Shadow Detection in Single RGB Images Using a Context Preserver Convolutional Neural Network Trained by Multiple Adversarial Examples

Mohajerani, Sorour; Saeedi, Parvaneh

doi:10.1109/tip.2019.2904267

Cited by 27 publications

(10 citation statements)

References 39 publications

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“…M denotes the total number of scenes in the test set. The Jaccard index is a widely accepted metric for measuring the performance of many image segmentation algorithms [36], [37]. This metric considers the regions in the predicted mask, which are correctly labeled as "cloud" and compares it to the union of the predicted mask and the ground truth.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Cloud and Cloud Shadow Segmentation for Remote Sensing Imagery Via Filtered Jaccard Loss Function and Parametric Augmentation

Mohajerani

Saeedi

2021

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

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Cloud and cloud shadow segmentation are fundamental processes in optical remote sensing image analysis. Current methods for cloud/shadow identification in geospatial imagery are not as accurate as they should, especially in the presence of snow and haze. This paper presents a deep learningbased framework for the detection of cloud/shadow in Landsat 8 images. Our method benefits from a convolutional neural network, Cloud-Net+ (a modification of our previously proposed Cloud-Net [1]) that is trained with a novel loss function (Filtered Jaccard Loss). The proposed loss function is more sensitive to the absence of foreground objects in an image and penalizes/rewards the predicted mask more accurately than other common loss functions. In addition, a sunlight direction-aware data augmentation technique is developed for the task of cloud shadow detection to extend the generalization ability of the proposed model by expanding existing training sets. The combination of Cloud-Net+, Filtered Jaccard Loss function, and the proposed augmentation algorithm delivers superior results on four public cloud/shadow detection datasets. Our experiments on Pascal VOC dataset exemplifies the applicability and quality of our proposed network and loss function in other computer vision applications.

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Section: Evaluation Metricsmentioning

confidence: 99%

Cloud and Cloud Shadow Segmentation for Remote Sensing Imagery Via Filtered Jaccard Loss Function and Parametric Augmentation

Mohajerani

Saeedi

2021

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

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“…Since the Jaccard index is related to both recall and precision, it can measure the similarity between the two sets of images. Therefore, the Jaccard index is widely used to measure the performance of image segmentation algorithms [49,50].…”

Section: Evaluation Metricsmentioning

confidence: 99%

Cloud Detection for Satellite Imagery Using Attention-Based U-Net Convolutional Neural Network

et al. 2020

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Cloud detection is an important and difficult task in the pre-processing of satellite remote sensing data. The results of traditional cloud detection methods are often unsatisfactory in complex environments or the presence of various noise disturbances. With the rapid development of artificial intelligence technology, deep learning methods have achieved great success in many fields such as image processing, speech recognition, autonomous driving, etc. This study proposes a deep learning model suitable for cloud detection, Cloud-AttU, which is based on a U-Net network and incorporates an attention mechanism. The Cloud-AttU model adopts the symmetric Encoder-Decoder structure, which achieves the fusion of high-level features and low-level features through the skip-connection operation, making the output results contain richer multi-scale information. This symmetrical network structure is concise and stable, significantly enhancing the effect of image segmentation. Based on the characteristics of cloud detection, the model is improved by introducing an attention mechanism that allows model to learn more effective features and distinguish between cloud and non-cloud pixels more accurately. The experimental results show that the method proposed in this paper has a significant accuracy advantage over the traditional cloud detection method. The proposed method is also able to achieve great results in the presence of snow/ice disturbance and other bright non-cloud objects, with strong resistance to disturbance. The Cloud-AttU model proposed in this study has achieved excellent results in the cloud detection tasks, indicating that this symmetric network architecture has great potential for application in satellite image processing and deserves further research.

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“…Several studies have been conducted to address the problem of shadow detection and removal in RGB images [7,8,9,10]. However, the number of research works which address those problems by considering near-infrared data is limited.…”

Section: Previous Workmentioning

confidence: 99%

Illumination-Invariant Image from 4-Channel Images: The Effect of Near-Infrared Data in Shadow Removal

Mohajerani¹,

Drew²,

Saeedi³

2020

Preprint

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Removing the effect of illumination variation in images has been proved to be beneficial in many computer vision applications such as object recognition and semantic segmentation. Although generating illumination-invariant images has been studied in the literature before, it has not been investigated on real 4-channel (4D) data. In this study, we examine the quality of illumination-invariant images generated from red, green, blue, and near-infrared (RGBN) data. Our experiments show that the near-infrared channel substantively contributes toward removing illumination. As shown in our numerical and visual results, the illumination-invariant image obtained by RGBN data is superior compared to that obtained by RGB alone.

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Shadow Detection in Single RGB Images Using a Context Preserver Convolutional Neural Network Trained by Multiple Adversarial Examples

Cited by 27 publications

References 39 publications

Cloud and Cloud Shadow Segmentation for Remote Sensing Imagery Via Filtered Jaccard Loss Function and Parametric Augmentation

Cloud and Cloud Shadow Segmentation for Remote Sensing Imagery Via Filtered Jaccard Loss Function and Parametric Augmentation

Cloud Detection for Satellite Imagery Using Attention-Based U-Net Convolutional Neural Network

Illumination-Invariant Image from 4-Channel Images: The Effect of Near-Infrared Data in Shadow Removal

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