SAR Image Segmentation Based on Constrained Smoothing and Hierarchical Label Correction

Shang, Ronghua; Liu, Mengmeng; Lin, Junkai; Feng, Jie; Li, Yangyang; Jiao, Licheng

doi:10.1109/tgrs.2021.3076446

Cited by 17 publications

(6 citation statements)

References 47 publications

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“…Approaches dealing with Data Noise: NSCT [95], HRO [97], PIQE [98], ACL-CNN [99], HS2P [9]. Approaches dealing with Label Noise: tRNSL [102], NTDNE [103], AF2GNN [105], RSSC-ETDL [106], CSHLC [107], I-FPFN-EM [108], FFCTL [109], RS-COCL-NLF [110], RVgg19 [111]. Table II provides more information about datasets, modalities and backbone of each reviewed methods.…”

Section: A Data Noisementioning

confidence: 99%

“…However, it's worth noting that training multiple CNNs, as done in this approach, can be computationally intensive. Another innovative approach is presented by [107], which progressively refines segmentation output using a novel constrained smoothing and hierarchical label correction (CSHLC) scheme. This scheme incorporates techniques such as pixel group counting comparison (PGCC) and gray similarity comparison (GSC) coupled with Markov Random Fields.…”

Section: B Label Noisementioning

confidence: 99%

See 1 more Smart Citation

Multimodal Colearning Meets Remote Sensing: Taxonomy, State of the Art, and Future Works

Kieu,

Nguyen,

Nazib

et al. 2024

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

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In remote sensing (RS), multiple modalities of data are usually available, e.g., RGB, Multispectral, Hyperspectral, LiDAR, and SAR. Multimodal machine learning systems, which fuse these rich multimodal data modalities, have shown better performance compared to unimodal systems. Most multimodal research assumes that all modalities are present, aligned, and noiseless during training and testing time. However, in real-world scenarios, it is common to observe that one or more modalities are missing, noisy, and non-aligned, in either training or testing or both. In addition, acquiring largescale, noise-free annotations is expensive, as a result, lacking sufficient annotated datasets or having to deal with inconsistent labels are open challenges. These challenges can be addressed under a learning paradigm called multimodal co-learning.This paper focuses on multimodal co-learning techniques for remote sensing data. We first review what data modalities are available in the remote sensing domain and the key benefits and challenges of combining multimodal data in the remote sensing context. We then review the remote sensing tasks that would benefit from multimodal processing including classification, segmentation, target detection, anomaly detection, and temporal change detection. We then dive deeper into technical details by reviewing more than 200 recent efforts in this area and provide a comprehensive taxonomy to systematically review state-of-the-art approaches in 4 key co-learning challenges including missing modalities, noisy modalities, limited modality annotations, and weakly-paired modalities. Based on these insights, we propose emerging research directions to inform potential future research in multimodal co-learning for remote sensing.

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Section: A Data Noisementioning

confidence: 99%

Section: B Label Noisementioning

confidence: 99%

Multimodal Colearning Meets Remote Sensing: Taxonomy, State of the Art, and Future Works

Kieu,

Nguyen,

Nazib

et al. 2024

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

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show abstract

“…FL still has a lot to learn about effective communication [4]. On the one hand, complicated neural networks are widely used in advanced machine learning applications that are installed on end devices, leading to local updates that frequently have huge gradient vectors.…”

Section: Introductionmentioning

confidence: 99%

cFL：Data distribution edge clustering algorithm based on deep Federated learning

Xide Wang

2024

jes

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Federated learning (FL) allows end-devices to train local models on their respective local datasets and collaborate with the server to train a global predictive model, thus achieving the goal of machine learning while protecting the privacy and sensitive data of end-devices. However, simultaneous access to the server by a large number of end devices may result in increased transmission latency and some local models may have malicious behavior, converging in the opposite direction to the global model. As a result, additional communication costs can occur during the federation learning process. Existing research has mainly focused on reducing the number of communication rounds or cleaning dirty local data. In order to decrease the overall amount of local updates, we provide an edge-based model cleaning and device clustering strategy in this study. By computing the cosine similarity between local update parameters and global model parameters over a multi-dimensional space, the approach assesses if local updates are necessary while preventing pointless communications. In addition, end devices are grouped together based on where they are connected to the network and connect to the cloud via mobile edge nodes in clusters, therefore lowering the latency associated with high concurrent server access. Convolutional neural networks and Soft max regression are used, for instance, to perform MNIST handwritten digit recognition, and the effectiveness of the suggested approach to enhancing communication efficiency is confirmed. According to experimental findings, the edge-based model cleaning and device clustering technique decreases the quantity of local updates by 60% and speeds up the model's convergence by 10.3% when compared to the conventional FL approach.

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“…Researchers have introduced series of remote sensing image processing methods based on CNN (Convolutional neural network) and verified the effectiveness of such algorithms [1]- [3]. CNN-based algorithms for SAR images are mostly applied in target detection, semantic segmentation, and classification [1], [4]- [8]. Chen et al [1] showed that baseline CNN structure can easily reach more than 97% test accuracy in ten-class classification task of moving and stationary target capture and recognition (MSTAR) dataset.…”

Section: Introductionmentioning

confidence: 99%

Dual Consistency Alignment Based Self-Supervised Learning for SAR Target Recognition With Speckle Noise Resistance

Zhai

Liao

Sun

et al. 2023

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

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Deep learning based on Convolutional Neural Networks (CNN) has been widely applied in Synthetic Aperture Radar (SAR) target recognition and made significant progress. However, due to the physical effects of the equipment used to collect images, various degrees of speckle noise will be introduced into SAR images. Traditional CNN based SAR target recognition methods are premised on the same noise intensity in the training and testing set, which is contrary to the target recognition in practice. To alleviate this problem, we propose a novel speckle noise resistant framework for SAR target recognition, called Dual Consistency Alignment based Self-Supervised Learning(DCA-SSL). Firstly, original SAR images are randomly added to speckle noise with different thresholds through multiplicative noise, after which contrastive pre-training is performed on unlabeled data. During this period, we combine instance pseudo-label consistency alignment and feature consistency alignment to align multiple threshold speckle noise views with original views under the same targets. Finally, the pre-trained model is migrated to the downstream SAR speckle noise target recognition task. In this article, speckle noise modeling is conducted based on MSTAR data testing set, and experiment results reveal that this method can adapt to different intensities of speckle noise, is robust to modeled SAR image recognition and maintains a high recognition rate even in small sample learning. Our code is available at: https://github.com/Jordan-Liao/DCA-SSL.

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SAR Image Segmentation Based on Constrained Smoothing and Hierarchical Label Correction

Cited by 17 publications

References 47 publications

Multimodal Colearning Meets Remote Sensing: Taxonomy, State of the Art, and Future Works

Multimodal Colearning Meets Remote Sensing: Taxonomy, State of the Art, and Future Works

cFL：Data distribution edge clustering algorithm based on deep Federated learning

Dual Consistency Alignment Based Self-Supervised Learning for SAR Target Recognition With Speckle Noise Resistance

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