Domain adaptation for unsupervised change detection of multisensor multitemporal remote-sensing images

Farahani, Mahsa; Mohammadzadeh, Ali

doi:10.1080/01431161.2019.1711239

Cited by 13 publications

(7 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Farahani et al [126] used a technique based on auto-encoder, which was a deep analysis method used to achieve fused features of SAR, "optical" to benefit from complementary information, to align multi-temporal images by a reduction in spectral and radiometric differences, and made multi-temporal features more similar, for better accuracy in CD.…”

Section: Deep Learning-based Unsupervised Methods For Sar Imagementioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Section: Deep Learning-based Unsupervised Methods For Sar Imagementioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Achieving a multitemporal analysis over several decades requires overcoming numerous technical challenges, such as rectifying the differences between multiple sensors across different time periods, or addressing heterogeneous data quality and availability due to various factors, notably cloud cover [25,26]. Recent cloud computing and big data approaches [27,28] have brought new solutions to such problems, allowing researchers to assemble and process very large datasets composed of collections of remote sensing images and other ancillary data [29,30], thus potentially dramatically improving the performances of image classification and LULC analyses [31,32].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Annual Land Use/Land Cover Change in the Tucson Metropolitan Area with Google Earth Engine (1986–2020)

et al. 2022

View full text Add to dashboard Cite

The Tucson metropolitan area, located in the Sonoran Desert of southeastern Arizona (USA), is affected by both massive population growth and rapid climate change, resulting in important land use and land cover (LULC) changes. As its fragile arid ecosystem and scarce resources are increasingly under pressure, there is a crucial need to monitor such landscape transformations. For such ends, we propose a method to compute yearly 30 m resolution LULC maps of the region from 1986 to 2020, using a combination of Landsat imagery, derived transformation and indices, texture analysis and other ancillary data fed to a Random Forest classifier. The entire process was hosted in the Google Earth Engine with tremendous computing capacities that allowed us to process a large amount of data and to achieve high overall classification accuracy for each year, ranging from 86.7 to 96.3%. Conservative post-processing techniques were also used to mitigate the persistent confusions between the numerous isolated houses in the region and their desert surroundings and to smooth year-specific LULC changes in order to identify general trends. We then show that policies to lessen urban sprawl in the area had little effects and we provide an automated tool to continue monitoring such dynamics in the future.

show abstract

“…e bolt information in the picture is removed to obtain the track fastener clip subpicture, so as to avoid the interference of the bolt subgraph feature on the track fastener defect detection. Secondly, AE [27,28] and RBM [29,30] are used to extract clip subimage features of track fastener, because AE focuses on edge feature for extracting subimage features of track fastener clip [31], while RBM focuses on texture feature [32,33]. Considering that the edge feature is advantageous for detecting the state of track fastener loss and track fastener clip loosening, the texture feature is advantageous for detecting the broken state of the track fastener clip.…”

Section: Introductionmentioning

confidence: 99%

Defect Detection of Pandrol Track Fastener Based on Local Depth Feature Fusion Network

Chen

et al. 2021

Complexity

View full text Add to dashboard Cite

There are three main problems in track fastener defect detection based on image: (1) The number of abnormal fastener pictures is scarce, and supervised learning detection model is difficult to establish. (2) The potential data features obtained by different feature extraction methods are different. Some methods focus on edge features, and some methods focus on texture features. Different features have different detection capabilities, and these features are not effectively fused and utilized. (3) The detection of the track fastener clip will be interfered by the track fastener bolt subimage. Aiming at the above three problems, a method for track fastener defects detection based on Local Deep Feature Fusion Network (LDFFN) is proposed. Firstly, the track fastener image segmentation method is used to obtain the track fastener clip subimage, which can effectively reduce the interference of bolt subimage features on the track fastener clip detection. Secondly, the edge features and texture features of track fastener clip subimages are extracted by Autoencoder (AE) and Restricted Boltzmann Machine (RBM), and the features are fused. Finally, the similarity measurement method Mahalanobis Distance (MD) is used to detect defects in track fasteners. The effectiveness of the proposed method is verified by real Pandrol track fastener images.

show abstract

Domain adaptation for unsupervised change detection of multisensor multitemporal remote-sensing images

Cited by 13 publications

References 50 publications

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

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

Monitoring Annual Land Use/Land Cover Change in the Tucson Metropolitan Area with Google Earth Engine (1986–2020)

Defect Detection of Pandrol Track Fastener Based on Local Depth Feature Fusion Network

Contact Info

Product

Resources

About