Research on Change Detection Method of High-Resolution Remote Sensing Images Based on Subpixel Convolution

Luo, Xin; Li, Xiaoxi; Wu, Yuxuan; Hou, Weimin; Wang, Meng; Jin, Yuwei; Xu, Wenbo

doi:10.1109/jstars.2020.3044060

Cited by 20 publications

(10 citation statements)

References 39 publications

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“…Luo et al [142] improved the change detection results using deep convolutional generative adversarial and the DeepLabv3+ network. To address the issue that DL networks require many samples and that CD samples are difficult to get, they used both nongenerative and DCGAN generative approaches for data augmentation.…”

Section: Deep Learning-based Unsupervised Methods For Multispectral I...mentioning

confidence: 99%

Deep Learning-Based Change Detection in Remote Sensing Images: A Review

et al. 2022

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Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods.

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Section: Deep Learning-based Unsupervised Methods For Multispectral I...mentioning

confidence: 99%

Deep Learning-Based Change Detection in Remote Sensing Images: A Review

et al. 2022

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“…For the different images, different methods are used to obtain the final change-detection results, including the thresholding method, pattern classification method, Markov random field method, multivariate statistical analysis method, and so on [ 175 , 176 , 177 ]. Due to the advantage of multi-level complex-feature extraction, end-to-end, pre-training, large-scale training sets, and other deep learning training mechanisms have also been applied to change detection [ 178 ].…”

Section: Remote Sensing Monitoring Approachesmentioning

confidence: 99%

Remote Sensing Approaches for Meteorological Disaster Monitoring: Recent Achievements and New Challenges

2022

IJERPH

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Meteorological disaster monitoring is an important research direction in remote sensing technology in the field of meteorology, which can serve many meteorological disaster management tasks. The key issues in the remote sensing monitoring of meteorological disasters are monitoring task arrangement and organization, meteorological disaster information extraction, and multi-temporal disaster information change detection. To accurately represent the monitoring tasks, it is necessary to determine the timescale, perform sensor planning, and construct a representation model to monitor information. On this basis, the meteorological disaster information is extracted by remote sensing data-processing approaches. Furthermore, the multi-temporal meteorological disaster information is compared to detect the evolution of meteorological disasters. Due to the highly dynamic nature of meteorological disasters, the process characteristics of meteorological disasters monitoring have attracted more attention. Although many remote sensing approaches were successfully used for meteorological disaster monitoring, there are still gaps in process monitoring. In future, research on sensor planning, information representation models, multi-source data fusion, etc., will provide an important basis and direction to promote meteorological disaster process monitoring. The process monitoring strategy will further promote the discovery of correlations and impact mechanisms in the evolution of meteorological disasters.

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“…Hence, this mechanism is used to focus on the most important graph nodes of the network. A typical integration of GNN with CNN is to implement a GNN after a CNN-based image segmentation to produce the final RS image classification results [80,81]. Accordingly, the attention network adjusts the weight for each graph node through the graph convolutional layers (Figure 6) [82].…”

Section: Deep Neural Network Architectures With Attention For Rs Image Processingmentioning

confidence: 99%

“…This is one of the challenging tasks and with the increasing amount of multi-temporal RS images has become more popular. At-DL was used in 7 papers to detect changes in general [110,111], in buildings [51], or any other objects [81,112]. (vi) Other tasks, such as image dehazing [113], digital elevation model (DEM) void filling [114], and SAR image despeckling [115] were addressed with At-DL in 9 papers.…”

Section: Overview Of the Reviewed Papersmentioning

confidence: 99%

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

et al. 2021

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Machine learning, particularly deep learning (DL), has become a central and state-of-the-art method for several computer vision applications and remote sensing (RS) image processing. Researchers are continually trying to improve the performance of the DL methods by developing new architectural designs of the networks and/or developing new techniques, such as attention mechanisms. Since the attention mechanism has been proposed, regardless of its type, it has been increasingly used for diverse RS applications to improve the performances of the existing DL methods. However, these methods are scattered over different studies impeding the selection and application of the feasible approaches. This study provides an overview of the developed attention mechanisms and how to integrate them with different deep learning neural network architectures. In addition, it aims to investigate the effect of the attention mechanism on deep learning-based RS image processing. We identified and analyzed the advances in the corresponding attention mechanism-based deep learning (At-DL) methods. A systematic literature review was performed to identify the trends in publications, publishers, improved DL methods, data types used, attention types used, overall accuracies achieved using At-DL methods, and extracted the current research directions, weaknesses, and open problems to provide insights and recommendations for future studies. For this, five main research questions were formulated to extract the required data and information from the literature. Furthermore, we categorized the papers regarding the addressed RS image processing tasks (e.g., image classification, object detection, and change detection) and discussed the results within each group. In total, 270 papers were retrieved, of which 176 papers were selected according to the defined exclusion criteria for further analysis and detailed review. The results reveal that most of the papers reported an increase in overall accuracy when using the attention mechanism within the DL methods for image classification, image segmentation, change detection, and object detection using remote sensing images.

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Research on Change Detection Method of High-Resolution Remote Sensing Images Based on Subpixel Convolution

Cited by 20 publications

References 39 publications

Deep Learning-Based Change Detection in Remote Sensing Images: A Review

Deep Learning-Based Change Detection in Remote Sensing Images: A Review

Remote Sensing Approaches for Meteorological Disaster Monitoring: Recent Achievements and New Challenges

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

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