Using Adversarial Network for Multiple Change Detection in Bitemporal Remote Sensing Imagery

Zhao, Wenzhi; Chen, Xi; Ge, Xiaoshan; Chen, Jiage

doi:10.1109/lgrs.2020.3035780

Cited by 26 publications

(9 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhao et al [164] proposed an attention gates generative adversarial adaptation network (AG-GAAN) .The AG-contributions GAAN's are as follows: (1) This method can detect multiple changes automatically; (2) it includes an attention gates mechanism for spatial constraint and accelerates change area identification with finer contours; and (3) the domain similarity loss is introduced to improve the model's discriminability, allowing the model to more accurately map out real changes.…”

Section: Deep Learning-based Unsupervised Methods For Vhr Imagesmentioning

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 Vhr Imagesmentioning

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

“…1) Data with Seasonal Differences: The dataset [56] has three bands with a spatial resolution of 0.3 m that describe two different seasonal variations; the size of the images is 2700 × 4275 pixels. A large number of change targets have been "increased" and "decreased" within the image pairs in different seasons, and are used to test the proposed model for its ability to detect seasonal differences.…”

Section: A Datasetsmentioning

confidence: 99%

UCDFormer: Unsupervised Change Detection Using a Transformer-Driven Image Translation

Xu,

Shi,

Guo

et al. 2023

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

Change detection (CD) by comparing two bitemporal images is a crucial task in remote sensing. With the advantages of requiring no cumbersome labeled change information, unsupervised CD has attracted extensive attention in the community. However, existing unsupervised CD approaches rarely consider the seasonal and style differences incurred by the illumination and atmospheric conditions in multi-temporal images. To this end, we propose a change detection with domain shift setting for remote sensing images. Furthermore, we present a novel unsupervised CD method using a light-weight transformer, called UCDFormer. Specifically, a transformer-driven image translation composed of a light-weight transformer and a domain-specific affinity weight is first proposed to mitigate domain shift between two images with real-time efficiency. After image translation, we can generate the difference map between the translated before-event image and the original after-event image. Then, a novel reliable pixel extraction module is proposed to select significantly changed/unchanged pixel positions by fusing the pseudo change maps of fuzzy c-means clustering and adaptive threshold. Finally, a binary change map is obtained based on these selected pixel pairs and a binary classifier. Experimental results on different unsupervised CD tasks with seasonal and style changes demonstrate the effectiveness of the proposed UCDFormer. For example, compared with several other related methods, UCDFormer improves performance on the Kappa coefficient by more than 12%. In addition, UCDFormer achieves excellent performance for earthquake-induced landslide detection when considering large-scale applications. The code is available at https://github.com/zhu-xlab/UCDFormer.

show abstract

“…These fused features are then used as skip connections in the decoder. After this seminal work, an entire research line investigated both the Early Fusion Strategy [3,12,25,26], and the Feature Fusion Strategy [2,6,10,11,13,14,[27][28][29][30][31][32].…”

Section: Related Workmentioning

confidence: 99%

TINYCD: A (Not So) Deep Learning Model For Change Detection

Codegoni

Lombardi²,

Ferrari³

2022

Preprint

View full text Add to dashboard Cite

The aim of change detection (CD) is to detect changes occurred in the same area by comparing two images of that place taken at different times. The challenging part of the CD is to keep track of the changes the user wants to highlight, such as new buildings, and to ignore changes due to external factors such as environmental, lighting condition, fog or seasonal changes. Recent developments in the field of deep learning enabled researchers to achieve outstanding performance in this area. In particular, different mechanisms of space-time attention allowed to exploit the spatial features that are extracted from the models and to correlate them also in a temporal way by exploiting both the available images. The downside is that the models have become increasingly complex and large, often unfeasible for edge applications. These are limitations when the models must be applied to the industrial field or in applications requiring real-time performances. In this work we propose a novel model, called TinyCD, demonstrating to be both lightweight and effective, able to achieve performances comparable or even superior to the current state of the art with 13-150X fewer parameters. In our approach we have exploited the importance of low-level features to compare images. We introduce a novel mixing block capable of cross correlating features in both space and time domains. Finally, to fully exploit the information contained in the computed features, we define the PW-MLP block able to perform a pixel wise classification.

show abstract

Using Adversarial Network for Multiple Change Detection in Bitemporal Remote Sensing Imagery

Cited by 26 publications

References 19 publications

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

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

UCDFormer: Unsupervised Change Detection Using a Transformer-Driven Image Translation

TINYCD: A (Not So) Deep Learning Model For Change Detection

Contact Info

Product

Resources

About