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检索条件"任意字段=Conference on Image and Signal Processing for Remote Sensing IX"

共 3615 条记录，以下是571-580 订阅

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Road extraction from remote sensing images based on residual dense blocks and mixed attention mechanism

Road extraction from remote sensing images based on residual...

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6th International conference on Intelligent Computing and signal processing (ICSP)

作者： Xiaohui He Yinjie Jiang The School of Geo-Science & Technology Zhengzhou University Zhengzhou China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

In order to solve the problem of road edge blur in remote sensing image road extraction, a road edge feature information enhancement algorithm based on residual dense blocks and mixed attention mechanism is designed in this paper. At present, semantic segmentation algorithm based on deep learning has been able to extract relatively complete and accurate road network, but the segmentation of road edge is not accurate enough, and the extraction of road edge details is often incomplete. To this end, the ADRU-net road extraction network is designed in this paper, and experimental verification is conducted on data set Zimbabwe_Data_Roads.

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HYPERSPECTRAL image CHANGE DETECTION BASED ON INTRINSIC image DECOMPOSITION FEATURE EXTRACTION 27

HYPERSPECTRAL IMAGE CHANGE DETECTION BASED ON INTRINSIC IMAG...

引用

conference on image and signal processing for remote sensing XXVII

作者： Du, Kecheng Liu, Sicong Tongji Univ Coll Surveying & Geoinformat Shanghai Peoples R China

ISBN: (数字)9781510645691

ISBN: (纸本)9781510645691;9781510645684

Hyperspectral images (HSIs) provides abundant spectral information through hundreds of bands with continuous spectral information that can be used in land cover fine change detection (CD). HSIs make it possible for hyperspectral CD performance with higher discrimination on changes but provides a challenge to the conventional CD techniques due to its high dimensionality and dense spectral representation. In this paper, we implemented intrinsic image decomposition (IID) model to decompose the hyperspectral temporal difference image into two parts: real change and pseudo change information. In particular, the spectral reflecting component is selected as a kind of pure spectral feature used to enhance the CD performance in multitemporal HSIs. Experimental results illustrate the effectiveness of IID features extraction in addressing a supervised CD task.

关键词： Hyperspectral image change detection intrinsic image decomposition multiple changes

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Phase-coded Waveform Optimization of Spaceborne Sliding Spotlight SAR with Low Sidelobe

Phase-coded Waveform Optimization of Spaceborne Sliding Spot...

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signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Ru Jia Yang Luo Ning Fu Hongyang An Zhongyu Li Junjie Wu Jianyu Yang Ping Du Yueyue Zhang Mingji Dong Xin Miao Jiaxuan Liu School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Shanghai Key Laboratory of Satellite Network Shanghai Satellite Network Research Institute Co. Ltd. Shanghai China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Spaceborne sliding spotlight synthetic aperture radar (3S-SAR) has achieved long-duration and high-resolution observations of targets, playing a vital role in remote sensing. However, strong scattering targets in SAR imagery exhibit high two-dimensional sidelobes, which would degrade image quality and obscure critical targets. To address this issue, we propose a phase-coded waveform optimization of 3S-SAR with low sidelobe. Firstly, we establish that the SAR imagery sidelobes are equivalent to the autocorrelation sidelobes. Next, we formulate the waveform optimization as a minimization problem aimed at reducing the discrete integrated autocorrelation sidelobe. Finally, we apply the majorization-minimization method to solve this optimization. Experimental results demonstrate the effectiveness and superiority of the proposed method.

关键词： Spaceborne radar Scattering Optimization methods Minimization Radar polarimetry Radar signal processing Autocorrelation Iterative methods Synthetic aperture radar remote sensing

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Adaptive Fine-Tuning and Feature Enhancement of SAM for remote sensing Instance Segmentation

Adaptive Fine-Tuning and Feature Enhancement of SAM for Remo...

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Symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Zhixin Zhao Yangguang Liu Lingwu Meng Haochen Zhang Mingyang Zhu Liang Xiao School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

remote sensing image instance segmentation aims to accurately separate independent objects and automatically identify land attributes. Recent advancements in large models have propelled self-supervised learning, especially with the segment anything model (SAM). Although SAM can generate instance segmentation masks without additional training, its application to remote sensing images still faces challenges, such as a severe reliance on manual prior prompts, inaccuracies in detail segmentation, and insufficient generalization capability. Therefore, to enhance the generalization capability of SAM in the field of remote sensing images, we propose a SAM-based fine-tuning model called AFFE-SAM. This model combines adaptive low-rank (AdaLoRA) and variable adapter (MiMi-Adapter) fine-tuning methods. Additionally, it improves mask quality in the decoder by employing adaptive multi-scale feature enhancement. We conducted experiments on two datasets, obtaining mAP scores of 66.5 for the NWPU VHR-10 dataset and 67.1 for the SSDD dataset. The evaluation results validate the effectiveness of our method.

关键词： Instance segmentation Training Adaptation models Self-supervised learning Propulsion Decoding Sensors Object recognition Informatics remote sensing

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Hyperspectral image processing with Machine Learning and Mathematical Models 7th

Hyperspectral Image Processing with Machine Learning and Ma...

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7th International conference on Soft Computing and signal processing, ICSCSP 2024

作者： Augustin, Anju Kiliroor, Cinu C. Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam India

ISBN: (纸本)9789819799251

Hyperspectral imaging is a fast-growing imaging technique in many fields, like remote sensing, fruit analysis, clinical images, etc. The spectral image consists of two parts: spectral data and spatial data. The processing of spectral images includes the processing of the spatial part as well as the spectral part. This paper analyzes various methods and algorithms that are used for spectral image processing in existing systems and proposes a diagram for hyperspectral image processing. There are five important steps for processing the spectral image and analyzing the region selection and dimensionality reduction methods in detail. Comparing the performance of region selection and dimensionality reduction techniques, the Rembg algorithm and Principal Component Analysis (PCA), respectively, show very good results. A well processed image has better performance in the classification and regression steps, and it reduces the computational time and cost. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Principal component analysis

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Discrete atomic compression of satellite images: a comprehensive efficiency research 27

Discrete atomic compression of satellite images: a comprehen...

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conference on image and signal processing for remote sensing XXVII

作者： Makarichev, Viktor O. Lukin, Vladimir V. Brysina, Iryna, V Vozel, Benoit Chehdi, Kacem Natl Aerosp Univ UA-61070 Kharkov Ukraine Univ Rennes 1 IETR UMR CNRS 6164 F-22305 Lannion France

ISBN: (数字)9781510645691

ISBN: (纸本)9781510645691;9781510645684

In this paper, a problem of resource expenses needed for storage, processing and transferring a large number of high resolution digital remote sensing images is considered. Application of discrete atomic compression (DAC), which is an algorithm based on atomic wavelets, to solving this problem is studied. Dependence of efficiency of the DAC algorithm on its parameters, in particular, quality loss settings, a structure of discrete atomic transform, which is a core of DAC, and a method of quantized wavelet coefficients' encoding, is investigated. Binary arithmetic coding and a combination of Huffman codes with run-length encoding are used to provide lossless compression of quantized atomic wavelet coefficients. Comparison of these methods is presented. A set of digital images of the European Space Agency is employed as test data. In addition, we discuss promising ways to improve the DAC algorithm.

关键词： image compression atomic function atomic wavelet discrete atomic transform lossy compression

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Semi-Supervised remote sensing image Change Detection Using Mean Teacher Model for Constructing Pseudo-Labels

Semi-Supervised Remote Sensing Image Change Detection Using ...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zan Mao Xinyu Tong Ze Luo Computer Network Information Center Chinese Academy of Sciences Beijing China University of the Chinese Academy of Sciences Beijing China

In recent years, deep learning has ushered in great developments in remote sensing image change detection. Practically, it is labor-intensive and time-consuming to label images for co-registration. In this paper, we propose a semi-supervised training that uses the mean teacher model to construct pseudo-labels to increase the generalizability of the model trained with a handful of data supervision. More specifically, we first supervise the training of a student change detection model with a few labeled data, while migrating the model parameters from each training round to a teacher model with the same structure through Exponential Moving Average (EMA). The weakly augmented output produced by the teacher model was preferable to the strongly augmented prediction produced by the student to penalize the latter. We explore the paradigm of Strong-to-Weak Consistency in change detection. Experiments on the LEVIR-CD and WHU-CD have been extensively conducted and state-of-the-art performance has been achieved 1 .

关键词： Training Deep learning Predictive models signal processing Semisupervised learning Data models Acoustics

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Multi-exposure remote sensing image HDR synthesis technology based on spaceborne DSP 2021

Multi-exposure remote sensing image HDR synthesis technology...

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3rd International conference on Information Technology and Computer Communications, ITCC 2021

作者： Ma, Jun Qian, Jinzhuo School of Software Henan University Kaifeng China

ISBN: (纸本)9781450389884

Since traditional high dynamic range (HDR) imaging technology cannot be directly applied to satellites, in the field of remote sensing imaging, a technology for directly generating HDR remote sensing images on the satellite has been explored for a long time. Based on the above requirements, this paper proposes a multi-exposure remote sensing image fusion algorithm based on spaceborne DSP TMS320C6678 (C6678) to realize this technology. Firstly, three low dynamic range images of overexposure, normal exposure and underexposure are generated by the method of fast continuous shooting by the satellite camera, and the acquired multi-exposure remote sensing images are transmitted to the satellite DSP C6678 for processing. Then the multi-exposure remote sensing image fusion algorithm built in DSP will be used to quickly generate high-quality and high-signal-to-noise HDR images. This technology greatly improves the image information acquisition capabilities of remote sensing satellites and fills the gap in the application of spaceborne HDR synthesis technology. © 2021 ACM.

关键词： remote sensing

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remote sensing aircraft image target detection based on Yolov5-EBA

Remote sensing aircraft image target detection based on Yolo...

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7th International conference on Vision, image and signal processing (ICVISP 2023)

作者： Y. Chen Y. Jia X. Zhang School of Computer Science and Technology Southwest University of Science and Technology Mianyang Sichuan 621000 People's Republic of China Troops Xi'an 710000 People's Republic of China

The problems of overlooked detection and false detection issues are pervasive in the aircraft target detection process, and a remote sensing image target improvement model YOLOv5s-EBA based on YOLOv5s is proposed. first, the algorithm introduces an attention network to improve the model main network to enhance the feature selection of aircraft with different resolutions; then, the path aggregation network (PAN) in the original YOLOv5s network is replaced by the bi-directional feature pyramid network to implementing the multi-scale feature blending of aircraft targets simply and quickly; Finally, we substitute CIoU with α-IoU, and the prediction accuracy of the bounding box is improved by introducing the parameter α and by adjusting the parameter α. The experimental results indicate on the homemade dataset Airplane-GDD, contrast with the original YOLOv5 algorithm, the average precision of the YOLOv5s-EBA algorithm is increased by 2.2 percentage points, and the accuracy and recall witness a boost of 2.3 and 2.7 percentage points, respectively, and there is also a noteworthy enhancement for the problem of incorrect and overlooked detections in the detection process.

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An Efficient YOLOv5n Based Object Detection Heterogeneous SoC Implementation for remote sensing images

An Efficient YOLOv5n Based Object Detection Heterogeneous So...

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sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International conference on

作者： Heming Liu Bowen Yao Rong Xu Yu Peng Liansheng Liu School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Shanghai Institute of Satellite Engineering Shanghai China School of Electronics and Information Engineering Harbin Institute of Technology Yantai China

ISBN: (数字)9798331529192

ISBN: (纸本)9798331529208

Convolutional neural networks (CNNs) are widely used in the field of remote sensing image object detection due to their high accuracy. However, the large number of parameters and high computational complexity of CNNs make it challenging to deploy them in real-time on embedded devices with limited computational and storage resources. This significantly restricts their practical application. To address this challenge, lightweight YOLOv5n model is chosen to realize object detection. The model is further optimized by activation function modification and parameter quantization to be more hardware-friendly. In addition, deploying a Deep Learning processing Unit (DPU) on the Zynq heterogeneous SoC by hardware-software co-design to significantly accelerate the YOLOv5n based remote sensing object detection. Experimental results show that the optimized YOLOv5n model reaches a lossless accuracy at 61.4% on the DIOR dataset when implemented on an embedded platform based on Zynq. The experimental platform achieves an image throughput of 232.1 FPS with a power consumption of 19.4W. Performance per watt (FPS/W) is 9.0× and 1.8× higher than that of i7-12700H CPU and RTX 3070Ti GPU respectively.

关键词： Performance evaluation Accuracy Quantization (signal) Computational modeling Object detection Throughput Real-time systems Energy efficiency Convolutional neural networks remote sensing

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