboratory (NREL), Joint Base San Antonio, and two locations in the Canary Islands. The original design used optical flow to extrapolate cloud positions, followed by ray-tracing to predict shadow locations on solar panels. The latter problem is mathematically ill-posed. This paper details an alternative strategy that uses artificial intelligence (AI) to forecast irradiance directly from an extracted subimage surrounding the sun. Several different AI models are compared including Deep Learning and Gradient Boosted Trees. Results and error metrics are presented for a total of 147 days of NREL data collected during the period from October 2015 to May 2016.

Hybrid Invasive Weed Optimization Algorithm for Parameter Inversion Problems

A hybrid invasive weed optimization (HIWO) algorithm based on the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm is proposed for the problems on parameter inversion of the nonlinear models of sun shadow with integer variables in the study. Our presented algorithm can take full advantage of the local search ability of BFGS algorithm and the global search ability of invasive weed optimization (IWO) algorithm. The HIWO algorithm can not only reverse the date of sun shadow model successfully, but also conquer the weaknesses that the classic mathematical methods are hard to address integer nonlinear optimization problems by utilizing integers in some random variables from algorithms. The results of numerical experiments demonstrate that the HIWO algorithm has not only high computing accuracy, but also fast convergence speed. It can effectively improve the accuracy and efficiency of the techniques of sun shadow location, and afford an effective and efficient technique to handle the issues of integer parameter inversion in engineering applications.

A cloud shadow detection method combined with cloud height iteration and spectral analysis for Landsat 8 OLI data

Although enhanced over prior Landsat instruments, Landsat 8 OLI can obtain very high cloud detection precisions, but for the detection of cloud shadows, it still faces great challenges. Geometry-based cloud shadow detection methods are considered the most effective and are being improved constantly. The Function of Mask (Fmask) cloud shadow detection method is one of the most representative geometry-based methods that has been used for cloud shadow detection with Landsat 8 OLI. However, the Fmask method estimates cloud height employing fixed temperature rates, which are highly uncertain, and errors of large area cloud shadow detection can be caused by errors in estimations of cloud height. This article improves the geometry-based cloud shadow detection method for Landsat OLI from the following two aspects. (1) Cloud height no longer depends on the brightness temperature of the thermal infrared band but uses a possible dynamic range from 200 m to 12,000 m. In this case, cloud shadow is not a specific location but a possible range. Further analysis was carried out in the possible range based on the spectrum to determine cloud shadow location. This effectively avoids the cloud shadow leakage caused by the error in the height determination of a cloud. (2) Object-based and pixel spectral analyses are combined to detect cloud shadows, which can realize cloud shadow detection from two aspects of target scale and pixel scale. Based on the analysis of the spectral differences between the cloud shadow and typical ground objects, the best cloud shadow detection bands of Landsat 8 OLI were determined. The combined use of spectrum and shape can effectively improve the detection precision of cloud shadows produced by thin clouds. Several cloud shadow detection experiments were carried out, and the results were verified by the results of artificial recognition. The results of these experiments indicated that this method can identify cloud shadows in different regions with correct accuracy exceeding 80%, approximately 5% of the areas were wrongly identified, and approximately 10% of the cloud shadow areas were missing. The accuracy of this method is obviously higher than the recognition accuracy of Fmask, which has correct accuracy lower than 60%, and the missing recognition is approximately 40%.

Research and Application of Sun Shadow Location Technology in Video Big Data

This paper presents a sun shadow location model for quickly determining and analyzing the location and time of the photographed object in video big data. Because the length of the object's shadow usually changed, it is related to some factors, such as the solar zenith angle, the solar azimuth angle and the geographical latitude of the object. So it proposes building the location information fingerprint database, which shows mainly the relation between the length of the object’s shadow and time by reasonable assumptions for the interception of the video data. Then, it is very convenient to predict the location and time of the taken object by indexing the target in database. Finally, some experimental results show that the proposed model can accurately locate the object.

BigFoot: static check placement for dynamic race detection

Precise dynamic data race detectors provide strong correctness guarantees but have high overheads because they generally keep analysis state in a separate shadow location for each heap memory location, and they check (and potentially update) the corresponding shadow location on each heap access. The BigFoot dynamic data race detector uses a combination of static and dynamic analysis techniques to coalesce checks and compress shadow locations. With BigFoot, multiple accesses to an object or array often induce a single coalesced check that manipulates a single compressed shadow location, resulting in a performance improvement over FastTrack of 61%.

Postglacial vegetation history of Orcas Island, northwestern Washington

The revegetation of islands following retreat of Pleistocene glaciers is of great biogeographical interest. The San Juan Islands, Washington, feature regionally distinctive xerophytic plant communities, yet their vegetation history, as it relates to past climate and sea level, is poorly known. We describe a 13,700-year-old pollen record from Killebrew Lake Fen and compare the vegetation reconstruction with others from the region. The data suggest that the narrow channels surrounding Orcas Island were not a barrier to early postglacial immigration of plants. Between 13,700 and 12,000 cal yr BP, Pinus, Tsuga, Picea, Alnus viridis, and possibly Juniperus maritima were present in a mosaic that supported Bison antiquus and Megalonyx. The rise of Alnus rubra-type pollen and Pteridium spores at ca. 12,000 cal yr BP suggests a warming trend and probably more fires. Temperate conifer taxa, including Cupressaceae, Pseudotsuga, Tsuga heterophylla, and Abies, increased after 11,000 cal yr BP and especially in the last 7000 cal yr BP. After 6000 cal yr BP, Pseudotsuga and Cupressaceae dominated the vegetation. The last 1500 yr were the wettest period of the record. Due to its rain shadow location, Orcas Island experienced drier conditions than on the mainland during most of the postglacial period.

Automated Detection of Cloud and Cloud Shadow in Single-Date Landsat Imagery Using Neural Networks and Spatial Post-Processing

The use of Landsat data to answer ecological questions is greatly increased by the effective removal of cloud and cloud shadow from satellite images. We develop a novel algorithm to identify and classify clouds and cloud shadow, SPARCS: Spatial Procedures for Automated Removal of Cloud and Shadow. The method uses a neural network approach to determine cloud, cloud shadow, water, snow/ice and clear sky classification memberships of each pixel in a Landsat scene. It then applies a series of spatial procedures to resolve pixels with ambiguous membership by using information, such as the membership values of neighboring pixels and an estimate of cloud shadow locations from cloud and solar geometry. In a comparison with FMask, a high-quality cloud and cloud shadow classification algorithm currently available, SPARCS performs favorably, with substantially lower omission errors for cloud shadow (8.0% and 3.2%), only slightly higher omission errors for clouds (0.9% and 1.3%, respectively) and fewer errors of commission (2.6% and 0.3%). Additionally, SPARCS provides a measure of uncertainty in its classification that can be exploited by other algorithms that require clear sky pixels. To illustrate this, we present an application that constructs obstruction-free composites of images acquired on different dates in support of a method for vegetation change detection.

A vision-based blind spot warning system for daytime and nighttime driver assistance

This paper proposes an effective blind spot warning system (BSWS) for daytime and nighttime conditions. The proposed BSWS includes camera models of a dynamic calibration and blind spot detection (BSD) algorithms for the daytime and nighttime. Under daytime conditions, the proposed system presents the Horizontal Edge and Shadow Composite Region (HESCR) method to extract the searching region and to acquire the shadow location of the targeted vehicles. Additionally, to detect vehicles at nighttime road scenes, the proposed system extracts bright objects and recognizes the paired headlights of the targeted vehicles for the BSD. The BSWS is implemented on a DSP-based embedded platform. The results of the BSWS are obtained by conducting practical experiments on our camera-assisted car on a highway in Taiwan under both nighttime and daytime conditions. Experimental results show that the proposed BSWS is feasible for vehicle detection and collision warning in various daytime and nighttime road environments.