Quantitative monitoring of sugarcane typhoon disaster based on multi-source remote sensing data

Qian, Lisha; Chen, Shuisen; Jiang, Hao; Dai, Xuemei; Jia, Kai

doi:10.1109/icgmrs55602.2022.9849279

Cited by 2 publications

(2 citation statements)

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“…With the development of sensor technology, the RS data have found extensive applications in disaster monitoring [2][3][4][5][6][7], precision agriculture [8,9], environmental risk assessment [10,11], and oil spill detection [12]. Furthermore, leveraging the repeated earth observations facilitated by satellites, multi-temporal RS data have been utilized to enhance land cover classification accuracy [13][14][15][16][17][18][19][20][21], object recognition [22][23][24], as well as disaster monitoring and assessment [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

A Novel Shipyard Production State Monitoring Method Based on Satellite Remote Sensing Images

Qin,

Song,

Zhu

et al. 2023

Remote Sensing

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Monitoring the shipyard production state is of great significance to shipbuilding industry development and coastal resource utilization. In this article, it is the first time that satellite remote sensing (RS) data is utilized to monitor the shipyard production state dynamically and efficiently, which can make up for the traditional production state data collection mode. According to the imaging characteristics of optical remote sensing images in shipyards with a different production state, the characteristics are analyzed to establish reliable production state evidence. Firstly, in order to obtain the characteristics of the production state of optical remote sensing data, the high-level semantic information in the shipyard is extracted by transfer learning convolutional neural networks (CNNs). Secondly, in the evidence fusion, for the conflict evidence from the core sites of the shipyard, an improved DS evidence fusion method is proposed, which constructs the correlation metric to measure the degree of conflict in evidence and designs the similarity metric to measure the credibility of evidence. Thirdly, the weight of all the evidence is calculated according to the similarity metric to correct the conflict evidence. The introduction of the iterative idea is motivated by the fact that the fusion result aligns more closely with the desired result, the iterative idea is introduced to correct the fusion result. This method can effectively solve the conflict of evidence and effectively improve the monitoring accuracy of the shipyard production state. In the experiments, the Yangtze River Delta and the Bohai Rim are selected to verify that the proposed method can accurately recognize the shipyard production state, which reveals the potential of satellite RS images in shipyard production state monitoring, and also provides a new research thought perspective for other industrial production state monitoring.

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Section: Introductionmentioning

confidence: 99%

A Novel Shipyard Production State Monitoring Method Based on Satellite Remote Sensing Images

Qin,

Song,

Zhu

et al. 2023

Remote Sensing

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“…Additionally, remote sensing images are instrumental in managing natural disasters such as floods, earthquakes, and wildfires. They offer realtime information that assists authorities in making prompt decisions [3]. Furthermore, remote sensing images are also widely used in the fields of geology and geomorphology [4], archaeology, and cultural heritage [5], as well as climate change studies [6] and other fields [7].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Dehazing through an Unsupervised Generative Adversarial Network

Zhao,

Yin,

Zhong

et al. 2023

Sensors

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The degradation of visual quality in remote sensing images caused by haze presents significant challenges in interpreting and extracting essential information. To effectively mitigate the impact of haze on image quality, we propose an unsupervised generative adversarial network specifically designed for remote sensing image dehazing. This network includes two generators with identical structures and two discriminators with identical structures. One generator is focused on image dehazing, while the other generates images with added haze. The two discriminators are responsible for distinguishing whether an image is real or generated. The generator, employing an encoder–decoder architecture, is designed based on the proposed multi-scale feature-extraction modules and attention modules. The proposed multi-scale feature-extraction module, comprising three distinct branches, aims to extract features with varying receptive fields. Each branch comprises dilated convolutions and attention modules. The proposed attention module includes both channel and spatial attention components. It guides the feature-extraction network to emphasize haze and texture within the remote sensing image. For enhanced generator performance, a multi-scale discriminator is also designed with three branches. Furthermore, an improved loss function is introduced by incorporating color-constancy loss into the conventional loss framework. In comparison to state-of-the-art methods, the proposed approach achieves the highest peak signal-to-noise ratio and structural similarity index metrics. These results convincingly demonstrate the superior performance of the proposed method in effectively removing haze from remote sensing images.

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Quantitative monitoring of sugarcane typhoon disaster based on multi-source remote sensing data

Cited by 2 publications

References 0 publications

A Novel Shipyard Production State Monitoring Method Based on Satellite Remote Sensing Images

A Novel Shipyard Production State Monitoring Method Based on Satellite Remote Sensing Images

Remote Sensing Image Dehazing through an Unsupervised Generative Adversarial Network

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