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5th Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2024

ISBN: (纸本)9798350374407

The proceedings contain 114 papers. The topics discussed include: research on intelligent English-Chinese translation proofreading system based on gated feedback re current neural networks;intelligent recognition of structures in earth and rock dam images based on MASK-RCNN;a pupil diameter measurement system based on image processing;image enhancement and deep learning in predicting the Gleason score of transcrectal ultrasound images of prostate cancer;research on video logo removal processing method based on MATLAB;analysis and evaluation of quality control throughout production of real scene 3D modeling based on oblique aerial photography;improving remote sensing image classification through stochastic bilevel optimization;cross-domain image translation algorithm based on self-cross auto-encoder;a fast image mosaic algorithm based on feature partition extraction;and video compression and action recognition in self-supervised learning.

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Real-time image recognition System based on Ultra-Low-Latency Spiking Neural Networks 2

Real-time Image Recognition System based on Ultra-Low-Latenc...

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2nd IEEE International conference on Signal, Information and Data processing, ICSIDP 2024

作者： Lan, Bin Zhang, Xi Dong, Heng Xu, Ming Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Innovation Equipment Research Institute Sichuan TianFu New Area Chengdu610299 China Beijing100081 China

ISBN: (纸本)9798331515669

remote sensing technology plays an important role in many tasks such as natural disaster detection, weather and climate monitoring and military defense. Currently, remote sensing image processing predominantly relies on Convolutional Neural Networks (CNNs) and Transformers, which require a huge amount of multiplication and addition operations, bringing pressure to the deployment of satellite platforms with strict power consumption and computing power constraints. Spiking neural network (SNN), where the signal is encoded as a spike train instead of analog values, significantly reduces energy consumption compared to traditional artificial neural network. This paper proposes an all-input parallel deployment approach that minimizes or eliminates interactions with off-chip memory, thereby accelerating inference speed and reducing power consumption. We deployed LeNet ultra-low latency SNN on the Xilinx VC709 Board. Experimental results demonstrate that the proposed accelerator achieves a processing speed of 1240 frames per second, an accuracy rate of 98.14%, and a power consumption of only 0.745 Watts, making it well-suited for the low power and real-time requirements of remote sensing tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on synthetic aperture radar recognition method based on deep learning

Research on synthetic aperture radar recognition method base...

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2024 International conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2024

作者： Hua, Zhendong Wang, Haifeng Wang, Haoxin Chinese Flight Test Establishmen Xi’an China

ISBN: (纸本)9781510685840

Target recognition in SAR images is a key issue in remote sensing image processing, which is widely used in many fields. Traditional recognition methods face many challenges due to the complexity of SAR images. In this paper, we propose an improved method based on GoogLeNet and improved YOLOV5 algorithm to cope with the limitations of MSTAR dataset. In this paper, GoogLeNet is introduced to improve the performance of target recognition in complex backgrounds, and its unique module improves the efficiency while reducing the parameters, which is especially suitable for handling small targets and complex textures in SAR images. Meanwhile, YOLOV5 is improved to enhance the detection accuracy and reduce the computational complexity. The effectiveness and superiority of the method is verified through experiments on the MSTAR dataset. © 2024 SPIE.

关键词： Synthetic aperture radar Target detection Convolution Target recognition Education and training Detection and tracking algorithms Feature extraction image processing Performance modeling Neck

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Aircraft Type recognition in remote sensing images using Mean Interval Kernel

Aircraft Type Recognition in Remote Sensing Images using Mea...

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International conference on image processing and Vision Engineering (IMPROVE)

作者： Sharma, Jaya Datla, Rajeshreddy Sravani, Yenduri Chalavadi, Vishnu Mohan, Krishna C. Indian Inst Technol Hyderabad Dept Comp Sci & Engn Hyderabad India Adv Data Proc Res Inst ADRIN Dept Space Secunderabad India

ISBN: (纸本)9789897585630

Structural characteristics representation and their fine variations are crucial for the recognition of different types of aircrafts in remote sensing images. Aircraft type classification across different sensor remote sensing images by spectral and spatial resolutions of objects in an image involves variable length spatial pattern identification. In our proposed approach, we explore dynamic kernels to deal with variable length spatial patterns of aircrafts in remote sensing images. A Gaussian mixture model (GMM), namely, structure model (SM) is trained over aircraft scenes to implicitly learn the local structures using the spatial scale-invariant feature transform (SIFT) features. The statistics of SM are used to design dynamic kernel, namely, mean interval kernel (MIK) to deal with the spatial changes globally in the identical scene and preserve the similarities in local spatial structures. The efficacy of the proposed method is demonstrated on the multi-type aircraft remote sensing images (MTARSI) benchmark dataset (20 distinct kinds of aircraft) using MIK. Also, we compare the performance of the proposed approach with other dynamic kernels, such as supervector kernel (SVK) and intermediate matching kernel (IMK).

关键词： remote sensing images Aircraft Type recognition Structural Information Model Scale-invariant Feature Transform (SIFT) Dynamic Kernels

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Forest canopy information extraction in Gannan Plateau based on UAV high-resolution images

Forest canopy information extraction in Gannan Plateau based...

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2023 International conference on image, Signal processing, and pattern recognition, ISPP 2023

作者： Wu, Yanduo Han, Hui Faculty of Geomatics Lanzhou Jiaotong University Lanzhou730070 China National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring Lanzhou730070 China Gansu Provincial Engineering Laboratory for National Geographic State Monitoring Lanzhou730070 China

ISBN: (纸本)9781510666351

In order to explore the feasibility of applying UAV remote sensing and object-oriented canopy extraction technology to forest areas with different canopy densities, 26 forest plots located in Gannan Plateau were selected and divided into two categories of coniferous forests with medium and high canopy densities. ESP tool was used to screen the optimal segmentation scale for multi-scale segmentation, and the feature space was optimized for object-oriented classification. The east-west and north-south crown amplitudes on quadrat scale were extracted in ArcGIS. The extracted crown amplitude was compared with the field measured data to evaluate the accuracy. The results were as follows: In UAV image extraction, the fitting values R2 and RMSE of east and west canopy width and measured values were 0.865 and 1.025m, respectively, and the fitting values R2 and RMSE of north and south canopy width were 0.867 and 1.235m, respectively. The R2 and RMSE of east and west canopy width with high canopy closure were 0.828 and 1.145m, respectively, and the R2 and RMSE of north and south canopy width were 0.787 and 1.523m, respectively. The extraction effect of the plots with medium canopy closure was better than that of the plots with high canopy closure, and both of the two canopy closure met the accuracy requirements. The results show that the automatic extraction method of canopy parameter information based on UAV remote sensing image is efficient and reliable, and has important practical significance in forest resources investigation. © 2023 SPIE.

关键词： remote sensing

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

2024 7th International Conference on Pattern Recognition and...

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

ISBN: (纸本)9798350350890

The proceedings contain 177 papers. The topics discussed include: short-term power load forecasting model based on EEMD-SE-ERCNN;dynamic low-rank adaptation based pruning algorithm for large language models;dual-input dual-output underwater image enhancement with branch interaction;object detection model of YOLOv8-CSD for UAV images;SE-ResNet: recognition of ECG anomalies based on convolutional neural network with fused attention;predicting accuracy for quantized neural networks: an attention-based approach with single-image;improved self-attention for Spodoptera frugiperda larval instar stages identification;an improved YOLOv8 algorithm for rotating object detection in unmanned aerial vehicle remote sensing images;and a comprehensive data augmentation method for modern cigarette packaging defect detection.

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CVIPPR 2024 - 2nd Asia conference on Computer Vision, image processing and pattern recognition

CVIPPR 2024 - 2nd Asia Conference on Computer Vision, Image ...

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2nd Asia conference on Computer Vision, image processing and pattern recognition, CVIPPR 2024

ISBN: (纸本)9798400716607

The proceedings contain 59 papers. The topics discussed include: 3D point clouds simplification based on low-dimensional contour feature extraction;3D human pose estimation using pressure images on a smart chair;combining doses from internal and external radiotherapies for cervical cancer with successive image registration;attention mechanism-based feature fusion generative network for infrared-visible person re-identification;a vision-based remote assistance method and its application in object transfer;research on model-free 6D object pose estimation based on vision 3D matching;active exploration of modality complementarity for multimodal sentiment analysis;self-attention-based multi-scale feature fusion network for road ponding segmentation;and low light image enhancement algorithm based on edge and color information.

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Self-Correcting Capsule Network on remote sensing Scene Classification

Self-Correcting Capsule Network on Remote Sensing Scene Clas...

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2023 Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2023

作者： Kan, Dongdonz Cao, Yue School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Cork Ireland

ISBN: (纸本)9781665457033

Most of the current capsule network methods have good classification effects mainly on simple content datasets such as MNIST and CIFAR10. However, for remote sensing scene images with complex objects, these methods cannot well resolve the main objects in the images, which in turn affects the recognition of scene categories. To address the above problems, this paper proposes a self-correcting capsule network with the ability to enhance the main object information contained in the features. First, the original features of the image are encapsulated into capsules. Then, the part extraction layer goes to learn the part information of the main targets in the scene and corrects the information of the primary capsule features, so as to parse the parts of different objects in the image. Since the traditional dynamic routing algorithms cannot be applied well in remote sensing scenes, in order to get the category information effectively, a class activation routing is proposed to act into the part capsules by learning the class activation weights in the original features, and using the routing to learn the spatial relationship between different parts to combine different part capsules to calculate the capsules rich in category information. The method in this paper effectively improves the classification performance of the network. © 2023 IEEE.

关键词： remote sensing

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SG-BEV: Satellite-Guided BEV Fusion for Cross-View Semantic Segmentation

SG-BEV: Satellite-Guided BEV Fusion for Cross-View Semantic ...

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IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Ye, Junyan Luo, Qiyan Yu, Jinhua Zhong, Huaping Zheng, Zhimeng He, Conghui Li, Weijia Sun Yat Sen Univ Guangzhou Guangdong Peoples R China Shanghai AI Lab Shanghai Peoples R China SenseTime Res Shanghai Peoples R China Zhejiang Univ Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9798350353006

This paper aims at achieving fine-grained building attribute segmentation in a cross-view scenario, i.e., using satellite and street-view image pairs. The main challenge lies in overcoming the significant perspective differences between street views and satellite views. In this work, we introduce SG-BEV, a novel approach for satellite-guided BEV fusion for cross-view semantic segmentation. To overcome the limitations of existing cross-view projection methods in capturing the complete building facade features, we innovatively incorporate Bird's Eye View (BEV) method to establish a spatially explicit mapping of street-view features. Moreover, we fully leverage the advantages of multiple perspectives by introducing a novel satellite-guided reprojection module, optimizing the uneven feature distribution issues associated with traditional BEV methods. Our method demonstrates significant improvements on four cross-view datasets collected from multiple cities, including New York, San Francisco, and Boston. On average across these datasets, our method achieves an increase in mIOU by 10.13% and 5.21% compared with the state-of-the-art satellite-based and cross-view methods. The code and datasets of this work will be released at https: //***/yejy53/SG-BEV.

关键词： BEV Fusion Cross-View remote sensing Semantic Segmentation

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Triple Robustness Augmentation Local Features for multi-source image registration

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND remote sensing 2023年第1期199卷 1-14页

作者： Wang, Changwei Xu, Lele Xu, Rongtao Xu, Shibiao Meng, Weiliang Wang, Ruisheng Zhang, Xiaopeng Chinese Acad Sci Inst Automat MAIS Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Technol & Engn Ctr Space Utilizat Key Lab Space Utilizat Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Univ Calgary Dept Geomat Engn Calgary AB Canada

With the proliferation of a wide variety of sensors, accurate multi-source image registration is crucial for many remote sensing image processing tasks. However, the registration of multi-source images faces the challenges of rotations, scales, and domain transformations caused by significant differences in shooting time, viewing angle, and sensor imaging modes. To cope with this problem, we propose a deep learning-based registration method named TRFeat, which aims to comprehensively improve the rotation, scale, and cross-domain robustness of local features. First, we introduce a special circular sampling convolutional layer to replace the standard square convolutional layer, in order to enhance the rotational robustness of local features. Second, we design a scale pyramid backbone network architecture to improve the robustness of the network to scale transformations. Third, we promote the use of hypercolumn domain alignment loss to extract cross-domain robust local descriptors for images from different sources. In addition, we develop a novel keypoint detection training framework based on iterative refinement supervision to obtain repeatable and reliable keypoints localization in multi-source images. Finally, we conduct thorough experiments on five multi-source datasets. Extensive experimental results validate that our TRFeat outperforms other state-of-the-art hand-crafted (e.g. RIFT) and deep learning-based methods (e.g. ASLFeat). Specifically, our TRFeat achieves an MMA@3 of 76.08% on the HPatches dataset and an RMSE of 3.38 on the Xiang dataset. The code is available at https://***/vignywang/TRFeat.

关键词： Multi-source image registration image matching Domain robust local features

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