Building Shadow Detection on Ghost Images

Zhou, Guoqing; Sha, Hongjun

doi:10.3390/rs12040679

Cited by 13 publications

(6 citation statements)

References 21 publications

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“…Shadow detection and segmentation for remote sensing data can be achieved using both classical computer vision methods and deep neural networks. Among the classical methods, the utilization of shadow indices [29], Principal Component Analysis (PCA) [30], or ghost image-based approaches [31] can be distinguished. When it comes to deep learning solutions, commonly used architectures such as UNet and DeepLab [32] are employed, as well as structures that incorporate global-local awareness for a more effective feature fusion at both the local and global levels [33], or models that focus on analyzing contextual information [34].…”

Section: Deep Learning For Geospatial Datamentioning

confidence: 99%

The Use of Deep Learning Methods for Object Height Estimation in High Resolution Satellite Images

Glinka,

Bajer,

Wierzbicki

et al. 2023

Sensors

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Processing single high-resolution satellite images may provide a lot of important information about the urban landscape or other applications related to the inventory of high-altitude objects. Unfortunately, the direct extraction of specific features from single satellite scenes can be difficult. However, the appropriate use of advanced processing methods based on deep learning algorithms allows us to obtain valuable information from these images. The height of buildings, for example, may be determined based on the extraction of shadows from an image and taking into account other metadata, e.g., the sun elevation angle and satellite azimuth angle. Classic methods of processing satellite imagery based on thresholding or simple segmentation are not sufficient because, in most cases, satellite scenes are not spectrally heterogenous. Therefore, the use of classical shadow detection methods is difficult. The authors of this article explore the possibility of using high-resolution optical satellite data to develop a universal algorithm for a fully automated estimation of object heights within the land cover by calculating the length of the shadow of each founded object. Finally, a set of algorithms allowing for a fully automatic detection of objects and shadows from satellite and aerial imagery and an iterative analysis of the relationships between them to calculate the heights of typical objects (such as buildings) and atypical objects (such as wind turbines) is proposed. The city of Warsaw (Poland) was used as the test area. LiDAR data were adopted as the reference measurement. As a result of final analyses based on measurements from several hundred thousand objects, the global accuracy obtained was ±4.66 m.

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Section: Deep Learning For Geospatial Datamentioning

confidence: 99%

The Use of Deep Learning Methods for Object Height Estimation in High Resolution Satellite Images

Glinka,

Bajer,

Wierzbicki

et al. 2023

Sensors

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“…In order to mitigate the interference of shadow on remote sensing images and improve the efficiency of utilization of images, researchers are trying to improve shadow processing technology of remote sensing images from various angles [ 10 , 11 , 12 , 13 , 14 , 15 ]. Our work is a new attempt to reform the shadow images by combining the Atmospheric Physical Transmission model with the generation of confrontation network.…”

Section: Related Workmentioning

confidence: 99%

AP Shadow Net: A Remote Sensing Shadow Removal Network Based on Atmospheric Transport and Poisson’s Equation

Wang

2022

Entropy

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Shadow is one of the fundamental indicators of remote sensing image which could cause loss or interference of the target data. As a result, the detection and removal of shadow has already been the hotspot of current study because of the complicated background information. In the following passage, a model combining the Atmospheric Transport Model (hereinafter abbreviated as ATM) with the Poisson Equation, AP ShadowNet, is proposed for the shadow detection and removal of remote sensing images by unsupervised learning. This network based on a preprocessing network based on ATM, A Net, and a network based on the Poisson Equation, P Net. Firstly, corresponding mapping between shadow and unshaded area is generated by the ATM. The brightened image will then enter the Confrontation identification in the P Net. Lastly, the reconstructed image is optimized on color consistency and edge transition by Poisson Equation. At present, most shadow removal models based on neural networks are significantly data-driven. Fortunately, by the model in this passage, the unsupervised shadow detection and removal could be released from the data source restrictions from the remote sensing images themselves. By verifying the shadow removal on our model, the result shows a satisfying effect from a both qualitative and quantitative angle. From a qualitative point of view, our results have a prominent effect on tone consistency and removal of detailed shadows. From the quantitative point of view, we adopt the non-reference evaluation indicators: gradient structure similarity (NRSS) and Natural Image Quality Evaluator (NIQE). Combining various evaluation factors such as reasoning speed and memory occupation, it shows that it is outstanding among other current algorithms.

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“…The third category comprises building height extraction methods based on single images [28]- [30]. For [33].…”

Section: Introductionmentioning

confidence: 99%

Building Height Extraction From High-Resolution Single-View Remote Sensing Images Using Shadow and Side Information

Xu,

Feng,

Wan

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Extracting building heights from single-view remote sensing images greatly enhances the application of remote sensing data. While methods for extracting building height from single-view shadow images have been widely studied, it remains a challenging task. The main reasons are as follows: (1) The traditional method for extracting shadow information exhibits low accuracy. (2) The use of only shadow information to extract building height results in limited application scenarios. To solve the above problems, this paper introduces building side and shadow information to complement each other, and proposes a building height extraction method from high-resolution single-view remote sensing images using shadow and side information. Firstly, we propose the RMU-Net method, which utilizes multi-scale features for the extraction of shadow and side information. This method aims to address issues related to pixel detail loss and imprecise edge segmentation, which result from significant scale differences within segmentation targets. Additionally, we employ the area threshold method to optimize the segmentation results, specifically to tackle small stray patches and holes, enhancing the overall integrity and accuracy of shadow and side information extraction. Secondly, we propose a method for building height extraction that integrates shadow and side information based on an enhanced proportional coefficient model. The accuracy of measuring building side and shadow lengths is improved by incorporating the fishing net method, informed by our analysis of the geometric relationships among buildings. Finally, we establish a dataset containing building shadow and side information from remote sensing images, and select multiple areas for experimental analysis. The results demonstrate a shadow extraction accuracy of 91.03% and a side extraction accuracy of 90.29%. Additionally, the average absolute error (MAE) for building height extraction is 1.22, while the average root mean square error (RMSE) is 1.21. Furthermore, the proposed method's validity and scalability are affirmed through experimental analyses of applicability and anti-interference performance in extensive areas.

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Building Shadow Detection on Ghost Images

Cited by 13 publications

References 21 publications

The Use of Deep Learning Methods for Object Height Estimation in High Resolution Satellite Images

The Use of Deep Learning Methods for Object Height Estimation in High Resolution Satellite Images

AP Shadow Net: A Remote Sensing Shadow Removal Network Based on Atmospheric Transport and Poisson’s Equation

Building Height Extraction From High-Resolution Single-View Remote Sensing Images Using Shadow and Side Information

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