Remote sensing inversion of suspended particulate matter in the estuary of the Pinglu Canal in China based on machine learning algorithms DOI Creative Commons
Jun Mo,

Yichao Tian,

Jiale Wang

et al.

Frontiers in Marine Science, Journal Year: 2024, Volume and Issue: 11

Published: Nov. 14, 2024

Introduction Suspended particulate matter (SPM) is a critical indicator of water quality and has significant impact on the nearshore ecological environment. Consequently, quantitative evaluation SPM concentrations essential for managing environments planning marine resources. Methods This study utilized Sentinel-2’s single band index variables to develop remote sensing inversion model oceanic in estuary Pinglu Canal China. Six machine learning algorithms were employed: K-nearest neighbor regression (KNNR), AdaBoost (ABR), random forest (RF), gradient boosting (GBR), extreme (XGBR), light generalized boosted (LGBM). The with optimal performance was then selected further analysis. research applied established investigate spatial-temporal dynamics from 2021 2023. Results findings indicated that (1) XGBR algorithm exhibited superior (R 2 = 0.9042, RMSE 3.0258 mg/L), LGBM =0.8258, 4.0813 mg/L) GBR 0.823, 4.3477 also demonstrating effective fitting. However, ABR, RF, KNNR produced less satisfactory fitting results. (2) Additionally, revealed combination input more accurate than single-variable inputs. (3) contribution single-band surpassed variables, B12, B4, B11 emerging as top three influential model. (4) annual concentration area an overall increasing trend, while its spatial distribution generally decreased toward Maowei Sea Qinzhou Bay. Discussion Sentinel-2 data shown good retrieving concentration, providing new method approach large-scale estimation concentration.

Language: Английский

Synthetic Aperture Radar Image Change Detection Based on Principal Component Analysis and Two-Level Clustering DOI Creative Commons
Liangliang Li, Hongbing Ma,

Xueyu Zhang

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(11), P. 1861 - 1861

Published: May 23, 2024

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

Language: Английский

Citations

23
Infrared and Visible Image Fusion via Sparse Representation and Guided Filtering in Laplacian Pyramid Domain DOI Creative Commons
Liangliang Li, Yan Shi,

Ming Lv

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(20), P. 3804 - 3804

Published: Oct. 13, 2024

The fusion of infrared and visible images together can fully leverage the respective advantages each, providing a more comprehensive richer set information. This is applicable in various fields such as military surveillance, night navigation, environmental monitoring, etc. In this paper, novel image method based on sparse representation guided filtering Laplacian pyramid (LP) domain introduced. source are decomposed into low- high-frequency bands by LP, respectively. Sparse has achieved significant effectiveness fusion, it used to process low-frequency band; excellent edge-preserving effects effectively maintain spatial continuity band. Therefore, combined with weighted sum eight-neighborhood-based modified (WSEML) bands. Finally, inverse LP transform reconstruct fused image. We conducted simulation experiments publicly available TNO dataset validate superiority our proposed algorithm fusing images. Our preserves both thermal radiation characteristics detailed features

Language: Английский

Citations

10
A deep learning and image enhancement based pipeline for infrared and visible image fusion DOI
Jin Qi, Deboch Eyob Abera,

Mola Natnael Fanose

et al.

Neurocomputing, Journal Year: 2024, Volume and Issue: 578, P. 127353 - 127353

Published: Feb. 10, 2024

Language: Английский

Citations

8
Fractal Dimension-Based Multi-Focus Image Fusion via Coupled Neural P Systems in NSCT Domain DOI Creative Commons
Liangliang Li, Xiaobin Zhao,

Huayi Hou

et al.

Fractal and Fractional, Journal Year: 2024, Volume and Issue: 8(10), P. 554 - 554

Published: Sept. 25, 2024

In this paper, we introduce an innovative approach to multi-focus image fusion by leveraging the concepts of fractal dimension and coupled neural P (CNP) systems in nonsubsampled contourlet transform (NSCT) domain. This method is designed overcome challenges posed limitations camera lenses depth-of-field effects, which often prevent all parts a scene from being simultaneously focus. Our proposed technique employs CNP with local topology-based model merge low-frequency components effectively. Meanwhile, for high-frequency components, utilize spatial frequency dimension-based focus measure (FDFM) achieve superior performance. The effectiveness validated through extensive experiments conducted on three benchmark datasets: Lytro, MFI-WHU, MFFW. results demonstrate superiority our method, showcasing its potential significantly enhance clarity across entire scene. algorithm has achieved advantageous values metrics QAB/F, QCB, QCV, QE, QFMI, QG, QMI, QNCIE.

Language: Английский

Citations

7
Study on the extraction method of Glycyrrhiza uralensis Fisch. distribution area based on Gaofen-1 remote sensing imagery: a case study of Dengkou county DOI Creative Commons

Xinxin Wei,

Zeyuan Zhao,

Taiyang Chen

et al.

Frontiers in Plant Science, Journal Year: 2025, Volume and Issue: 16

Published: March 7, 2025

Glycyrrhiza uralensis Fisch., a perennial medicinal plant with robust root system, plays significant role in mitigating land desertification when cultivated extensively. This study investigates Dengkou County, semi-arid region, as the research area. First, reflectance differences of feature types, and importance bands were evaluated by using random forest (RF) algorithm. Second, after constructing G. vegetation index (GUVI), recognition accuracy was compared between RF classification model constructed based on January-December GUVI common indices set support vector machine (SVM) set. Finally, spectral characteristics other types under 2022 analyzed, historical distribution identified mapped. The results demonstrated that blue near-infrared are particularly for distinguishing . Incorporating year-round (January-December) data significantly improved identification accuracy, achieving producer’s 97.26%, an overall 93.00%, Kappa coefficient 91.38%, user’s 97.32%. Spectral analysis revealed distinct different years types. From 2014 to 2022, expanded from northeast County central southwestern regions, transitioning small, scattered patches larger, concentrated areas. highlights effectiveness models identifying , demonstrating superior performance alternative sets or algorithms. However, generalizability may be limited due influence natural anthropogenic factors Therefore, regional adjustments optimization parameters necessary. provides valuable reference employing remote sensing technology accurately map current regions similar environmental conditions.

Language: Английский

Citations

0
MERFusion: A multiscale edge-preserving filter combined with Retinex enhancement for infrared and visible image fusion DOI
Chenxuan Yang, Yunan He, C.C. Sun

et al.

Optics & Laser Technology, Journal Year: 2025, Volume and Issue: 188, P. 112823 - 112823

Published: April 4, 2025

Language: Английский

Citations

0
Robust Infrared–Visible Fusion Imaging with Decoupled Semantic Segmentation Network DOI Creative Commons
Xuhui Zhang, Yunpeng Yin, Zhuowei Wang

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(9), P. 2646 - 2646

Published: April 22, 2025

The fusion of infrared and visible images provides complementary information from both modalities has been widely used in surveillance, military, other fields. However, most the available methods have only evaluated with subjective metrics visual quality fused images, which are often independent following relevant high-level tasks. Moreover, as a useful technique especially low-light scenarios, effect conditions on result not well-addressed yet. To address these challenges, decoupled semantic segmentation-driven image network is proposed this paper, connects downstream task to drive be optimized. Firstly, cross-modality transformer module designed learn rich hierarchical feature representations. Secondly, semantic-driven developed enhance key features prominent targets. Thirdly, weighted strategy adopted automatically adjust weights different modality features. This effectively merges thermal characteristics detailed images. Additionally, we design refined loss function that employs decoupling constrain pixel distributions produce more-natural evaluate robustness generalization method practical challenge applications, Maritime Infrared Visible (MIV) dataset created verified for maritime environmental perception, will made soon. experimental results public datasets practically collected MIV highlight notable strengths best-ranking among its counterparts. Of more importance, achieved over 96% target detection accuracy dominant high mAP@[50:95] value far surpasses all competitors.

Language: Английский

Citations

0
Multi-Focus Image Fusion via PAPCNN and Fractal Dimension in NSST Domain DOI Creative Commons

Ming Lv,

Zhenhong Jia, Liangliang Li

et al.

Mathematics, Journal Year: 2023, Volume and Issue: 11(18), P. 3803 - 3803

Published: Sept. 5, 2023

Multi-focus image fusion is a popular technique for generating full-focus image, where all objects in the scene are clear. In order to achieve clearer and fully focused effect, this paper, multi-focus method based on parameter-adaptive pulse-coupled neural network fractal dimension nonsubsampled shearlet transform domain was developed. The pulse coupled network-based rule used merge low-frequency sub-bands, dimension-based via multi-scale morphological gradient high-frequency sub-bands. inverse reconstruct fused coefficients, final generated. We conducted comprehensive evaluations of our algorithm using public Lytro dataset. proposed compared with state-of-the-art algorithms, including traditional deep-learning-based approaches. quantitative qualitative demonstrated that outperformed other as evidenced by metrics data such QAB/F, QE, QFMI, QG, QNCIE, QP, QMI, QNMI, QY, QAG, QPSNR, QMSE. These results highlight clear advantages fusion, providing significant contribution field.

Language: Английский

Citations

4
Research on Infrared and Visible Image Fusion DOI

义坪 毛

Computer Science and Application, Journal Year: 2023, Volume and Issue: 13(08), P. 1569 - 1575

Published: Jan. 1, 2023

Language: Английский

Citations

2
Real-time fault detection for IIoT facilities using GA-Att-LSTM based on edge-cloud collaboration DOI Creative Commons

Jiuling Dong,

Zehui Li, Yuanshuo Zheng

et al.

Frontiers in Neurorobotics, Journal Year: 2024, Volume and Issue: 18

Published: Nov. 11, 2024

With the rapid development of Industrial Internet Things (IIoT) technology, various IIoT devices are generating large amounts industrial sensor data that spatiotemporally correlated and heterogeneous from multi-source multi-domain. This poses a challenge to current detection algorithms. Therefore, this paper proposes an improved long short-term memory (LSTM) neural network model based on genetic algorithm, attention mechanism edge-cloud collaboration (GA-Att-LSTM) framework is proposed detect anomalies facilities. Firstly, established real-time process amount at edge node in real time, which reduces time uploading cloud platform. Secondly, overcome problem insufficient important features input sequence traditional LSTM algorithms, we introduce adaptively adjust weights model. Meanwhile, algorithm optimized hyperparameters transform anomaly into classification effectively extract correlation time-series data, improves recognition rate fault detection. Finally, method has been evaluated publicly available database. The results indicate accuracy 99.6%, F1-score 84.2%, precision 89.8%, recall 77.6%, all exceed performance five machine learning methods.

Language: Английский

Citations

0