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ISPRS Technical Commission I Midterm Symposium on Intelligent sensing and remote sensing Application

作者： Wang, Lei Peng, Lingmu Hong, Hanyu Zhao, Shuwei Lv, Qiwen Gui, Rong Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Hubei Wuhan430205 China School of Geosciences and Info-physics Central South University Changsha410083 China

The lack of precisely labeled data limits the development of supervised polarimetric synthetic aperture radar (PolSAR) image change detection. Therefore, semi-supervised deep learning methods have recently demonstrated their significant capability for PolSAR image change detection. Similarity Matching (SimMatch) improves the performance of semi-supervised learning tasks across different benchmark datasets and different settings. Introducing SimMatch into the field of PolSAR image change detection can improve the performance of semi-supervised PolSAR image change detection under limited labeled data conditions. Usually, semi-supervision solves the problem of insufficient labeled data by generating pseudo-labels. However, when the pseudo-label method is simply applied, the model will fit on the confident but wrong pseudo-labels, resulting in poor performance. SimMatch offers a solution by requiring the strongly augmented view to share the same semantic similarity (i.e. label prediction) and instance characteristics (i.e. similarity between instances) with a weak augmented view for more intrinsic feature matching. Besides, by using a labeled memory buffer, the two similarities can be isomorphically transformed with each other by introducing the aggregating and unfolding techniques. Therefore, the semantic and instance pseudo-labels can be mutually propagated, and then, the detection performance of the PolSAR image change detection is improved. Experimental results on real PolSAR datasets demonstrated that SimMatch is an effective semi-supervised PolSAR change detection method and its performance surpasses some well-known change detection methods. Compared to the fully-supervised algorithm CWNN, the semi-supervised SimMatch algorithm can improve accuracy by up to 14.4%. © Author(s) 2024.

关键词： Change detection

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Streaming Point Cloud Segmentation of GNSS/SLAM-LiDAR and Multi-beam Scanning Sonar Data for Urban River Mapping 45

Streaming Point Cloud Segmentation of GNSS/SLAM-LiDAR and Mu...

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45th Asian Conference on remote sensing, ACRS 2024

作者： Nakagawa, Masafumi Kimura, Naoto Sadachika, Nozomi Komori, Takeshi Kubo, Nobuaki Shimizu, Etsuro Shibaura Institute of Technology Japan Tokyo University of Marine Science and Technology Japan

Simultaneous localization and mapping (SLAM) can provide point clouds and self-position data for 3D mapping, virtual reality, augmented reality, mixed reality, UAV flight control, and autonomous vehicle control. In 3D mapping, 3D object recognition based on model fitting and machine learning is required for the automation of 3D mapping and modeling using dense point clouds generated by SLAM and mobile mapping. The machine learning-based 3D object recognition is classified into clustering and segmentation. The segmentation is fundamental processing for 3D mapping, scan-to-BIM, and autonomous vehicles using point clouds. The segmentation can be classified into image-based and 3D-based approaches. Each approach has strengths and weaknesses, thus we focus on the integration of image-based and 3D-based approaches embedded in SLAM and mobile mapping. In this research, we propose a methodology to improve the performance of streaming point cloud processing based on image-based and 3D-based point cloud segmentation for 3D mapping of urban river environments. We also developed a methodology of streaming point cloud segmentation embedded in GNSS/LiDAR-SLAM and multi-beam scanning. In our experiments, we used a water-borne mobile mapping system at an urban river as GNSS and non-GNSS environments. We acquired dense streaming point clouds above water surfaces with GNSS/LiDAR-SLAM consisting of two LiDARs and precise point positioning based on real-time kinematic positioning with a centimeter-level augmentation service using a quasi-zenith satellite system. In parallel, we also acquired dense streaming point clouds underwater surfaces with a multi-beam scanning sonar with RTK-GNSS positioning. Moreover, we experimented with streaming point cloud segmentation of acquired massive point clouds to classify streaming point clouds into bridges, revetments, buildings, and underwater surfaces. Through the experiment using the streaming point clouds, we confirmed that our methodology can i

关键词： image segmentation

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Mamba-Based Residual Network for remote sensing image Super-Resolution 7

Mamba-Based Residual Network for Remote Sensing Image Super-...

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7th International Conference on pattern recognition and Artificial Intelligence, PRAI 2024

作者： Zhao, Gongbo Wang, Feng Shanghai200433 China Yiwu Research Institute Fudan University Yiwu322000 China

ISBN: (纸本)9798350350890

image super-resolution is the process of recovering high-resolution images from low-resolution inputs, which is crucial in low-level computer vision tasks. In recent years, progress in deep learning has significantly propelled the development of image super-resolution technology. Various models, such as convolutional neural networks (CNNs), generative adversarial networks, Transformers, and diffusion models, have been employed to address this problem with significant impact. Among these models, CNNs are effective in capturing local features but have limitations in modeling long-range dependencies;Transformers, despite their powerful modeling capabilities, face computational bottlenecks due to the quadratic complexity of the self-attention mechanism when dealing with large images. To address these issues, we propose the Residual Mamba Block, which combines state space models (SSMs) with residual connections, providing a linear complexity attention mechanism that effectively enhances long-range dependency modeling. Experimental results demonstrate the effectiveness and superior performance of the proposed method. Furthermore, our research indicates that vision state space models have great potential to become a foundational framework for future super-resolution tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhanced visual detection of litchi fruit in complex natural environments based on unmanned aerial vehicle (UAV) remote sensing

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PRECISION AGRICULTURE 2025年第1期26卷 1-24页

作者： Liang, Changjiang Liang, Juntao Yang, Weiguang Ge, Weiyi Zhao, Jing Li, Zhaorong Bai, Shudai Fan, Jiawen Lan, Yubin Long, Yongbing South China Agr Univ Coll Elect Engn Coll Artificial Intelligence Guangzhou 510642 Peoples R China Guangdong Lab Lingnan Modern Agr Guangzhou 510642 Peoples R China Natl Ctr Int Collaborat Res Precis Agr Aviat Pesti Guangzhou 510642 Peoples R China Minist Agr & Rural Affairs South China Smart Agr Publ Res & Dev Ctr Guangzhou 510642 Peoples R China

Rapid and accurate detection of fruits is crucial for estimating yields and making scientific decisions in litchi orchards. However, litchis grow in complex natural environments, characterized by variable lighting, severe occlusion from branches and leaves, small fruit sizes, and dense overlapping, all of which pose significant challenges for accurate detection. This paper addressed this problem by proposing a method that combines unmanned aerial vehicle (UAV) remote sensing and deep learning for litchi detection. A remote sensing image dataset comprising litchi fruit was first constructed. Subsequently, an improved algorithm, YOLOv7-MSRSF, was developed. Experimental results demonstrated that YOLOv7-MSRSF's mean average precision (mAP) reached 96.1%, outperforming YOLOv7 and pure transformer algorithms by 3.7% and 20.6%, respectively. Tests on randomly selected 24 images demonstrated that integrating the Swin-transformer module into YOLOv7 improved litchi fruit detection accuracy under severe occlusion, dense overlapping, and variable lighting by 19.55%, 6.63%, and 13.94%, respectively. YOLOv7-MSRSF showed further improvements in these three complex conditions, with detection accuracy increasing by 26.99%, 9.82%, and 18.68%, respectively, reaching 89.16%, 97.79%, and 95.56%. Furthermore, the Real-ESRGAN algorithm significantly enhanced the YOLOv7-MSRSF model's recognition accuracy of objects in low-resolution images captured by high-altitude drones. The average detected accuracy of three images collected at 27.5 m above the canopy reached a high value of 82.2%, which was improved by 70.6% compared with that (11.6%) before super-resolution processing. The proposed method offered valuable guidance for detecting small, dense agricultural objects in large-scale, complex natural environments.

关键词： UAV Object detection YOLOv7 Litchi fruit Real-ESRGAN

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3D convolution for multidate crop recognition from multitemporal image sequences

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INTERNATIONAL JOURNAL OF remote sensing 2022年第15-16期43卷 6056-6077页

作者： Rogozinski, Marcos Chamorro Martinez, Jorge Andres Feitosa, Raul Queiroz Pontifical Catholic Univ Rio de Janeiro Postgrad Programme Metrol Rio De Janeiro Brazil Pontifical Catholic Univ Rio de Janeiro BR-22451900 Rio De Janeiro Brazil

The increasing food demand is regarded as the main threat to nature today. In this scenario, remote Rensing is an essential technology to assess and monitor the extent and productivity of cultivated land. However, most work on crop mapping from remote sensing imagery so far focused on temperate areas, where a single crop prevails throughout the year. This paper explores different Convolutional Neural Network architectures for multi-temporal crop recognition in tropical areas, with highly complex crop dynamics and many crops per year. The proposed models are a U-Net with bidirectional ConvLSTM added to its skip connections, a U-Net with 3D convolutions, and a U-Net with 3D convolutions and Atrous Temporal Pyramid Pooling. The paper also assesses the gain of a post-processing step based on fully connected conditional random fields (CRF) as a second look at the spatial context to improve the networks' predictions. In an extensive experimental analysis conducted on two public multitemporal datasets of tropical areas with complex crop dynamics, all proposed models achieved better results than state-of-the-art methods based on recurrent networks in terms of Overall Accuracy and Average F1-Score. The 3D convolution with the Atrous Temporal Pyramid Pooling model stood up as the best performing architecture, with gains up to 4.3% over the baseline. The proposed CRF post-processing also proved beneficial for all tested architectures, bringing improvements of up to 2.7% in terms of average F1-score.

关键词： Convolution

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UAV Visual Localization Technology Based on Heterogenous remote sensing image Matching 5

UAV Visual Localization Technology Based on Heterogenous Rem...

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5th International Conference on Artificial Intelligence and pattern recognition, AIPR 2022

作者： Tang, Haoyang Shi, Jiakun Miao, Xin Wu, Ruichen Yang, Dongfang School of Automation Xi'an University of Posts and Telecommunications Xi'an710121 China School of Computer Science Xi'an University of Posts and Telecommunications Xi'an710121 China The Rocket Force University of Engineering Graduate School Xi'an710025 China

ISBN: (纸本)9781450396899

At present, the positioning function of intelligent UAVs mainly uses GPS technology, and GPS signals are susceptible to environmental and electromagnetic interference factors. In this paper, we combine remote sensing image processing with image matching algorithms to propose a GPS-independent visual localization technique for UAVs. First, the VGG16 network is used as the feature extraction backbone network, and the backbone network is designed and optimized for the characteristics of heterogenous remote sensing images. Secondly, a feature point screening and matching strategy is constructed, by which common feature points between heterogeneous remote sensing images can be screened and used for feature matching. Finally, the remote sensing image containing geographic location information and the UAV aerial image are fed into the network for feature extraction and matching, and the transformation matrix between the aligned images is calculated by the successfully matched feature points, and the transformation matrix is used to complete the mapping from the aerial image to the satellite image, and finally the geographic location information of each pixel can be read from the mapped image to complete the localization. © 2022 ACM.

关键词： Unmanned aerial vehicles (UAV)

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Leveraging Bioclimatic Context for Supervised and Self-supervised Land Cover Classification 45th

Leveraging Bioclimatic Context for Supervised and Self-super...

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45th Annual Conference of the German-Association-for-pattern-recognition (DAGM GCPR)

作者： Leonhardt, Johannes Drees, Lukas Gall, Juergen Roscher, Ribana Univ Bonn Bonn Germany Lamarr Inst Machine Learning & Artificial Intelli Dortmund Germany Forschungszentrum Julich Julich Germany

ISBN: (纸本)9783031546044;9783031546051

Modern neural networks achieve state-of-the-art results on land cover classification from satellite imagery, as is the case for almost all vision tasks. One of the main challenges in this context is dealing with geographic variability in both image and label distributions. To tackle this problem, we study the effectiveness of incorporating bioclimatic information into neural network training and prediction. Such auxiliary data can easily be extracted from freely available rasters at satellite images' georeferenced locations. We compare two methods of incorporation, learned embeddings and conditional batch normalization, to a bioclimate-agnostic baseline ResNet18. In our experiments on the EuroSAT and BigEarthNet datasets, we find that especially the use of conditional batch normalization improves the network's overall accuracy, generalizability, as well as training efficiency, in both a supervised and a self-supervised learning setup. Code and data are publicly available at https://***/NDQFF.

关键词： remote sensing Land Cover Classification Multi-Modal Learning Data Shift Conditional Batch Normalization

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EGF: An Improved Edge Detection Model for Low-Resolution images 2

EGF: An Improved Edge Detection Model for Low-Resolution Ima...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Deepak Raj, D.M. Shanmuganathan, Harinee Geetha, A. Keerthika, V. Alliance University Department of Computer Science and Engineering Banglore India

ISBN: (纸本)9798350308846

Edge detection can benefit many different industries and domains, including computer vision, machine learning, image analysis, remote sensing, thermal imaging, pattern recognition, and medical imaging. The technique of determining the borders between several objects or regions in an image is known as edge detection. The edges of an object in a picture serve as the object's limits and can reveal crucial details about the object's size, shape, and position. Since low-resolution images have low pixel densities or pixel values, which muddy the images, detecting edges in them is demanding work. This paper proposes a novel edge-detection approach called EGF (Extended Gaussian Filter) for low-resolution images. EGF utilizes the basic concept of Gaussian filter to find the edges of images. The objective function of EGF is developed to reduce the noise and pixel differentiation in images. The outcomes show that the suggested strategy outperforms the conventional edge detection technique. © 2023 IEEE.

关键词： edge detection gaussian filter image processing machine learning pre-processing

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BANet: A Balance Attention Network for Anchor-Free Ship Detection in SAR images

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2022年 60卷

作者： Hu, Qi Hu, Shaohai Liu, Shuaiqi Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China Beijing Jiaotong Univ Beijing Key Lab Adv Informat Sci & Network Techno Beijing 100044 Peoples R China Hebei Univ Machine Vis Engn Res Ctr Hebei Prov Coll Elect & Informat Engn Baoding 071002 Peoples R China Chinese Acad Sci Natl Lab Pattern Recognit NLPR Inst Automat Beijing 100190 Peoples R China

Recently, methods based on deep learning have been successfully applied to ship detection for synthetic aperture radar (SAR) images. However, most current ship detection networks rely too much on the anchor mechanism. These methods have low accuracy and poor generalization ability for multiscale ship detection. To solve the aforementioned problems, an anchor-free framework for multiscale ship detection in SAR images based on a balance attention network (BANet) is proposed. First, due to the diversity of scales and rotation angles of ships, deformable convolution is introduced to build a local attention module (LAM) to better obtain local information of ships and effectively enhance the robustness of the network. Second, a nonlocal attention module (NLAM) is designed to extract the nonlocal features of the SAR image, so as to balance the local features and nonlocal features acquired by the entire network. Finally, the feature pyramid network (FPN) is used to detect ships of different sizes at different scales. The detection results on three datasets demonstrate that the detection precision of our method is higher than that of all comparison methods, and this method achieves the most advanced performance.

关键词： Marine vehicles Radar polarimetry Object detection Feature extraction Convolution Head Clutter Anchor-free mechanism feature pyramid network (FPN) nonlocal attention ship detection synthetic aperture radar (SAR)

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SAGN: Sparse Adaptive Gated Graph Neural Network With Graph Regularization for Identifying Dual-View Brain Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第5期36卷 8085-8099页

作者： Xue, Wei He, Hong Wang, Yanbing Zhao, Ying Univ Shanghai Sci & Technol Sch Hlth Sci & Engn Shanghai 200093 Peoples R China

Due to the absence of a gold standard for threshold selection, brain networks constructed with inappropriate thresholds risk topological degradation or contain noise connections. Therefore, graph neural networks (GNNs) exhibit weak robustness and overfitting problems when identifying brain networks. Furthermore, existing studies have predominantly focused on strongly coupled connections, neglecting substantial evidence from other intricate systems that highlight the value of weakly coupled connections. Consequently, the potential of weakly coupled brain networks remains untapped. In this study, we pioneeringly construct weakly coupled brain networks and validate their values in emotion identification tasks. Subsequently, we propose a sparse adaptive gated GNN (SAGN) that can simultaneously perceive the valuable topology of dual-view (i.e., strongly coupled and weakly coupled) brain networks. The SAGN contains a sparse adaptive global receptive field. Moreover, SAGN employs a gated mechanism with feature enhancement and adaptive noise suppression capabilities. To address the lack of inductive bias and the large capacity of SAGN, a graph regularization term built with prior topology of dual-view brain networks is introduced to enhance generalization. Besides a public dataset (SEED), we also built a custom dataset (MuSer) with 60 subjects to evaluate weakly coupled brain networks' value and validate the SAGN's performance. Experiments demonstrate that brain physiological patterns associated with different emotional states are separable and rooted in weakly coupled brain networks. In addition, SAGN exhibits excellent generalization and robustness in identifying brain networks.

关键词： Physiology Graph neural networks pattern recognition Logic gates Emotion recognition Electroencephalography Task analysis Dual-view brain network electroencephalogram (EEG) emotion identification graph neural network (GNN) weakly coupled brain network

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