Shadow Detection Algorithm Research Articles

Reconstructing Snow Cover under Clouds and Cloud Shadows by Combining Sentinel-2 and Landsat 8 Images in a Mountainous Region

Snow cover is a sensitive indicator of global climate change, and optical images are an important means for monitoring its spatiotemporal changes. Due to the high reflectivity, rapid change, and intense spatial heterogeneity of mountainous snow cover, Sentinel-2 (S2) and Landsat 8 (L8) satellite imagery with both high spatial resolution and spectral resolution have become major data sources. However, optical sensors are more susceptible to cloud cover, and the two satellite images have significant spectral differences, making it challenging to obtain snow cover beneath clouds and cloud shadows (CCSs). Based on our previously published approach for snow reconstruction on S2 images using the Google Earth Engine (GEE), this study introduces two main innovations to reconstruct snow cover: (1) combining S2 and L8 images and choosing different CCS detection methods, and (2) improving the cloud shadow detection algorithm by considering land cover types, thus further improving the mountainous-snow-monitoring ability. The Babao River Basin of the Qilian Mountains in China is chosen as the study area; 399 scenes of S2 and 35 scenes of L8 are selected to analyze the spatiotemporal variations of snow cover from September 2019 to August 2022 in GEE. The results indicate that the snow reconstruction accuracies of both images are relatively high, and the overall accuracies for S2 and L8 are 80.74% and 88.81%, respectively. According to the time-series analysis of three hydrological years, it is found that there is a marked difference in the spatial distribution of snow cover in different hydrological years within the basin, with fluctuations observed overall.

Threshold segmentation based on information fusion for object shadow detection in remote sensing images

In the shadow detection task, the shadow model is usually consistent with the approximate contour of ontology semantics, it is difficult to extract the features of land covered objects or ground pixels, and easy to be confused into foreground objects in gray scale. Therefore, we present to formulate and apply one new threshold segmentation method based on information fusion for object shadow detection in remote sensing images. Firstly, object shadow pixels are screened using intensity and chromaticity information in HSI color space. Secondly, the remote sensing image is carried out by principal component analysis (PCA) to obtain the first principal component. A new shadow index is constructed using the results obtained from HSI and the first principal component. Thirdly, based on the results of the above two information fusion, a threshold segmentation model is established using the improved threshold segmentation algorithm between the maximum and the minimum threshold segmentation algorithm, so as to obtain the final object shadow detection results. Finally, affluent experiments are conducted on the datasets collected from Google Earth. The results show that the proposed object shadow detection algorithm in remote sensing images can achieve better segmentation and detection (more than 95%) effect compared with state-of-the-art methods.

No-reference shadow detection quality assessment via reference learning and multi-mode exploring

Shadow detection is a common computer vision task that identifies the location of shadows. It has been used as a key primitive for many high-level tasks such as object segmentation and video tracking. Due to the complexity and variety of shadows, it is challenging to design a one-size-fits-all shadow detection algorithm in real-world scenarios. Instead, it is often necessary in practice to pick the best shadow detection algorithm for a particular vision application. While shadow detection algorithms can be assessed by comparing the detected shadow with the ground truth, such ground truth is often unavailable in real-world vision systems consistently generating new data. In this paper, we propose a no-reference shadow detection quality assessment network (NSDQA-Net) to rank shadow detection algorithms without needing the ground truth. For a given image, the proposed assessment network first learns a reference map that compensates the false positives and false negatives of multiple input shadow detection algorithms. The reference map, as well as the original image and the input shadow detection results, are then used to regress the quality scores of the shadow detection methods. Our framework supports the assessment of one, two, or multiple algorithms in a single run, making it easier for deployment in practical scenarios. Experiments results show that the proposed network achieves optimal accuracy in no-reference shadow detection assessment. Relying on it for adaptive algorithm selection also improves the performance of shadow detection using a fixed algorithm on benchmark datasets.

Urban feature shadow extraction based on high-resolution satellite remote sensing images

With the continuous increase in the resolution of high-resolution remote sensing satellite images, shadows have increasingly impacted feature target recognition and classification in images, urban reconstruction, and image interpretation. Although various shadow detection methods exist for different features, undetected small shadowed areas, misclassified dark areas, and highlighted non-shadowed areas remain. Moreover, it is still difficult to distinguish waters from shadowed regions owing to their similar characteristics. To further solve these problems, we propose a novel mixed shadow detection index (MSDI) for extracting urban feature shadows, using the original information of the first principal component that contains the differences between most of the different objects in the image, as well as the difference features in the shadowed areas and water bodies. We evaluated the effectiveness and robustness of our method by conducting comparison experiments using Gaofen-1, Gaofen-2, and WorldView-3 images from different scenes, times, and acquisition locations. Through visual analysis and data analysis of our method, we found that the method achieved excellent shadow detection results, with an average total accuracy 94% for shadow detection. The proposed shadow detection algorithm could accurately recognize waters, and it could also accurately recognize easily missed small shadowed areas and easily confused non-shadowed areas.

A ViSAR Shadow-Detection Algorithm Based on LRSD Combined Trajectory Region Extraction

Shadow detection is a new method for video synthetic aperture radar moving target indication (ViSAR-GMTI). The shadow formed by the target occlusion will reflect its real position, preventing the defocusing or offset of the moving target from making it difficult to identify the target during imaging. To achieve high-precision shadow detection, this paper proposes a video SAR moving target shadow-detection algorithm based on low-rank sparse decomposition combined with trajectory area extraction. Based on the low-rank sparse decomposition (LRSD) model, the algorithm creates a new decomposition framework combined with total variation (TV) regularization and coherence suppression items to improve the decomposition effect, and a global constraint is constructed to suppress interference using feature operators. In addition, it cooperates with the double threshold trajectory segmentation and error trajectory elimination method to further improve the detection performance. Finally, an experiment was carried out based on the video SAR data released by Sandia National Laboratory (SNL); the results prove the effectiveness of the proposed method, and the detection performance of the method is proved by comparative experiments.

Research on multispectral satellite image cloud and cloud shadow detection algorithm of domestic satellite

Research on multispectral satellite image cloud and cloud shadow detection algorithm of domestic satellite

An Extended Cloud Shadow Detection Algorithm Supported by an A Priori Database

An Extended Cloud Shadow Detection Algorithm Supported by an A Priori Database

Segmenting water and shadow regions within WorldView imagery using local binary patterns

Many shadow detection algorithms pertaining to remotely sensed imagery exist. Several of these algorithms exploit the spectral characteristics of shadows within imagery to identify shaded regions. However, these algorithms can have problems when water is also present within the imagery because water shares similar spectral characteristics with shadows. Many of these algorithms are applied to small image subsets instead of the image as a whole and frequently are applied to urban environments that require additional use of the normalized difference water index or other features to affect the removal of water from the shadow mask. This diversity of the image scene content coupled with the complexity and wide variety of environmental conditions that satellite imagery can acquire makes reliably separating shadow and water within larger images a complex problem. Thresholding various spectral indices to produce segmentation maps can be challenging when large, complex, and often imbalanced scenes are captured, which may require manual adjustment of algorithmic parameters for different image areas. We present a methodology that makes use of the near-infrared channel using a watershed segmentation algorithm, local binary pattern measurements, and a support vector machine to classify shadow and water within a full WorldView scene. Results are promising with initial accuracies above 97% for both shadow and water.

Cast Shadow Angle Detection in Morphological Aerial Images Using Faster R-CNN

With the tremendous advancements in digital image processing technology over the last few years, it is now possible to resolve many challenging issues. In light of this, this study proposes that digital image processing can be used to detect shadows in photographs. Since unmanned aerial vehicles and satellite devices have become more common image generating devices. The significant issue in the generated images is of its shadow. Shadows are inevitable in remote sensing photographs, particularly in metropolitan environments, due to the block of high-rise objects and the influence of the sun's altitude. This results in missing information in the shadow zone. The state-of-the-art shadow detection algorithms require manual alignment and predefined specific parameters. Most of those existing algorithms fail to deliver precise results in a variety of lighting and ecological conditions. To overcome these limitations, we propose a framework Multi Layered Linked approach with Tagged Feature Model for Shadow Angle Detection (MLTFM-SAD). The aim of the proposed model is to detect the shadows from aerial photographs and angle of those shadows. The proposed framework is a three-step approach. Initially, the image segmentation is applied on the input images. Second, hybrid Gaussian mixing mode and Otsu's approach is applied on the segmented shadow mask map and corresponding pixel set is generated. As a result, an initial shadow mask map is refined using object spectral attributes and spatial correlations between objects. Finally, the angle at which the shadow appears in the given image is recognised and analysed. The proposed method's performance is compared to that of all current approaches and the results revealed that the proposed model performance levels are superior.

An Illumination-Invariant Shadow-Based Scene Matching Navigation Approach in Low-Altitude Flight

Differences in acquisition time, light conditions, and viewing angle create significant differences among the airborne remote sensing images from Unmanned Aerial Vehicles (UAVs). Real-time scene matching navigation applications based on fixed reference maps are error-prone and have poor robustness. This paper presents a novel shadow-based matching method for the localization of low-altitude flight UAVs. A reference shadow map is generated from an accurate (0.5 m spatial resolution) Digital Surface Model (DSM) with the known date and time information; a robust shadow detection algorithm is employed to detect shadows in aerial images; the shadows can then be used as a stable feature for scene matching navigation. Combining the conventional intensity-based matching method, a fusion scene navigation scheme that is more robust to illumination variations is proposed. Experiments were performed with Google satellite maps, DSM data, and real aerial images of the Zurich region. The radial localization error of the Shadow-based Matching (SbM) is less than 7.3 m at flight height below 1200 m. The fusion navigation approach also achieves an optimal combination of shadow-based matching and intensity-based matching. This study shows the solution to the inconsistencies caused by changes in light, viewing angle, and acquisition time for accurate and effective scene matching navigation.

DARCLOS: a cloud shadow detection algorithm for TROPOMI

Abstract. Cloud shadows are observed by the TROPOMI satellite instrument as a result of its high spatial resolution compared to its predecessor instruments. These shadows contaminate TROPOMI's air quality measurements, because shadows are generally not taken into account in the models that are used for aerosol and trace gas retrievals. If the shadows are to be removed from the data, or if shadows are to be studied, an automatic detection of the shadow pixels is needed. We present the Detection AlgoRithm for CLOud Shadows (DARCLOS) for TROPOMI, which is the first cloud shadow detection algorithm for a spaceborne spectrometer. DARCLOS raises potential cloud shadow flags (PCSFs), actual cloud shadow flags (ACSFs), and spectral cloud shadow flags (SCSFs). The PCSFs indicate the TROPOMI ground pixels that are potentially affected by cloud shadows based on a geometric consideration with safety margins. The ACSFs are a refinement of the PCSFs using spectral reflectance information of the PCSF pixels and identify the TROPOMI ground pixels that are confidently affected by cloud shadows. Because we find indications of the wavelength dependence of cloud shadow extents in the UV, the SCSF is a wavelength-dependent alternative for the ACSF at the wavelengths of TROPOMI's air quality retrievals. We validate the PCSF and ACSF with true-colour images made by the VIIRS instrument on board Suomi NPP orbiting in close proximity to TROPOMI on board Sentinel-5P. We find that the cloud evolution during the overpass time difference between TROPOMI and VIIRS complicates this validation strategy, implicating that an alternative cloud shadow detection approach using co-located VIIRS observations could be problematic. We conclude that the PCSF can be used to exclude cloud shadow contamination from TROPOMI data, while the ACSF and SCSF can be used to select pixels for the scientific analysis of cloud shadow effects.

Intelligent Target Detection and Tracking Algorithm for Martial Arts Applications

Moving object detection and tracking is the basis and key technology for intelligent video surveillance, human-computer interaction, mobile robot and vehicle visual navigation, industrial robot system, and other applications. It has important applications in intelligent monitoring, human-computer interaction, visual navigation, and so on. Intelligent technology can greatly facilitate the monitoring of countless targets. This paper adopts the random motion model to describe the motion state of the target. Based on wireless communication and information security, this paper studies the communication and information security of the intelligent system of martial arts target detection and tracking algorithm, and the basic idea of mean-shift tracking algorithm is to use gradient climbing of probability density to find local optimal. This paper uses the background subtraction method based on the vibe algorithm to obtain the binary image of the foreground object; the shadow detection algorithm is used to remove the shadow of the foreground image; the haar-like (Haar) feature is selected as the feature of motion detection, and the feature value of the rectangular area is quickly calculated to describe the adjacent image area. The difference between the features, the final result image, and then the intelligent system for analysis. Experimental data shows that the time consumed by the tracking algorithm is less than 20 ms, which can meet the real-time requirements of ordinary target tracking systems. The average processing time of the hybrid modeling method is 62.8 ms, and the detection rate is 15.92 frames/SEC. The results show that the algorithm improves the utilization of particles, greatly reduces the complexity, and reduces the degradation of the particle filter.

Structure‐aware multiple salient region detection and localization for autonomous robotic manipulation

This paper proposes a multiple salient region detection and localization approach for unstructured industrial robot work environments with arbitrarily located and orientated objects. Different from the existing, the authors' novel technique to detect multiple salient regions performs locally adaptive center-surround operations on proto-object partitions obtained through color consistency and spatial proximity analysis. The multi-scale center-surround operations are done by masks that are local structure-aware yielding regions with precise and accurate boundaries as required for robotic manipulation. First, experiments to evaluate the multiple salient region detection performance are carried out using four standard databases having images with multiple salient objects. Quantitative result analysis using F-measure, shuffled F-measure, shuffled AUC and MAE, and subjective result inspection suggests that the proposed approach is in general better at collectively detecting multiple salient regions than the state-of-the-art, including those based on deep learning. Then, real-life experiments involving robotic manipulation are carried out to demonstrate the utility of the multiple salient region detection method. For robotic manipulation, object localization is improved after salient region detection by employing a fast shadow detection algorithm proposed based on hue analysis, and recognition through existing matching techniques is applied only at the localized salient regions. The benefit of the novel multiple salient region detection approach in the robotic manipulation system is shown using localization and pose estimation accuracy, rates of detection and recognition, positional and angular errors, and processing speed.

A Comprehensive Review of Vehicle Detection Techniques Under Varying Moving Cast Shadow Conditions Using Computer Vision and Deep Learning

Design of a vision-based traffic analytic system for urban traffic video scenes has a great potential in context of Intelligent Transportation System (ITS). It offers useful traffic-related insights at much lower costs compared to their conventional sensor based counterparts. However, it remains a challenging problem till today due to the complexity factors such as camera hardware constraints, camera movement, object occlusion, object speed, object resolution, traffic flow density, and lighting conditions etc. ITS has many applications including and not just limited to queue estimation, speed detection and different anomalies detection etc. All of these applications are primarily dependent on sensing vehicle presence to form some basis for analysis. Moving cast shadows of vehicles is one of the major problems that affects the vehicle detection as it can cause detection and tracking inaccuracies. Therefore, it is exceedingly important to distinguish dynamic objects from their moving cast shadows for accurate vehicle detection and recognition. This paper provides an in-depth comparative analysis of different traffic paradigm-focused conventional and state-of-the-art shadow detection and removal algorithms. Till date, there has been only one brief survey which highlights the shadow removal methodologies particularly for traffic paradigm. In this paper, a number of research papers containing results of urban traffic scenes have been shortlisted from the last three decades to give a comprehensive overview of the work done in this area. The study reveals that the only way to make a comparative evaluation is to use the existing Highway I, II, and III datasets which are frequently used for qualitative or quantitative analysis of shadow detection or removal algorithms. Furthermore, the paper not only provides cues to solve moving cast shadow problems, but also suggests that even after the advent of Convolutional Neural Network (CNN)-based vehicle detection methods, the problems caused by moving cast shadows persists. Therefore, this paper proposes a hybrid approach which uses a combination of conventional and state-of-the-art techniques as a pre-processing step for shadow detection and removal before using CNN for vehicles detection. The results indicate a significant improvement in vehicle detection accuracies after using the proposed approach.

An Approach for Shadow Detection in Aerial Images Based on Multi-Channel Statistics

Shadows are likely to cause flaws in color interpretation, loss of image information or deformation of objects; this is a relevant issue owing to the fact that with the development of unmanned aerial vehicles and satellite devices, object detection by aerial images has become an essential aim to research works. The main contribution of this article is the development of an algorithm that processes shadow detection by detaching the channels red, green, blue and an additional low saturation channel on individual masks; having created those masks, the task of shadow detection is accomplished by conducting a pixel-by-pixel basis with the aid of a decision tree and statistical descriptors extracted out of the image, where the low saturation channel information leads to improvements on shadow detection accuracy in regions that contain asphalt and concrete. Thus, experimental results show the good behavior of shadow detection algorithm in terms of accuracy, over-segmentation, and under-segmentation, in which the accuracy of obtained shadow detection approaches a 94%.

Property‐based shadow detection and removal method for licence plate image

Shadow detection and removal is a classic research in the field of image processing. This work aims to address the problem of shadow detection and removal for licence plate. The shadow detection and removal method based on inherent properties of licence plate is presented in this study. First, an S–V-channel-property-based shadow detection algorithm is investigated to extract the background-shaded area of licence plate. Afterwards, a shadow edge location algorithm is proposed to detect the complete shadow edge. Then, the shadow is removed on H, S, and V channels with different strategies, respectively. Also, a filter-based median filtering algorithm is employed to remove the pseudo-edge. Finally, the proposed method is verified through comparing with other generic shadow detection and removal methods quantitatively and qualitatively.

Moving Vehicle Detection and Tracking Based on Optical Flow Method and Immune Particle Filter under Complex Transportation Environments

Video-based moving vehicle detection and tracking is an important prerequisite for vehicle counting under complex transportation environments. However, in the complex natural scene, the conventional optical flow method cannot accurately detect the boundary of the moving vehicle due to the generation of the shadow. In addition, traditional vehicle tracking algorithms are often occluded by trees, buildings, etc., and particle filters are also susceptible to particle degradation. To solve this problem, this paper proposes a kind of moving vehicle detection and tracking based on the optical flow method and immune particle filter algorithm. The proposed method firstly uses the optical flow method to roughly detect the moving vehicle and then uses the shadow detection algorithm based on the HSV color space to mark the shadow position after threshold segmentation and further combines the region-labeling algorithm to realize the shadow removal and accurately detect the moving vehicle. Improved affinity calculation and mutation function of antibody are proposed to make the particle filter algorithm have certain adaptivity and robustness to scene interference. Experiments are carried out in complex traffic scenes with shadow and occlusion interference. The experimental results show that the proposed algorithm can well solve the interference of shadow and occlusion and realize accurate detection and robust tracking of moving vehicles under complex transportation environments, which has the potentiality to be processed on a cloud computing platform.

Shadow Elimination Algorithm Using Color and Texture Features.

Shadow detection and removal in real scene images are a significant problem for target detection. This work proposes an improved shadow detection and removal algorithm for urban video surveillance. First, the foreground is detected by background subtraction and the shadow is detected by HSV color space. Using local variance and OTSU method, we obtain the moving targets with texture features. According to the characteristics of shadow in HSV space and texture feature, the shadow is detected and removed to eliminate the shadow interference for the subsequent processing of moving targets. Finally, we embed our algorithm into C/S framework based on the HTML5 web socket protocol. Both the experimental and actual operation results show that the proposed algorithm is efficient and robust in target detection and shadow detection and removal under different scenes.

A shadow detection and removal method for fruit recognition in natural environments

Effective shadow detection and shadow removal can improve the performance of fruit recognition in natural environments and provide technical support for agricultural intelligence. In this study, a superpixel segmentation method was used to divide an image into multiple small regions. Based on the superpixel segmentation results, the shadow regions and the shadowless regions of the orchard images under natural light were compared and studied. Seven shadow saliency features (SSF) were explored and analyzed for shadow detection. The SSF were used to enhance the shadow characteristics. Then, the genetic algorithm (GA) was used to optimize the parameters, and support vector machine recursive feature elimination (SVM-RFE) was used to determine the best feature combination for shadow detection. According to the best feature combination, the support vector machine (SVM) algorithm was used to determine whether each segment of the superpixel segmentation results belonged to the shadow region. Shadow removal was carried out on each detected shadow region, and a natural light image after shadow removal was obtained. Finally, the accuracy of shadow detection was tested. The experimental results showed that the average accuracy of the shadow detection algorithm in this study was 91.91%. As a result, the precision and recall for fruits recognition after shadow removal generally improved.

Large Scale Shadow Annotation and Detection Using Lazy Annotation and Stacked CNNs.

Recent shadow detection algorithms have shown initial success on small datasets of images from specific domains. However, shadow detection on broader image domains is still challenging due to the lack of annotated training data, caused by the intense manual labor required for annotating shadow data. In this paper we propose "lazy annotation", an efficient annotation method where an annotator only needs to mark the important shadow areas and some non-shadow areas. This yields data with noisy labels that are not yet useful for training a shadow detector. We address the problem of label noise by jointly learning a shadow region classifier and recovering the labels in the training set. We consider the training labels as unknowns and formulate label recovery as the minimization of the sum of squared leave-one-out errors of a Least Squares SVM, which can be efficiently optimized. Experimental results show that a classifier trained with recovered labels achieves comparable performance to a classifier trained on the properly annotated data. These results motivated us to collect a new dataset that is 20 times larger than existing datasets and contains a large variety of scenes and image types. Naturally, such a large dataset is appropriate for training deep learning methods. Thus, we propose a stacked Convolutional Neural Network architecture that efficiently trains on patch level shadow examples while incorporating image level semantic information. This means that the detected shadow patches are refined based on image semantics. Our proposed pipeline, trained on recovered labels, performs at state-of-the art level. Furthermore, the proposed model performs exceptionally well on a cross dataset task, proving the generalization power of the proposed architecture and dataset.