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检索条件"任意字段=Conference on Image Processing and Pattern Recognition in Remote Sensing II"

共 707 条记录，以下是51-60 订阅

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Study on multi-annual and simultaneous satellite-ground remote sensing retrieval of water transparency (Secchi disk depth) in Poyang lake

Study on multi-annual and simultaneous satellite-ground remo...

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2024 International conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Zheng, Pu Xu, Xin-Fa Xu, Xiao-Hua Huang, Ping Zhou, Xin-Wen Jiang xi Academy of Water Science and Engineering Nanchang330029 China Jiangxi Provincial Technology Innovation Center for Ecological Water Engineering in Poyang Lake Basin 330029 China

ISBN: (纸本)9781510680425

Poyang Lake is the largest freshwater lake in China. It is located in the middle and lower reaches of the Yangtze River in the north-central Jiangxi Province. As the largest Lake which is connecting the Yangtze River, the water ecological security of Poyang Lake affects the entire Yangtze River basin. Water transparency(SDD) is an important indicator of water quality, so it is of great significance to carry out routine monitoring of SDD using remote sensing methods. This study takes Poyang Lake as the experimental area, and uses the SDD as the inversion index to characterize water quality. Based on observation data from 2015 to 2020 and Landsat series images, the inversion of SDD model suitable for summer Poyang Lake was constructed by analyzing and comparing the correlation coefficients between different wave bands and the SDD under the condition of standardized modeling phase and "star-ground" collaborative matching. After verification by four sets of independent data, the mean R2 is 0.8621, the mean RSME is 15.37cm, and the mean MAPE is 23.6%. It is believed that the method used in this study can be well applied to the inversion of SDD in the Poyang Lake area, and also provides a new reference method for monitoring the transparency of inland water bodies under long-term observation conditions. © 2024 SPIE.

关键词： Lakes

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UHV transmission line monitoring based on satellite remote sensing

UHV transmission line monitoring based on satellite remote s...

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

作者： Zhang, Yunfeng Zhang, Peng Shen, Jianliang Wei, Wenli Yue, Lingping Zhang, Jun State Grid Huzhou Power Supply Company Zhejiang Huzhou313000 China Center of Mass Entrepreneurship Innovation State Grid Zhejiang Electric Power Co. Ltd Zhejiang Hangzhou310000 China NARI-TECH Nanjing Control Systems Ltd Jiangsu Nanjing210000 China

ISBN: (纸本)9781510666351

UHV transmission lines have problems such as high impedance and low loss. This paper mainly introduces the monitoring method of UHV transmission and distribution line based on satellite remote sensing technology, aiming at avoiding or reducing property losses caused by line problems and maintaining people's normal life. In this paper, the experimental system construction and quantitative analysis methods are mainly used, and the results of two kinds of line detection are compared by comparison. The experimental data shows that in different types of fault lines, the test time of fault is within 0.1s. © 2023 SPIE.

关键词： Electric lines

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A Sidelobe-Aware Semi-Deformable Convolutional Ship Detection Network for Synthetic Aperture Radar imagery 7th

A Sidelobe-Aware Semi-Deformable Convolutional Ship Detectio...

引用

7th Chinese conference on pattern recognition and Computer Vision

作者： Luo, Hao Lin, Xianming Xiamen Univ Minist Educ Key Lab Multimedia Trusted Percept & Efficient Co Xiamen 361005 Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen 361005 Peoples R China Nanjing Marine Radar Inst Nanjing 211153 Peoples R China

ISBN: (纸本)9789819784929;9789819784936

This paper focus on SAR ship detection, which has been widely used in tasks such as marine traffic, fisheries management, battlefield posture assessment and military target reconnaissance. One popular solution is to utilise deep learning algorithms in conjunction with SAR image object detection. However, due to the unique imaging characteristics of SAR, existing solutions usually do not distinguish well between ships and interference targets in complex backgrounds. In this paper, we address this problem by proposing a sidelobe-aware semi-deformable convolution that takes full advantage of the combination of both standard and deformable convolution to learn high-quality ship features without significantly increasing the computational complexity. Specifically, it makes the feature extraction location more closely fit to the ship shape, strengthens the extraction capability of the region of the ship target itself and the sidelobe features, while reducing the extraction of background information. The channel attention mechanism is then proposed to enhance the ship local detail information extraction. Experiments on two widely used datasets show that the proposed method outperforms the state-of-the-art methods, which is effective and efficient to improve SAR ship detection.

关键词： Deformable Convolution Synthetic Aperture Radar (SAR) imagery Ship Detection remote sensing image processing

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Attention-based U-Net for building extraction from remote sensing images

Attention-based U-Net for building extraction from remote se...

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2022 International conference on image, Signal processing, and pattern recognition, ISPP 2022

作者： Zhang, Qiong College of Geomatics and Geoinformation Guilin university of technology Guangxi Guilin China

ISBN: (纸本)9781510654846

To extract ground objects from remote sensing images based on deep learning method is one of the current research hotspots, and building information has attracted much attention as an important artificial feature. Convolutional neural networks have shown great potential for building extraction tasks. In view of the current research status, this paper proposes a lightweight improved network ECAU-Net that combines semantic segmentation network U-Net and efficient channel attention mechanism, and applies it to automatic extraction of buildings from high-resolution remote sensing images. Experiments show that the extraction method proposed in this paper shows good extraction results on the Massachusetts building data set, and the accuracy, recall, precision, and F1 score are better than the original U-Net. Therefore, this method does have certain advantages. © 2022 SPIE.

关键词： remote sensing

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AutoLC: Search Lightweight and Top-Performing Architecture for remote sensing image Land-Cover Classification 26

AutoLC: Search Lightweight and Top-Performing Architecture f...

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26th International conference on pattern recognition / 8th International Workshop on image Mining - Theory and Applications (IMTA)

作者： Zheng, Chenyu Wang, Junjue Ma, Ailong Zhong, Yanfei Wuhan Univ Sch Comp Sci Wuhan Peoples R China Wuhan Univ LIESMARS Wuhan Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Land-cover classification has long been a hot and difficult challenge in remote sensing community. With massive High-resolution remote sensing (HRS) images available, manually and automatically designed Convolutional Neural Networks (CNNs) have already shown their great latent capacity on HRS land-cover classification in recent years. Especially, the former can achieve better performance while the latter is able to generate lightweight architecture. Unfortunately, they both have shortcomings. On the one hand, because manual CNNs are almost proposed for natural image processing, it becomes very redundant and inefficient to process HRS images. On the other hand, nascent Neural Architecture Search (NAS) techniques for dense prediction tasks are mainly based on encoder-decoder architecture, and just focus on the automatic design of the encoder, which makes it still difficult to recover the refined mapping when confronting complicated HRS scenes. To overcome their defects and tackle the HRS land-cover classification problems better, we propose AutoLC which combines the advantages of two methods. First, we devise a hierarchical search space and gain the lightweight encoder underlying gradient-based search strategy. Second, we meticulously design a lightweight but top-performing decoder that is adaptive to the searched encoder of itself. Finally, experimental results on the LoveDA land-cover dataset demonstrate that our AutoLC method outperforms the state-of-art manual and automatic methods with much less computational consumption.

关键词： Training Gradient methods image processing Computer architecture Manuals Search problems Decoding

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Intelligent recognition of Point Source Target image Control Points with Simulation Datasets 1

引用

6th Asian conference on pattern recognition (ACPR)

作者： Li, Kai Yang, Weiming Zhang, Lei Zhang, Zhenchao Acad Syst Engn Acad Mil Sci Beijing Peoples R China Strateg Support Force Informat Engn Univ Sch Geospatial Informat Zhengzhou Peoples R China

ISBN: (数字)9783031024443

ISBN: (纸本)9783031024443;9783031024436

The point source target (PST) can provide high object and image positioning accuracy and is expected to play an important role in the precise geometric processing of optical remote sensing sensors in the future. This paper proposes a method for intelligently recognizing PST image control points (ICPs) from satellite imagery, which can improve the intelligent level of geometric processing of optical remote sensing sensors. Two deep convolutional neural networks (DCNNs), Faster R-CNN and CenterNet are selected to complete the recognition task. Due to the lack of training data, a large number of simulated samples are generated considering the PST image characteristics. The simulated and real PST ICPs are then used to test the trained DCNNs. The two DCNNs complete the recognition task on the simulated dataset successfully. The Recall and Precision values of the two DCNNs are close to 100%. The performance of the two DCNNs on real PST ICPs is worse than that on the simulated data, but the recognition task is also well completed when the quality of PST ICPs is good. The Recall values of both models are above 95%, and the Precision values are close to 100%. Experiment results also show that the performance of CenterNet is better than Faster R-CNN and the image quality has a great impact on the recognition performance.

关键词： Point source target (PST) image control point (ICP) Deep convolutional neural networks (DCNNs) Faster R-CNN CenterNet

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Revealing the Unseen: A Single-Stage Attention Based Occluded Object Detection Model in remote sensing imagery 10th

Revealing the Unseen: A Single-Stage Attention Based Occlude...

引用

10th Biennial International conference on pattern recognition and Machine Intelligence (PReMI)

作者： Saini, Nandini Chattopadhyay, Chiranjoy Das, Debasis IIT Jodhpur Comp Sci & Engn Jodhpur 342037 India FLAME Univ Sch Comp & Data Sci Pune 412115 India

ISBN: (纸本)9783031451690;9783031451706

Object detection based on deep learning has achieved promising results on traditional datasets, but detecting objects in remote sensing imagery with diverse occlusions remains challenging. This is due to the fact that natural occlusions are common in real-world images, and there is a lack of adequate datasets and neglect of latent information that can be useful for identification. Our research endeavors to tackle this challenge by presenting an innovative single-stage image-adaptive YOLO-transformer framework that leverages attention-based mechanisms to enable the model to concentrate on important regions and extract more distinctive features. To optimize the model's accuracy while maintaining its lightweight and suitability for real-time applications, we employed a depthwise convolution and SiLU activation function in lieu of the standard convolution. These modifications allow our framework to attain high levels of precision. Our approach is evaluated on synthetically generated occluded datasets from publicly available NWPU-VHR10 and demonstrated adaptive processing of images in both normal and three types of environmental occlusions (foggy, rainy, and cloudy). The experimental results are highly promising, as our approach achieved an inference speed of 6 ms with 11.9 GFLOPs on the NVIDIA Tesla T4 while maintaining effectiveness in all three occlusion types.

关键词： Object Detection Environmental Occlusion remote sensing images CNN-Transformer Attention Mechanism

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Improved Prototypical Networks for remote sensing Scene Classification

Improved Prototypical Networks for Remote Sensing Scene Clas...

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

作者： Zhou, Yujin Zhang, Xiang Li, Jie Wang, Guoqing Xie, Yizhuang Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing100081 China The Shanghai Aerospace Electronic Technology Institute Shanghai201109 China

ISBN: (纸本)9781510666351

In practical applications, remote sensing (RS) scene classification faces data shift problems, including novel class and data discrepancy problems. Due to these problems, it is difficult to obtain representative and discriminative features. Therefore, we propose improved prototypical networks (IPN) based on few-shot learning to solve data shift problems in RS scene classification. First, a novel and effective scheme is proposed, which is to introduce Vision Transformer (ViT) pre-trained on a large-scale dataset as the feature extractor of the prototypical networks. Based on the meta-task training framework, IPN can adapt well to RS scene classification tasks and obtain representative features. In addition, a novel loss function called self-distillation-based prototype loss is designed to obtain discriminative features by introducing inter-sample self-distillation and inter-layer self-distillation methods. Extensive experiments are conducted on several public RS scene datasets. Compared with the existing methods, the proposed method achieves an improvement of 4.18%-21.42%. Results demonstrate that the proposed method can effectively solve the data shift problems in RS scene classification. © 2023 SPIE.

关键词： Distillation

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Learning-based Classification Approach for Coded Aperture Compressive Spectral image

Learning-based Classification Approach for Coded Aperture Co...

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

作者： Wang, Peng Ma, Xu Zhao, Qile Key Laboratory of Photoelectronic Imaging Technology System of Ministry of Education of China School of Optics and Photonics Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781510666351

Spectral images include rich spatio-spectral information of target scene, which can accurately identify and distinguish the features of ground objects. Therefore, spectral image classification is widely used in remote sensing. However, traditional spectral imaging techniques need to scan the region of interest along the spatial dimension or spectral dimension, which takes a long acquisition time and increases the burden of data transmission and storage. To overcome these shortcomings, coded aperture snapshot spectral imaging (CASSI) system based on compressive sensing theory appeared. In this paper, we build a testbed of dual-disperser CASSI (DD-CASSI) system, which can reconstruct the three-dimensional (3D) spectral image datacube of target object from a few two-dimensional compressive measurements. Then, a 3D convolutional neural network is applied to accomplish the spectral image classification based on the reconstructed datacube. Different classification methods are compared based on the experimental data. It shows that the proposed compressive spectral image classification method achieves pretty close results compared to the classification methods based on the original datacube. But, the proposed method is beneficial to improve the acquisition efficiency of the spectral image data. © 2023 SPIE.

关键词： image classification

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Quantum-Inspired Spectral-Spatial Pyramid Network for Hyperspectral image Classification

Quantum-Inspired Spectral-Spatial Pyramid Network for Hypers...

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

作者： Zhang, Jie Zhang, Yongshan Zhou, Yicong Univ Macau Dept Comp & Informat Sci Macau Peoples R China China Univ Geosci Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9798350301298

Hyperspectral image (HSI) classification aims at assigning a unique label for every pixel to identify categories of different land covers. Existing deep learning models for HSIs are usually performed in a traditional learning paradigm. Being emerging machines, quantum computers are limited in the noisy intermediate-scale quantum (NISQ) era. The quantum theory offers a new paradigm for designing deep learning models. Motivated by the quantum circuit (QC) model, we propose a quantum-inspired spectral-spatial network (QSSN) for HSI feature extraction. The proposed QSSN consists of a phase-prediction module (PPM) and a measurement-like fusion module (MFM) inspired from quantum theory to dynamically fuse spectral and spatial information. Specifically, QSSN uses a quantum representation to represent an HSI cuboid and extracts joint spectral-spatial features using MFM. An HSI cuboid and its phases predicted by PPM are used in the quantum representation. Using QSSN as the building block, we further propose an end-to-end quantum-inspired spectral-spatial pyramid network (QSSPN) for HSI feature extraction and classification. In this pyramid framework, QSSPN progressively learns feature representations by cascading QSSN blocks and performs classification with a softmax classifier. It is the first attempt to introduce quantum theory in HSI processing model design. Substantial experiments are conducted on three HSI datasets to verify the superiority of the proposed QSSPN framework over the state-of-the-art methods.

关键词： Photogrammetry and remote sensing

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