Cloud/snow recognition for multispectral satellite imagery based on a multidimensional deep residual network

Xia, Min; Liu, Wan’an; Shi, Bicheng; Weng, Liguo; Liu, Jia

doi:10.1080/01431161.2018.1508917

Cited by 38 publications

(21 citation statements)

References 12 publications

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“…The convolutional neural network's main job is to learn the infrared image features, and the extracted features have a decisive effect on the subsequent segmentation. Xia also suggested in [5] that feature extraction's accuracy directly affects the final classification accuracy. Feature extraction networks mainly include AlexNet [6], VGG [7], etc.…”

Section: Atrous Convolution For Infrared Image Feature Extractionmentioning

confidence: 99%

MS-AFF: a novel semantic segmentation approach for buried object based on multi-scale attentional feature fusion

Lu¹,

Chen

et al. 2021

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Infrared technology is a widely used in precision guidance and mine detection since it can capture the heat radiated outward from the target object. We use infrared (IR) thermography to get the infrared image of the buried obje cts. Compared to the visible images, infrared images present poor resolution, low contrast, and fuzzy visual effect, which make it difficult to segment the target object, specifically in the complex backgrounds. In this condition, traditional segmentation methods cannot perform well in infrared images since they are easily disturbed by the noise and non-target objects in the images. With the advance of deep convolutional neural network (CNN), the deep learningbased methods have made significant improvements in semantic segmentation task. However, few of them research Infrared image semantic segmentation, which is a more challenging scenario compared to visible images. Moreover, the lack of an Infrared image dataset is also a problem for current methods based on deep learning. We raise a multiscale attentional feature fusion (MS-AFF) module for infrared image semantic segmentation to solve this problem. Precisely, we integrate a series of feature maps from different levels by an atrous spatial pyramid structure. In this way, the model can obtain rich representation ability on the infrared images. Besides, a global spatial information attention module is employed to let the model focus on the target region and reduce disturbance in infrared images' background. In addition, we propose an infrared segmentation dataset based on the infrared thermal imaging system. Extensive experiments conducted in the infrared image segmentation dataset show the superiority of our method.

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Section: Atrous Convolution For Infrared Image Feature Extractionmentioning

confidence: 99%

MS-AFF: a novel semantic segmentation approach for buried object based on multi-scale attentional feature fusion

Lu¹,

Chen

et al. 2021

Opt Quant Electron

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“…Compared with ML-based cloud detection models, DL-based techniques extract low-level to high-level features end-to-end and have shown great promise to distinguish cloudless areas and cloudy regions. 21,22 Based on the texture, spectral, and structural information, Shao et al 10 combined a neural network and fuzzy theory to improve the performance of cloud detection. Subsequently, Shao et al 23 proposed a multi-scale convolutional neural network (CNN) to automatically extract spatial and spectral information from multi-spectral satellite cloud images, which further enhance the prediction result of cloud detection.…”

Section: Introductionmentioning

confidence: 99%

“…These models include fully CNN, 24,25 deep CNN, 26 cascade CNN, 27 and residual network. 22 DL-based algorithms have made great progress in the improvement of cloud detection. However, there are some defects of DL-based methods that limit their practical application in a cloud detection task.…”

Section: Introductionmentioning

confidence: 99%

Multi-spectral cloud detection based on a multi-dimensional and multi-grained dense cascade forest

Shao

Zou

2021

J. Appl. Rem. Sens.

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Cloud detection in satellite images is a vital step for cloud/land recognition, cloud/ snow discrimination, and cloud shadow removal. Accurate cloud detection plays an important role in land resource management, environmental pollution monitoring, and land target recognition. Deep learning (DL) algorithms have shown great progress in cloud detection. However, as the complexity of the DL-based model increases, cloud detection efficiency decreases. DLbased cloud detection models are unable to successfully balance the performance-efficiency tradeoff. In our study, a multi-dimensional and multi-grained dense cascade forest (MDForest) is proposed for multi-spectral cloud detection. MDForest is a deep forest structure that automatically extracts low-level and high-level features from satellite cloud images end-to-end; a multi-dimensional and multi-grained scanning mechanism is introduced to capture the spectral information of multi-spectral satellite images while enhancing the representation learning ability of cascade forest. The experimental results on the HJ-1A/1B dataset show that MDForest improves the performance of cloud detection and possesses a good inference efficiency compared with DL-based cloud detection methods, which makes the proposed MDForest satisfy the application where good performance and high efficiency are both required. © The Authors.Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…As for the snow category, even convolutional deep learning approaches present difficulties in its separation from clouds. 19,[25][26][27] From the above, it is clear that convolutional deep learning approaches generally perform better than other approaches in the detection of challenging cases in cloud masking applications. It should be though highlighted that a crucial factor for achieving satisfactory performance is the high accuracy of the ground truth cloud masks.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural networks for detecting challenging cases in cloud masking using Sentinel-2 imagery

Kristollari

Karathanassi

2020

Eighth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2020)

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Cloud contamination represents a large obstacle for mapping the earth's surface using remotely sensed data. Therefore, cloudy pixels should be identified and eliminated before any further data processing can be achieved. Although several threshold, multi-temporal and machine learning applications have been developed to tackle this issue, it still remains a challenge. The main challenges are imposed by the difficulty to detect thin clouds and to separate bright clouds from bright non-cloud objects. Convolutional neural networks (CNNs) have proven to be one of the most promising methods for image classification tasks and their use is rapidly increasing in remote sensing problems. CNNs present interesting properties for image processing since they directly exploit not only the spectral information but also the spatial covariance of the data. In this work, we study the applicability of CNNs in cloud detection of Sentinel-2 imagery, a complex remote sensing problem with crucial spatial context. A patch-to-pixel CNN architecture consisting of three convolutional layers and two fully connected layers is trained on a recently available manually created public dataset. The results were evaluated both qualitatively and quantitatively through comparison with ground truth cloud masks and state-of-the art pixel-based algorithms (Fmask, Sen2Cor). It was shown that the proposed architecture even though simpler than the deep learning architectures proposed by recent literature, performs very favorably, especially in the challenging cases. Besides the evaluation of the results, feature maps where observed as an initial effort to extract the weights of the useful kernels for cloud masking applications.

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Cloud/snow recognition for multispectral satellite imagery based on a multidimensional deep residual network

Cited by 38 publications

References 12 publications

MS-AFF: a novel semantic segmentation approach for buried object based on multi-scale attentional feature fusion

MS-AFF: a novel semantic segmentation approach for buried object based on multi-scale attentional feature fusion

Multi-spectral cloud detection based on a multi-dimensional and multi-grained dense cascade forest

Convolutional neural networks for detecting challenging cases in cloud masking using Sentinel-2 imagery

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