Patch-Based Change Detection Method for SAR Images with Label Updating Strategy

Shu, Yuanjun; Wei, Li; Yang, Menglong; Cheng, Peng; Han, Songchen

doi:10.3390/rs13071236

Cited by 20 publications

(9 citation statements)

References 59 publications

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“…However, the model complexity was high, and their result evaluation performance was lower than in previous research. In [128], PCC and kappa was 0.99, 0.94. The main advantage is that model accuracy and computational time is increased.…”

Section: Deep Learning-based Semi-supervised Methods For Sar Imagementioning

confidence: 91%

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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 Semi-supervised Methods For Sar Imagementioning

confidence: 91%

“…Shu et al [128] used a patch-based approach for CD. A mask function converts change labels with irregular shapes into a regular map, allowing the network to learn patches end-to-end.…”

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

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“…For example, Samadi et al [31] proposed a method that combines morphological images with two original images to provide a suitable data source for DBN training. Shu et al [32] proposed a two-stage patch-based deep learning method using a label update strategy. Initial labels and masks are generated in the pre-classification stage.…”

Section: Change Detectionmentioning

confidence: 99%

Generative Adversarial Differential Analysis for Infrared Small Target Detection

Ma,

Pang,

Hao

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Infrared small target detection refers to the extraction of small targets in a complex, low signal-to-noise ratio background. Depthwise convolution makes it difficult to comprehensively characterize small infrared targets and ignores the importance of image background for the detection task. In this paper, we propose the generative adversarial differential analysis (GADA) model for infrared small target detection, the core of which aims to weaken the reliance on target features and enhance the use of background information. Specifically, we first construct pseudo background labels by the fast marching method. Then, the background-guided generative adversarial network is used to learn the background data distribution. On this basis, the differential image containing interest regions of small targets is obtained by differential analysis. Finally, the detection results are obtained by performing an elaborate characterization of the interest regions. The effectiveness of GADA is verified with three public datasets. Compared to several state-of-the-art methods, GADA achieves better performance in terms of F 1, IoU , and AU C.

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“…Synthetic Aperture Radar (SAR) is an active microwave imaging sensor, which can penetrate clouds, rain, snow, and smoke, and has all-day and all-weather imaging observation capability, which has been widely used in both military and civilian fields [1][2][3][4][5][6][7]. Aircraft detection is an important application of SAR.…”

Section: Introductionmentioning

confidence: 99%

FedDAD: Solving the Islanding Problem of SAR Image Aircraft Detection Data

et al. 2023

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In aircraft feature detection, the difficulty of acquiring Synthetic Aperture Radar (SAR) images leads to the scarcity of some types of aircraft samples, and the high privacy makes the personal sample set have the characteristics of data silos. Existing data enhancement methods can alleviate the problem of data scarcity through feature reuse, but they are still powerless for data that are not involved in local training. To solve this problem, a new federated learning framework was proposed to solve the problem of data scarcity and data silos through multi-client joint training and model aggregation. The commonly used federal average algorithm is not effective for aircraft detection with unbalanced samples, so a federal distribution average deviation (FedDAD) algorithm, which is more suitable for aircraft detection in SAR images, was designed. Based on label distribution and client model quality, the contribution ratio of each client parameter is adaptively adjusted to optimize the global model. Client models trained through federated cooperation have an advantage in detecting aircraft with unknown scenarios or attitudes while remaining sensitive to local datasets. Based on the YOLOv5s algorithm, the feasibility of federated learning was verified on SAR image aircraft detection datasets and the portability of the FedDAD algorithm on public datasets. In tests based on the YOLOv5s algorithm, FedDAD outperformed FedAvg’s mAP0.5–0.95 on the total test set of two SAR image aircraft detection and far outperformed the local centralized training model.

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Patch-Based Change Detection Method for SAR Images with Label Updating Strategy

Cited by 20 publications

References 59 publications

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

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

Generative Adversarial Differential Analysis for Infrared Small Target Detection

FedDAD: Solving the Islanding Problem of SAR Image Aircraft Detection Data

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