Shadow Detection Method Research Articles

Computer Vision Based Method for Shadow Detection

Computer Vision Based Method for Shadow Detection

Image Shadow Detection and Removal Based on Region Matching of Intelligent Computing.

Shadow detection and removal play an important role in the field of computer vision and pattern recognition. Shadow will cause some loss and interference to the information of moving objects, resulting in the performance degradation of subsequent computer vision tasks such as moving object detection or image segmentation. In this paper, each image is regarded as a small sample, and then a method based on material matching of intelligent computing between image regions is proposed to detect and remove image shadows. In shadow detection, the proposed method can be directly used for detection without training and ensures the consistency of similar regions to a certain extent. In shadow removal, the proposed method can minimize the influence of shadow removal operation on other features in the shadow region. The experiments on the benchmark dataset demonstrate that the proposed approach achieves a promising performance, and its improvement is more than 6% in comparison with several advanced shadow detection methods.

Single image shadow detection via uncertainty analysis and GCN-based refinement strategy

Learning-based shadow detection methods have achieved an impressive performance, while these works still struggle on complex scenes, especially ambiguous soft shadows. To tackle this issue, this work proposes an efficient shadow detection network (ESDNet) and then applies uncertainty analysis and graph convolutional networks for detection refinement. Specifically, we first aggregate global information from high-level features and harvest shadow details in low-level features for obtaining an initial prediction. Secondly, we analyze the uncertainty of our ESDNet for an input shadow image and then take its intensity, expectation, and entropy into account to formulate a semi-supervised graph learning problem. Finally, we solve this problem by training a graph convolution network to obtain the refined detection result for every training image. To evaluate our method, we conduct extensive experiments on several benchmark datasets, i.e., SBU, UCF, ISTD, and even on soft shadow scenes. Experimental results demonstrate that our strategy can improve shadow detection performance by suppressing the uncertainties of false positive and false negative regions, achieving state-of-the-art results.

Dynamic Shadow Detection and Removal for Vehicle Tracking System

Shadow leads to failure of moving target positioning, segmentation, tracking, and classification in the video surveillance system thus shadow detection and removal is essential for further computer vision process. The existing state-of-the-art methods for dynamic shadow detection have produced a high discrimination rate but a poor detection rate (foreground pixels are classified as shadow pixels). This paper proposes an effective method for dynamic shadow detection and removal based on intensity ratio along with frame difference, gamma correction, and morphology operations. The performance of the proposed method has been tested on two outdoor ATON datasets, namely, highway-I and highway-III for vehicle tracking systems. The proposed method has produced a discrimination rate of 89.07% and a detection rate of 80.79% for highway-I video sequences. Similarly, for a highway-III video sequence, the discrimination rate of 85.60% and detection rate of 84.05% have been obtained. Investigational outcomes show that the proposed method is the simple, steadiest, and robust for dynamic shadow detection on the dataset used in this work.

Learning to detect soft shadow from limited data

Soft shadow is more challenging to detect than hard shadow due to its ambiguous boundary. Existing shadow detection methods pay more attention to hard shadow scene since collecting and annotating hard shadow images is more effortless. Motivated by that soft shadow has similar characteristics with hard shadow, and many traditional hard shadow datasets are publicly available, we propose a novel soft shadow detection method (namely Soft-DA) based on adversarial learning and domain adaptation scheme. Specifically, we create a limited soft shadow dataset, containing 1K soft shadow images with various scenes and shapes. Note that we just only need to annotate 0.4K shadow masks for semi-supervised learning. Besides, to tackle obvious domain discrepancy and potential intention difference between different datasets and similar tasks, we first align data distributions between domains by feature adversarial adaptation. And then, we introduce a novel detector separation strategy to tackle the intention difference issue. In this way, Soft-DA can effectively detect soft shadow with only a small number of soft shadow annotations. Extensive experiments demonstrate that our method can achieve superior performance to state of the arts.

Shadow Detection and Compensation from Remote Sensing Images under Complex Urban Conditions

Due to the block of high-rise objects and the influence of the sun’s altitude and azimuth, shadows are inevitably formed in remote sensing images particularly in urban areas, which causes missing information in the shadow region. In this paper, we propose a new method for shadow detection and compensation through objected-based strategy. For shadow detection, the shadow was highlighted by an improved shadow index (ISI) combined color space with an NIR band, then ISI was reconstructed by the objects acquired from the mean-shift algorithm to weaken noise interference and improve integrity. Finally, threshold segmentation was applied to obtain the shadow mask. For shadow compensation, the objects from segmentation were treated as a minimum processing unit. The adjacent objects are likely to have the same ambient light intensity, based on which we put forward a shadow compensation method which always compensates shadow objects with their adjacent non-shadow objects. Furthermore, we presented a dynamic penumbra compensation method (DPCM) to define the penumbra scope and accurately remove the penumbra. Finally, the proposed methods were compared with the stated-of-art shadow indexes, shadow compensation method and penumbra compensation methods. The experiments show that the proposed method can accurately detect shadow from urban high-resolution remote sensing images with a complex background and can effectively compensate the information in the shadow region.

ESPFNet: An Edge-Aware Spatial Pyramid Fusion Network for Salient Shadow Detection in Aerial Remote Sensing Images

Shadows can hinder image interpretation in aerial remote sensing images. The existing shadow detection methods focus on all shadow regions and detect the shadow regions directly, but they ignore the fact that salient shadows have a more significant effect. In this work, a novel edge-aware spatial pyramid fusion network (ESPFNet) under a multitask learning framework is proposed for salient shadow detection in aerial remote sensing images. ESPFNet has three components: a parallel spatial pyramid (PSP) structure; an edge detection module (EDM); and an edge-aware multibranch integration (EMI). The PSP structure is constructed to extract multiscale features from the input image and fuse them gradually. The EDM then integrates the shallow features and deep features to detect the shadow edges. Finally, the EMI incorporates the edge features with multibranch features, and then concatenates them with the shallow features to generate the salient shadow detection result. The experimental analyses confirm the effectiveness of the ESPFNet method in both the qualitative and quantitative performance, compared to the existing methods, with the F-score reaching 92.04% in the salient shadow test set.

Detection of shadows in high spatial resolution ocean satellite data using DINEOF

Cloud shadows present in high spatial resolution remote sensing datasets can affect the quality of the data if they are not properly detected and removed. When working with ocean data, cloud shadows are often difficult to differentiate from non-shadow values, since they show similar spectral characteristics than water pixels. A methodology to detect cloud shadows over the ocean is proposed. The present approach combines a series of tests applied directly to the physical variables derived from the satellite measured radiances, and it therefore does not depend on the wavebands measured by a specific satellite sensor. The tests include a departure from an EOF basis calculated using DINEOF, a threshold test, a proximity to cloud test and a ray tracing test. The weighing of the different tests can be adapted to each case or domain of study. The results are compared to manually detected shadows and to another shadow detection method. The approach works with cloud shadows of all sizes, and also with very small objects shadows, like the shadows projected by offshore windmills.

Mask R-CNN for Moving Shadow Detection and Segmentation

One of the primary tasks of completing and developing many computer vision applications is to identify and remove shadow regions. Most existing moving shadow detection methods depend on extracting hand-crafted features of object and shadow regions manually (for example the chromaticity, physical, or geometric properties). Shadow detection using handcrafted features is a challenging task due to different environmental conditions of the shadow such as camouflage and illumination irregularity problems that make these features inefficient to handle such problems. The proposed method uses Convolution Neural Networks (CNN) to automatically learn different distinctive features to model shadow under different environmental conditions. In this paper, the Mask Region Convolution Neural Network (Mask R-CNN) framework is evaluated and tested to automatically perform semantic segmentation in order to detect and classify shadow pixels from the entire video frame. To adapt Mask R-CNN for segmenting and detecting shadow regions, the most significant features are extracted from video frames in a supervised way using deep Residual Network (ResNet-101) architecture. Then, the Region proposal network (RPN) predicts regions of interest (ROI) and their classes that contain foreground objects. Finally, Fully Convolutional Network (FCN) generates a binary segmentation mask for each detected class in ROI. The proposed framework evaluated on common shadow detection datasets that have different environmental issues. Experimental results achieved high performance rates compared to several state-of-the-art methods in terms of average detection rate (96.81%), average discrimination rate (99.42%), and overall accuracy (98.09 %).

Approach for shadow detection and removal using machine learning techniques

In this work, the authors have proposed a method for shadow detection and removal from videos by utilising methods of machine learning. From literature, various algorithms on shadow detection and removal have been accounted with advantages and disadvantages. Here some algorithms have a need for manual alignment and predefined explicit parameters, but fail to give precise outcome in different lighting and ecological surroundings. In this work, the authors propose a three-phase framework. In first stage, key frames are chosen by utilising features based K-means clustering which selects the key frames using features like colour, shape and surface. In second stage, they utilised two-stage segmentation techniques to segment the shadows by marking the region of interest. In the final step, they use threshold based segmentation to remove the shadow in videos. The performance of the proposed method is compared by performance evaluation of all state-of-the-art methods. The proposed strategies are established to achieve superior results in comparison to other state-of-the-art methods.

Automatic Shadow Detection for Multispectral Satellite Remote Sensing Images in Invariant Color Spaces

Shadow often results in difficulties for subsequent image applications of multispectral satellite remote sensing images, like object recognition and change detection. With continuous improvement in both spatial and spectral resolutions of satellite remote sensing images, a more serious impact occurs on satellite remote sensing image interpretation due to the existence of shadow. Though various shadow detection methods have been developed, problems of both shadow omission and nonshadow misclassification still exist for detecting shadow well in high-resolution multispectral satellite remote sensing images. These shadow detection problems mainly include high small shadow omission and typical nonshadow misclassification (like bluish and greenish nonshadow misclassification, and large dark nonshadow misclassification). For further resolving these problems, a new shadow index is developed based on the analysis of the property difference between shadow and the corresponding nonshadow with several multispectral band components (i.e., near-infrared, red, green and blue components) and hue and intensity components in various invariant color spaces (i.e., HIS, HSV, CIELCh, YCbCr and YIQ), respectively. The shadow mask is further acquired by applying an optimal threshold determined automatically on the shadow index image. The final shadow image is further optimized with a definite morphological operation of opening and closing. The proposed algorithm is verified with many images from WorldView-3 and WorldView-2 acquired at different times and sites. The proposed algorithm performance is particularly evaluated by qualitative visual sense comparison and quantitative assessment of shadow detection results in comparative experiments with two WorldView-3 test images of Tripoli, Libya. Both the better visual sense and the higher overall accuracy (over 92% for the test image Tripoli-1 and approximately 91% for the test image Tripoli-2) of the experimental results together deliver the excellent performance and robustness of the proposed shadow detection approach for shadow detection of high-resolution multispectral satellite remote sensing images. The proposed shadow detection approach is promised to further alleviate typical shadow detection problems of high small shadow omission and typical nonshadow misclassification for high-resolution multispectral satellite remote sensing images.

GSCA-UNet: Towards Automatic Shadow Detection in Urban Aerial Imagery with Global-Spatial-Context Attention Module

As an inevitable phenomenon in most optical remote-sensing images, the effect of shadows is prominent in urban scenes. Shadow detection is critical for exploiting shadows and recovering the distorted information. Unfortunately, in general, automatic shadow detection methods for urban aerial images cannot achieve satisfactory performance due to the limitation of feature patterns and the lack of consideration of non-local contextual information. To address this challenging problem, the global-spatial-context-attention (GSCA) module was developed to self-adaptively aggregate all global contextual information over the spatial dimension for each pixel in this paper. The GSCA module was embedded into a modified U-shaped encoder–decoder network that was derived from the UNet network to output the final shadow predictions. The network was trained on a newly created shadow detection dataset, and the binary cross-entropy (BCE) loss function was modified to enhance the training procedure. The performance of the proposed method was evaluated on several typical urban aerial images. Experiment results suggested that the proposed method achieved a better trade-off between automaticity and accuracy. The F1-score, overall accuracy, balanced-error-rate, and intersection-over-union metrics of the proposed method were higher than those of other state-of-the-art shadow detection methods.

Feature Analysis and Target Detection of High-resolution Remote Sensing Image of Coal Mining Subsidence Area

Aiming at the phenomenon of regional ground collapse caused by mining, the site information is obtained through field investigation, and the features of typical collapse targets are described. The drone remote sensing images are used to extract and analyze the color features and shape features of typical collapse targets. Combine the Hough transform, shadow and Harris corner detection methods to achieve the target detection of collapsed pits and trough collapsed areas. The combined method uses Hough transform to detect the edge shape curve of the collapsed pits and trough collapsed areas, and use shadow detection method to extract its internal areas, and Harris corner detection method is used to locate local feature points in the image. Experimental analysis results show that the method has good application effect on target detection in collapsed pits and trough collapsed areas.

스마트시티 환경에서 움직이는 그림자 자동 감지 및 제거 방법 연구

본 논문에서는, 장면에서 움직이는 물체가 존재하는 상황에서 현대적인 건물의 그림자를 정확하게 제거할 수 있는 새로운 방법론을 제시한다. 물체 인식, 추적 등 많은 컴퓨터 비전 분야에서 그림자 감지 활용에 대한 관심이 높다. 그림자 제거 시스템의 기하학적 특징들은 제안된 방법의 후보 그림자 픽셀을 검출하는 데 사용된다. 본 논문에서 제안하는 프레임워크는 배경 표면의 유사성과 고스트 이미지를 줄임으로써 여러 그림자 문제를 해결하는데 좋은 성과를 얻을 수 있는 장점이 있다. 분석을 위해, 널리 사용되는 그림자 제거 성능 데이터 셋을 활용하였다. 실험결과, 스마트 하우스, 회의장 또는 여러 실내 비디오 보안 감시 시스템과 같이 스마트 시티 환경에서 본 논문에서 제안하는 방법이 효과적으로 그림자를 인식하여 대피할 수 있도록 함을 알 수 있었다.

ВИДАЛЕННЯ ТІНЕЙ НА ЦИФРОВИХ КОСМІЧНИХ ЗНІМКАХ НА ОСНОВІ ВЕЙВЛЕТ-ПЕРЕТВОРЕННЯ

Розглянуто проблему ідентифікації та компенсації тіней на космічних знімках високого просторового розрізнення. У роботі використано знімки міської території, отримані супутником WorldView-3. Наявність тіней на знімках може привести до втрати корисної інформації і навіть помилок в роботі алгоритмів розпізнавання, виявлення, відстеження і класифікації об’єктів. Запропоновано новий алгоритм автоматичного виявлення та усунення тіней цифрових космічних знімків, що дозволяє відновити освітленість та підвищити якість цих знімків. Для цього використано перехід до кольорової метрики HSV, вейвлет-перетворення та контурну сегментацію. Порівняння кількісних показників, а також візуальні результати показали перевагу використання запропонованого алгоритму. Результати роботи можуть бути використані при подальшому розпізнаванні об’єктів та тематичній обробці космічних знімків.

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.

Building Shadow Detection on Ghost Images

Although many efforts have been made on building shadow detection from aerial images, little research on simultaneous shadows detection on both building roofs and grounds has been presented. Hence, this paper proposes a new method for simultaneous shadow detection on ghost image. In the proposed method, a corner point on shadow boundary is selected and its 3D approximate coordinate is calculated through photogrammetric collinear equation on the basis of assumption of average elevation within the aerial image. The 3D coordinates of the shadow corner point on shadow boundary is used to calculate the solar zenith angle and the solar altitude angle. The shadow areas on the ground, at the moment of aerial photograph shooting are determined by the solar zenith angle and the solar altitude angle with the prior information of the digital building model (DBM). Using the relationship between the shadows of each building and the height difference of buildings, whether there exists a shadow on the building roof is determined, and the shadow area on the building roof on the ghost image is detected on the basis of the DBM. High-resolution aerial images located in the City of Denver, Colorado, USA are used to verify the proposed method. The experimental results demonstrate that the shadows of the 120 buildings in the study area are completely detected, and the success rate is 15% higher than the traditional shadow detection method based on shadow features. Especially, when the shadows occur on the ground and on the buildings roofs, the successful rate of shadow detection can be improved by 9.42% and 33.33% respectively.

Shadow Detection in Still Road Images Using Chrominance Properties of Shadows and Spectral Power Distribution of the Illumination.

A well-known challenge in vision-based driver assistance systems is cast shadows on the road, which makes fundamental tasks such as road and lane detections difficult. In as much as shadow detection relies on shadow features, in this paper, we propose a set of new chrominance properties of shadows based on the skylight and sunlight contributions to the road surface chromaticity. Six constraints on shadow and non-shadowed regions are derived from these properties. The chrominance properties and the associated constraints are used as shadow features in an effective shadow detection method intended to be integrated on an onboard road detection system where the identification of cast shadows on the road is a determinant stage. Onboard systems deal with still outdoor images; thus, the approach focuses on distinguishing shadow boundaries from material changes by considering two illumination sources: sky and sun. A non-shadowed road region is illuminated by both skylight and sunlight, whereas a shadowed one is illuminated by skylight only; thus, their chromaticity varies. The shadow edge detection strategy consists of the identification of image edges separating shadowed and non-shadowed road regions. The classification is achieved by verifying whether the pixel chrominance values of regions on both sides of the image edges satisfy the six constraints. Experiments on real traffic scenes demonstrated the effectiveness of our shadow detection system in detecting shadow edges on the road and material-change edges, outperforming previous shadow detection methods based on physical features, and showing the high potential of the new chrominance properties.

ORTHOPHOTO SHADOW DETECTION METHOD UNDER ARTIFICIAL SHADOW

Abstract. Shadows are ubiquitous in high-resolution images, especially in urban regions where there are more serious shadow occlusions. In order to improve the detection effect of shadows, this paper analyzes the characteristics and properties of shadows in orthophotos, and proposes an orthophoto shadow detection method under artificial shadow. Firstly, the shadow modeling tool is used to calculate the shadow regions (i.e. artificial shadow) caused by the building obstructing the sun's rays. Secondly, the relaxation matching algorithm is extended by the position and the shape of the shadow polygon as characteristic constraints. The relaxation matching algorithm is extended by the position and shape as shadow polygon’s characteristic constraints. Thirdly, the feature constraint value is calculated which between the shadow polygons of the two images. The correlation coefficient is used to obtain the initial probability value of each shadow polygon in the orthophoto. Finally, the optimal solution is obtained by continuous correction and iteration of the initial probability value. The method performs an overall matching of the two images and obtains the position of the shadow regions of the buildings in the orthophoto image. Experiment shows that this method reduces the mismatch rate and improves the matching accuracy. This method can detect shadow regions of buildings in orthophoto quickly and efficiently.

Shadow detection in very high resolution RGB images using a special thresholding on a new spectral–spatial index

Shadow detection plays an important role in remote sensing applications. Shadow should be detected with damage assessment algorithms, and it should be removed from the ground surface with semantic labeling applications. The procedure of a typical shadow detection method includes defining a shadow index and thresholding it, either automatic or manually. An automatic shadow detection method is proposed to facilitate the process of automatic applications. Specifications of shadow and nonshadow areas are analyzed to construct a new spectral–spatial shadow index. Spectral elements of the index can handle the dark shadow extraction. The spatial element of the index provides a high separability between light shadow and dark nonshadow areas such as water bodies. Index definition follows a segmentation algorithm to provide a segment-based analysis. Thresholding is a nonseparable part of shadow detection methods as it divides the region into shadow and nonshadow areas. To avoid high false positive or negative results, the proposed thresholding method is dependent on the enhanced bimodality test. Bimodal distribution can easily be thresholded by typical techniques such as the Otsu thresholding, whereas a special clustering-based thresholding is proposed in the unimodal distribution. The evaluations show a great improvement in shadow detection of very high-resolution RGB images.