NRN-RSSEG: A Deep Neural Network Model for Combating Label Noise in Semantic Segmentation of Remote Sensing Images

Xi, Mengfei; Li, Jie; He, Zhiwei; Yu, Minmin; Qin, Fen

doi:10.3390/rs15010108

Cited by 5 publications

(6 citation statements)

References 35 publications

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“…Wang et al [49] introduced a novel cross-modal interactive fusion method to enhance the interactivity of modal fusion by combining multisource information, greatly improving classification accuracy. Xi et al [50] incorporated an anti-label-noise network framework into semantic segmentation, enhancing the model's robustness by mitigating label noise. However, to the best of our knowledge, scant attention has been directed toward investigating high dynamic range imaging of remote sensing images under arbitrary exposure conditions with noise.…”

Section: Remote Sensing Image Enhancementmentioning

confidence: 99%

ERS-HDRI: Event-Based Remote Sensing HDR Imaging

Li,

Cheng,

Zeng

et al. 2024

Remote Sensing

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High dynamic range imaging (HDRI) is an essential task in remote sensing, enhancing low dynamic range (LDR) remote sensing images and benefiting downstream tasks, such as object detection and image segmentation. However, conventional frame-based HDRI methods may encounter challenges in real-world scenarios due to the limited information inherent in a single image captured by conventional cameras. In this paper, an event-based remote sensing HDR imaging framework is proposed to address this problem, denoted as ERS-HDRI, which reconstructs the remote sensing HDR image from a single-exposure LDR image and its concurrent event streams. The proposed ERS-HDRI leverages a coarse-to-fine framework, incorporating the event-based dynamic range enhancement (E-DRE) network and the gradient-enhanced HDR reconstruction (G-HDRR) network. Specifically, to efficiently achieve dynamic range fusion from different domains, the E-DRE network is designed to extract the dynamic range features from LDR frames and events and perform intra- and cross-attention operations to adaptively fuse multi-modal data. A denoise network and a dense feature fusion network are then employed for the generation of the coarse, clean HDR image. Then, the G-HDRR network, with its gradient enhancement module and multiscale fusion module, performs structure enforcement on the coarse HDR image and generates a fine informative HDR image. In addition, this work introduces a specialized hybrid imaging system and a novel, real-world event-based remote sensing HDRI dataset that contains aligned remote sensing LDR images, remote sensing HDR images, and concurrent event streams for evaluation. Comprehensive experiments have demonstrated the effectiveness of the proposed method. Specifically, it improves state-of-the-art PSNR by about 30% and the SSIM score by about 9% on the real-world dataset.

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Section: Remote Sensing Image Enhancementmentioning

confidence: 99%

ERS-HDRI: Event-Based Remote Sensing HDR Imaging

Li,

Cheng,

Zeng

et al. 2024

Remote Sensing

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“…Noise is a common feature of most datasets. As a result, research has been conducted into the handling of noise in all kinds of datasets and applications [48][49][50]. In this work, we explored the robustness of Feature Selection Algorithms through two kinds of noise: asymmetric label noise and irrelevant feature addition.…”

Section: Noise Resiliencementioning

confidence: 99%

Variable Selection in Data Analysis: A Synthetic Data Toolkit

Mitra,

Ali,

Varam

et al. 2024

Mathematics

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Variable (feature) selection plays an important role in data analysis and mathematical modeling. This paper aims to address the significant lack of formal evaluation benchmarks for feature selection algorithms (FSAs). To evaluate FSAs effectively, controlled environments are required, and the use of synthetic datasets offers significant advantages. We introduce a set of ten synthetically generated datasets with known relevance, redundancy, and irrelevance of features, derived from various mathematical, logical, and geometric sources. Additionally, eight FSAs are evaluated on these datasets based on their relevance and novelty. The paper first introduces the datasets and then provides a comprehensive experimental analysis of the performance of the selected FSAs on these datasets including testing the FSAs’ resilience on two types of induced data noise. The analysis has guided the grouping of the generated datasets into four groups of data complexity. Lastly, we provide public access to the generated datasets to facilitate bench-marking of new feature selection algorithms in the field via our Github repository. The contributions of this paper aim to foster the development of novel feature selection algorithms and advance their study.

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“…(1) Specific network architecture [20,[22][23][24][44][45][46][47]. For synthetic aperture radar images, Ref.…”

Section: Deep Neural Network-based Label Noise Learning In Remote Sen...mentioning

confidence: 99%

“…To suppress the impact of label noise on the semantic segmentation of RS images, Ref. [47] constructed a general network framework by combining an attention mechanism and a noise robust loss function. (2) Robust loss function [19,20,25,[47][48][49][50].…”

Section: Deep Neural Network-based Label Noise Learning In Remote Sen...mentioning

confidence: 99%

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Agreement and Disagreement-Based Co-Learning with Dual Network for Hyperspectral Image Classification with Noisy Labels

et al. 2023

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Deep learning-based label noise learning methods provide promising solutions for hyperspectral image (HSI) classification with noisy labels. Currently, label noise learning methods based on deep learning improve their performance by modifying one aspect, such as designing a robust loss function, revamping the network structure, or adding a noise adaptation layer. However, these methods face difficulties in coping with relatively high noise situations. To address this issue, this paper proposes a unified label noise learning framework with a dual-network structure. The goal is to enhance the model’s robustness to label noise by utilizing two networks to guide each other. Specifically, to avoid the degeneration of the dual-network training into self-training, the “disagreement” strategy is incorporated with co-learning. Then, the “agreement” strategy is introduced into the model to ensure that the model iterates in the right direction under high noise conditions. To this end, an agreement and disagreement-based co-learning (ADCL) framework is proposed for HSI classification with noisy labels. In addition, a joint loss function consisting of a supervision loss of two networks and a relative loss between two networks is designed for the dual-network structure. Extensive experiments are conducted on three public HSI datasets to demonstrate the robustness of the proposed method to label noise. Specifically, our method obtains the highest overall accuracy of 98.62%, 90.89%, and 99.02% on the three datasets, respectively, which represents an improvement of 2.58%, 2.27%, and 0.86% compared to the second-best method. In future research, the authors suggest using more networks as backbones to implement the ADCL framework.

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NRN-RSSEG: A Deep Neural Network Model for Combating Label Noise in Semantic Segmentation of Remote Sensing Images

Cited by 5 publications

References 35 publications

ERS-HDRI: Event-Based Remote Sensing HDR Imaging

ERS-HDRI: Event-Based Remote Sensing HDR Imaging

Variable Selection in Data Analysis: A Synthetic Data Toolkit

Agreement and Disagreement-Based Co-Learning with Dual Network for Hyperspectral Image Classification with Noisy Labels

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