Landsat 8 Remote Sensing Image Based on Deep Residual Fully Convolutional Network

Jiaqiang, 张家强 Zhang; Xiaoyan, 李潇雁 Li; Liyuan, 李丽圆 Li; Pengcheng, 孙鹏程 Sun; Xiaofeng, 苏晓锋 Su; Tingliang, 胡亭亮 Hu; Fansheng, 陈凡胜 Chen

doi:10.3788/lop57.102801

Cited by 3 publications

(2 citation statements)

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“…Densely connected convolutional network Densenet, based on Resnet, establishes dense connection between shallow and deep layers, and realizes feature reuse through the connection of features on the channel. Using Densenet as the encoder and decoder can train to a deeper structure, in which the cascading cavity convolution module is added to increase the receptive field, which is conducive to capturing the global information of the image, and also realizes the reuse of the features obtained by the encoder [40] . Pu et al also uses the DenseNet network as the backbone, and then combines the global self attention module and the spatial pyramid pooling module to extract the depth semantic features [41] .…”

Section: Encoder Decoder Structurementioning

confidence: 99%

A brief analysis of remote sensing image cloud extraction method based on deep learning

Lin,

Gai

2023

Third International Conference on Machine Learning and Computer Application (ICMLCA 2022)

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Section: Encoder Decoder Structurementioning

confidence: 99%

A brief analysis of remote sensing image cloud extraction method based on deep learning

Lin,

Gai

2023

Third International Conference on Machine Learning and Computer Application (ICMLCA 2022)

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“…Zhang et al [15] . improved the Unet structure by combining the encoder with the residual block, resulting in more accurate cloud detection than the traditional Unet network.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing image cloud detection algorithm based on residual network module and pyramid structure of dilated convolution

Chen,

Zhu

2024

MIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications

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Approximately 50% of the Earth’s surface is covered by clouds. Optical remote sensing satellites encounter challenges in capturing clear ground images due to the difficulty of visible photoelectric magnetic waves penetrating clouds. Therefore, cloud detection is an essential and basic step in the processing and application of optical satellite remote sensing images. The traditional threshold-based cloud detection methods utilize spectral information of images to set thresholds, which is sensitive and less adaptable to varying imaging conditions. To enhance cloud detection performance, this paper proposes a deep learning method that combines residual network module and pyramid structure of dilated convolution. The method employs an encoder-decoder structure where the residual convolution module replaces conventional convolution kernels, reducing parameter quantity while enhancing feature expression capabilities. Furthermore, a pyramid structure of dilated convolution is introduced between the encoder and decoder to improve the acquisition of global information and reduce misjudgment of cloud pixels. In this work, ablation experiments are conducted to validate the reliability of the proposed network model. To evaluate the effectiveness of the proposed method, GaoFen-1 remote sensing image data was used for experimental verification. The results indicated that compared to traditional methods, the proposed approach achieves satisfactory cloud detection results in various surface types, including barren land, forests, grasslands/crops, and wetlands, while having a smaller model size and parameter quantity.

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An Improved Convolutional Neural Network Cloud Detection Method Proposed in Complex Background

高

2024

CSA

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Landsat 8 Remote Sensing Image Based on Deep Residual Fully Convolutional Network

Cited by 3 publications

References 0 publications

A brief analysis of remote sensing image cloud extraction method based on deep learning

A brief analysis of remote sensing image cloud extraction method based on deep learning

Remote sensing image cloud detection algorithm based on residual network module and pyramid structure of dilated convolution

An Improved Convolutional Neural Network Cloud Detection Method Proposed in Complex Background

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