Adversarial Complementary Learning for Multisource Remote Sensing Classification

Gao, Yunhao; Zhang, Mengmeng; Li, Wei; Song, Xiukai; Jiang, Xiangyang; Ma, Yuanqing

doi:10.1109/tgrs.2023.3255880

Cited by 45 publications

(10 citation statements)

References 52 publications

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“…The effectiveness of change detection methods is assessed through both qualitative and quantitative measures. For quantitative analysis, we employ metrics such as false negatives (FNs) [30], false positives (FPs) [31][32][33][34], overall error (OE) [35], percentage of correct classifications (PCCs) [36], Kappa coefficient (KC) [37][38][39], and F1-score [40,41]. The formulas for these metrics are provided in Table 2…”

Section: Experimental Datasets and Evaluation Criteriamentioning

confidence: 99%

Synthetic Aperture Radar Image Change Detection Based on Principal Component Analysis and Two-Level Clustering

Li,

Ma,

Zhang

et al. 2024

Remote Sensing

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Synthetic aperture radar (SAR) change detection provides a powerful tool for continuous, reliable, and objective observation of the Earth, supporting a wide range of applications that require regular monitoring and assessment of changes in the natural and built environment. In this paper, we introduce a novel SAR image change detection method based on principal component analysis and two-level clustering. First, two difference images of the log-ratio and mean-ratio operators are computed, then the principal component analysis fusion model is used to fuse the two difference images, and a new difference image is generated. To incorporate contextual information during the feature extraction phase, Gabor wavelets are used to obtain the representation of the difference image across multiple scales and orientations. The maximum magnitude across all orientations at each scale is then concatenated to form the Gabor feature vector. Following this, a cascading clustering algorithm is developed within this discriminative feature space by merging the first-level fuzzy c-means clustering with the second-level neighbor rule. Ultimately, the two-level combination of the changed and unchanged results produces the final change map. Five SAR datasets are used for the experiment, and the results show that our algorithm has significant advantages in SAR change detection.

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Section: Experimental Datasets and Evaluation Criteriamentioning

confidence: 99%

Synthetic Aperture Radar Image Change Detection Based on Principal Component Analysis and Two-Level Clustering

Li,

Ma,

Zhang

et al. 2024

Remote Sensing

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“…10: Relative performance gain analysis. 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].…”

Section: A Data 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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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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“…I MPROVING the resolution of satellite imagery can enhance the performance of sub-tasks such as semantic segmentation [1], object detection [2], change detection [3], and data fusion [4]. It can also find active utilization in military applications or the assessment of damage extent resulting from natural disasters.…”

Section: Introductionmentioning

confidence: 99%

Bridging the Domain Gap: A Simple Domain Matching Method for Reference-Based Image Super-Resolution in Remote Sensing

Min,

Lee,

Kim

et al. 2024

IEEE Geosci. Remote Sensing Lett.

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Recently, reference-based image super-resolution (RefSR) has shown excellent performance in image superresolution (SR) tasks. The main idea of RefSR is to utilize additional information from the reference (Ref) image to recover the high-frequency components in low-resolution (LR) images. By transferring relevant textures through feature matching, RefSR models outperform existing single image super-resolution (SISR) models. However, their performance significantly declines when a domain gap between Ref and LR images exists, which often occurs in real-world scenarios, such as satellite imaging. In this letter, we introduce a Domain Matching (DM) module that can be seamlessly integrated with existing RefSR models to enhance their performance in a plug-and-play manner. To the best of our knowledge, we are the first to explore Domain Matching-based RefSR in remote sensing image processing. Our analysis reveals that their domain gaps often occur in different satellites, and our model effectively addresses these challenges, whereas existing models struggle. Our experiments demonstrate that the proposed DM module improves SR performance both qualitatively and quantitatively for remote sensing super-resolution tasks.

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Adversarial Complementary Learning for Multisource Remote Sensing Classification

Cited by 45 publications

References 52 publications

Synthetic Aperture Radar Image Change Detection Based on Principal Component Analysis and Two-Level Clustering

Synthetic Aperture Radar Image Change Detection Based on Principal Component Analysis and Two-Level Clustering

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

Bridging the Domain Gap: A Simple Domain Matching Method for Reference-Based Image Super-Resolution in Remote Sensing

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