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7th International Conference on image and Graphics processing, ICIGP 2024

作者： Li, Jinyu Zhang, Mengmeng Beijing Institute of Technology China

ISBN: (纸本)9798400716720

This study addresses the challenges in remote sensing scene recognition, traditionally treated as an image classification problem, leading to issues with false positives and false negatives, especially in complex images. We propose a paradigm shift by framing scene recognition as an advanced object detection task and introduce a specialized dataset to assess models in realistic scenarios. Our approach includes StemNet, an innovative fusion technique integrating hyperspectral and RGB imagery, surpassing traditional methods in accuracy, precision, and robustness. Through extensive experimentation, StemNet consistently outperforms conventional techniques, offering a groundbreaking perspective and setting a benchmark for future methodologies in remote sensing scene recognition. The introduced dataset and StemNet contribute significantly to advancing research and practice in this field. © 2024 ACM.

关键词： Object detection

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MHRA-MS-3D-ResNet-BiLSTM: A Multi-Head-Residual Attention-Based Multi-Stream Deep Learning Model for Soybean Yield Prediction in the US Using Multi-Source remote sensing Data

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remote sensing 2025年第1期17卷 107-107页

作者： Fathi, Mahdiyeh Shah-Hosseini, Reza Moghimi, Armin Arefi, Hossein Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran *** Iran Hsch Mainz Univ Appl Sci I3mainz D-55128 Mainz Germany Leibniz Univ Hannover Ludwig Franzius Inst Hydraul Estuarine & Coastal E Nienburger Str 4 D-30167 Hannover Germany

Accurate prediction of soybean yield is important for safeguarding food security and improving agricultural management. Recent advances have highlighted the effectiveness and ability of Machine Learning (ML) models in analyzing remote sensing (RS) data for this purpose. However, most of these models do not fully consider multi-source RS data for prediction, as processing these increases complexity and limits their accuracy and generalizability. In this study, we propose the Multi-Residual Attention-Based Multi-Stream 3D-ResNet-BiLSTM (MHRA-MS-3D-ResNet-BiLSTM) model, designed to integrate various RS data types, including Sentinel-1/2 imagery, Daymet climate data, and soil grid information, for improved county-level U.S. soybean yield prediction. Our model employs a multi-stream architecture to process diverse data types concurrently, capturing complex spatio-temporal features effectively. The 3D-ResNet component utilizes 3D convolutions and residual connections for pattern recognition, complemented by Bidirectional Long Short-Term Memory (BiLSTM) for enhanced long-term dependency learning by processing data arrangements in forward and backward directions. An attention mechanism further refines the model's focus by dynamically weighting the significance of different input features for efficient yield prediction. We trained the MHRA-MS-3D-ResNet-BiLSTM model using multi-source RS datasets from 2019 and 2020 and evaluated its performance with U.S. soybean yield data for 2021 and 2022. The results demonstrated the model's robustness and adaptability to unseen data, achieving an R2 of 0.82 and a Mean Absolute Percentage Error (MAPE) of 9% in 2021, and an R2 of 0.72 and MAPE of 12% in 2022. This performance surpassed some of the state-of-the-art models like 3D-ResNet-BiLSTM and MS-3D-ResNet-BiLSTM, and other traditional ML methods like Random Forest (RF), XGBoost, and LightGBM. These findings highlight the methodology's capability to handle multiple RS data types and its

关键词： soybean yield prediction Multi-Stream Residual Attention Multi-Head Attention 3D-ResNet-biLSTM Sentinel-1 Sentinel-2 Daymet Soil Grid Google Earth Engine (GEE)

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Hyperspectral image Change Detection via Cross-Sample Slot Attention and Dual Gated Feed-Forward Network 7th

Hyperspectral Image Change Detection via Cross-Sample Slot A...

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7th Chinese Conference on pattern recognition and Computer Vision

作者： Cheng, Luyao Lin, Junyan Gao, Feng Ocean Univ China Sch Comp Sci & Technol Qingdao Peoples R China

ISBN: (纸本)9789819784929;9789819784936

Hyperspectral image (HSI) change detection is a key research topic in the field of remote sensing. Existing HSI change detection methods often overlook the potential interactions among training samples. To address this issue, we develop a novel HSI change detection network, the Cross-Sample Slot attention-based Network (CSSNet). This network, building on the slot attention mechanism, can explicitly distinguish between changed and unchanged region representations and disentangle these representations by multiple independent concepts. These concepts are instrumental in capturing the uniformity and diversity in the representations among different samples during batch processing. Furthermore, we introduced a Dual Gated Feed-forward Network (DGFN) to effectively filter out redundant and irrelevant information. Experimental results on two different HSI datasets demonstrate that CSSNet outperforms several existing mainstream methods in performance.

关键词： Hyperspectral image Change Detection Cross-Sample Slot Attention

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Circle Representation Network for Specific Target Detection in remote sensing images 6th

Circle Representation Network for Specific Target Detection ...

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6th Chinese Conference on pattern recognition and Computer Vision (PRCV)

作者： Yang, Xiaoyu Ge, Yun Peng, Haokang Leng, Lu Nanchang Hangkong Univ Sch Software Nanchang 330063 Jiangxi Peoples R China Jiangxi Huihang Engn Consulting Co Ltd Nanchang 330038 Jiangxi Peoples R China

ISBN: (纸本)9789819984619;9789819984626

Compared with natural images, remote sensing images have complex backgrounds as well as a variety of targets. The circular and square-like targets are very common in remote sensing images. For such specific targets, it is easy to bring background information when using the traditional bounding box. To address this issue, we propose a Circle Representation Network (CRNet) to detect the circular or square-like targets. We design a special network head to regression radius and it has smaller regression degrees of freedom. Then the bounding circle is proposed to represent the specific targets. Compared to the bounding box, the bounding circle has natural rotational invariance. The CRNet can accurately locate the object while carrying less background information. In order to reasonably evaluate the detection performance, we further propose the circle-IOU to calculate the mAP. The experiments evaluated on NWPU VHR-10 and RSOD datasets show that the proposed method has excellent performance when detecting circular and square-like objects, in which the detection accuracy of storage tanks is improved from 92.1% to 94.4%. Therefore, the CRNet is a simple and efficient detection method for the circular and square-like targets.

关键词： specific target detection remote sensing image bounding circles circle-IOU

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A novel NAM-based image segmentation using hierarchical density-based spatial clustering

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IET image processing 2024年第5期18卷 1245-1257页

作者： Zheng, Yunping Wen, Dilong Sarem, Mudar South China Univ Technol Sch Comp Sci & Engn Guangzhou Guangdong Peoples R China Gen Org Remote Sensing GORS Damascus Syria South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China

This paper proposes a new method for hierarchical image segmentation based on the nonsymetry and anti-packing pattern representation model (NAM) and the hierarchical density-based spatial clustering of application with noise (HDBSCAN). The proposed framework consists of two phases. In the first phase, a super-pixel generation algorithm base on NAM is proposed. In the second phase, instead of defining an affinity matrix to merge similar regions using spatial clustering, the distance matrix defined by different region features is directly fitted into an HDBSCAN clustering module in order to merge similar regions efficiently. Similar adjacent regions can be merged into larger ones progressively and form a segmentation dendrogram for image segmentation with the clustering module. The experiments show that the proposed algorithm has a comparable or even better performance compared to the state-of-the-art hierarchical image segmentation algorithms while having much less time and memory consumption. A new method based on the nonsymetry and anti-packing pattern representation model algorithm to generate super-pixels, which used for computation reduction, is proposed. Instead of defining an affinity matrix to facilitate the clustering of similar regions as in ICM, a different matrix is directly used to merge similar regions using the hierarchical density-based spatial clustering of application with noise clustering algorithm, which contribute to improving the speed and the memory ***

关键词： image processing image representation

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ML-SCODNet: Multitask Learning for Scene Classification and Object Detection Network from remote sensing images 7th

ML-SCODNet: Multitask Learning for Scene Classification and ...

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7th Chinese Conference on pattern recognition and Computer Vision

作者： Khaled, Khaled Fan, Shuyu Lian, Yuanfeng China Univ Petr Coll Artificial Intelligence Beijing 102249 Peoples R China China Univ Petr Dept Comp Sci & Technol Beijing Key Lab Petr Data Min Beijing 102249 Peoples R China

ISBN: (纸本)9789819788576;9789819788583

This paper presents a novel and efficient multitask learning for scene classification and object detection from remote sensing images, which mainly contains the Non-Independent and Identically Distributed Fisher Vector Network (NIID-FVNet) for scene classification and Two-Stream CNN-Transformer Network (TSCTNet) for object detection. NIID-FVNet is designed to classify the input image into inshore or offshore scene. Then, TSCTNet is proposed for extracting more informative salient and edge features, in which a three-stream decoder consisting of a saliency stream, edge stream and feature fusion stream is introduced to enhance the detection result by taking full advantage of the extracted information from different modalities. Finally, the salient-aware module and edge-aware module are designed to generate more accurate saliency detection results with clear boundaries. Experimental results demonstrate that the presented scene classification and object detection networks outperforms other approaches.

关键词： Multitask learning Scene classification Object detection remote sensing Non-independent and identically distributed

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EHAT:Enhanced Hybrid Attention Transformer for remote sensing image Super-Resolution 7th

EHAT:Enhanced Hybrid Attention Transformer for Remote Sensin...

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7th Chinese Conference on pattern recognition and Computer Vision

作者： Wang, Jian Xie, Zexin Du, Yanlin Song, Wei Shanghai Ocean Univ Coll Informat Technol Shanghai Peoples R China

ISBN: (纸本)9789819786848;9789819786855

In recent years, deep learning (DL)-based super-resolution techniques for remote sensing images have made significant progress. However, these models have constraints in effectively managing long-range non-local information and reusing features, while also encountering issues such as gradient vanishing and explosion. To overcome these challenges, we propose the Enhanced Hybrid Attention Transformer (EHAT) framework, which is based on the Hybrid Attention Transformer (HAT) network backbone and combines a region-level nonlocal neural network block and a skip fusion network SFN to form a new skip fusion attention group (SFAG). In addition, we form a Multi-attention Block (MAB) by introducing spatial frequency block (SFB) based on fast Fourier convolution. We have conducted extensive experiments on Uc Merced, CLRS and RSSCN7 datasets. The results show that our method improves the PSNR by about 0.2 dB on Uc Mercedx4.

关键词： Vison Transformer remote sensing self attention super resolution Nonlocal neural Network

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Research and Application of YOLOv3-Based Intelligent Module for remote sensing image recognition on Satellites 3

Research and Application of YOLOv3-Based Intelligent Module ...

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3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Tang, Hua Ye, Bingxin Shi, Huajun Wang, He Yu, Tao China Electronics Technology Group Corporation China The 32nd Research Institute of China Electronics Technology Group Corporation China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

With the continuous development of satellite technology, the acquisition and processing of satellite remote sensing data have become increasingly common. However, at present, satellites still use a mode of transmitting data back to the ground and utilizing ground CPU/GPU platforms for intelligent processing, which is inefficient due to the process of data transmission between the satellite and the ground. This article studies the application method and implementation of real-time remote sensing image recognition by integrating the Yulong810 on-board intelligent module on a satellite based on the YOLOv3 object detection *** response to the actual needs of satellite remote sensing image recognition, the algorithm is optimized based on the Yulong810 on-board intelligent module, which improves the recognition efficiency and reduces power consumption while ensuring the accuracy of remote sensing image recognition. By comparing with ground CPU/GPU platforms, the results show that the Yulong810 on-board intelligent module has lower power consumption and higher efficiency, and the recognition accuracy is comparable to that of ground platforms, which can completely replace ground remote sensing image detection equipment when integrated into *** integrating the module into the satellite, real-time intelligent processing can be achieved in orbit, improving the efficiency of satellite remote sensing image recognition. © 2023 SPIE.

关键词： image recognition

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Efficient and robust techniques for infrared imaging system correction

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IMAGING SCIENCE JOURNAL 2025年第3期73卷 381-400页

作者： Hamadouche, Sid Ahmed Boutemedjet, Ayoub Bouaraba, Azzedine Ecole Mil Polytech Lab Syst Lasers BP 17 Bordj El Bahri Algiers 16111 Algeria Ecole Mil Polytech Lab Radar Algiers Algeria

Stripe noise is a prevalent issue in infrared imaging systems, characterized by its distinctive directional features, which often appear as vertical lines across the image. This type of noise can significantly degrade the quality of the captured images, making it crucial to address and mitigate its effects. This paper presents an effective strategy to tackle this problem by transforming it from a 2D image issue into a 1D signal problem, enabling efficient resolution of stripes in infrared images. By understanding the characteristics of stripe noise, the proposed algorithm effectively solves the problem by first computing the column average of the noisy image, extracting stripe components from this one-dimensional signal, and effectively removing the stripes without blurring image details. This approach has been tested on numerous images with varying noise levels, demonstrating exceptional denoising performance compared to state-of-the-art methods. The results show marked improvements in visual quality, especially around edges and smooth areas, without requiring complex algorithms or iterative processes.

关键词： Stripe noise image denoising infrared imaging systems non-uniformity correction fixed pattern noise image processing remote sensing

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Satellite image recognition using ensemble neural networks and difference gradient positive-negative momentum

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CHAOS SOLITONS & FRACTALS 2024年 179卷

作者： Abdulkadirov, R. Lyakhov, P. Bergerman, M. Reznikov, D. North Caucasus Fed Univ North Caucasus Ctr Math Res Stavropol Russia North Caucasus Fed Univ Dept Math Modeling Stavropol Russia

The modern machine learning theory finds application in many areas of human activity. One of the most dispersed tasks is pattern recognition on satellite images. It is difficult for a person to recognize a large number of images in a short time. It made the researchers develop the automation process, such as neural network engagement. The loss function minimization and ensemble learning raise the pattern recognition accuracy. We propose the robust difference gradient positive-negative momentum optimization algorithm that achieves the global minimum of the loss function with higher accuracy and fewer iterations than known analogs. Such an optimization algorithm contains the generalized average moving estimation approach and more effective learning rate control by additional parameters. The proposed optimizer has the regret-bound rate estimation, belonging to O � root T ), and converges to the global minimum. However, the main problems in optimization theory are vanishing and blowing gradient values, where the standard gradient-based algorithms fail to achieve the required objective function value. The vanishing and blowing gradient problems meet in Rastrigin and Rosebrock test functions, where the proposed optimization algorithm attains the global extreme in the shortest number of iterations and has a more stable convergence process than state-of-the-art methods. Afterward, there are trained deep convolutional neural networks with different optimizers on satellite images from the University of California merced dataset containing 21 object classes, where the proposed algorithm gives the highest accuracy. There is a suggested ensemble-learning model consisting of 4 networks with different optimizers. The prediction results receive weight coefficients distributed according to the majority voting and ensemble neural network retrains with the higher pattern recognition accuracy. The suggested ensemble-learning model with the developed optimizer raised the accuracy by 1 %

关键词： Optimization methods Positive-negative moving averages Ensemble-learning pattern recognition remote sensing

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