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

共 3582 条记录，以下是261-270 订阅

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Accelerating a Two-Stage Object Detector for High Quality In-Orbit remote sensing 28

Accelerating a Two-Stage Object Detector for High Quality In...

引用

conference on image and signal processing for remote sensing XXVIII

作者： Kang, Jaemin Kwon, Jinse Kim, Hyungshin Chungnam Natl Univ Daejeon South Korea

ISBN: (纸本)9781510655386;9781510655379

Object detection from remote sensing images has been performed on the ground. Recently, on-board object detection has been studied only to show its feasibility with single-stage detectors. However, highly accurate models such as two stage detectors are compute intensive so that they are too slow to run on power-constrained on-board computers. In this paper, we propose a speed-up method for two-stage detectors. Two-stage detectors extract features and ROIs(Region of Interest) in the first stage and then classify them at the second stage. This structure gives high accuracy but induces large inference latency. In remote sensing images from satellites, object size is small relative to the whole image. Based on this characteristic, we propose to exclude features related to the large objects in the first stage. To verify our concept, we have selected various R-CNN models as two-stage object detectors. We have implemented our methods on two NVIDIA Jetson boards. We have achieved 1.8x speed up in inference latency with 5% accuracy drop with the small object dataset.

关键词： On-board Object detection Feature Pyramid Network Accelerate Two-stage Detector RoIAlign Small object DOTA Dataset

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Research on Snapshot GISC Hyperspectral image Analysis Technology Based on Mean Filtering Algorithm 2

Research on Snapshot GISC Hyperspectral Image Analysis Techn...

引用

2nd International conference on Mechatronics, IoT and Industrial Informatics, ICMIII 2024

作者： Liu, Zhuo Li, Xinyang Li, Meixuan Changchun College of Electronic Technology Changchun China Jilin Engineering Normal University Changchun China

ISBN: (纸本)9798350386639

With the improvement of the quality of life, people pay more and more attention to the quality of diet, and fruits and vegetables are an important part of a healthy diet, as well as essential nutrients for people to maintain physical health. Therefore, this paper proposes a non-destructive detection method for fruit and vegetable quality-GISC hyperspectral imaging technology. The principle of GISC hyperspectral imaging technology is introduced, and the mean filtering method is used to denoise the characteristic signal data periodically and at high frequencies. The mean and variance of the denoised signal data are calculated to form a feature vector. Finally, the effectiveness of the scheme is verified by using information entropy for image quality evaluation. © 2024 IEEE.

关键词： Wiener filtering

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image Fusion Technique Using Singular Value Decomposition Based Undecimated Subband Decomposition

Image Fusion Technique Using Singular Value Decomposition Ba...

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2023 IEEE International conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Evers, Julia Fraunhofer Iis Fraunhofer Institute for Integrated Circuits Iis Erlangen Germany

ISBN: (纸本)9798350327816

image fusion is an important area of research. In remote sensing, the usage of the same sensor in different working modes, or different image sensors, can provide complementary information. Therefore, it is needed to fuse outputs from multiple sensors (or the same sensor in different working modes) to improve the performance of the remote images, which are very useful for many image processing tasks. Singular Value Decomposition Undecimated Subband Decomposition (SVD-USBD) of images with its Multi-Resolution schemes and based on it novel image fusion technique have been presented. The performance of this algorithm is compared with that of often used image fusion technique using Multi-Resolution form of Singular Value Decomposition (MR-SVD). It is observed that image fusion by SVD-USBD provides almost similar fusion quality and that is why it can be used in combinations with other methods to develop hybrid schemes for multi-focus image fusion or fusion of different types of images as infrared or termal imaging. © 2023 IEEE.

关键词： image sub-band decomposition multi-resolution singular value decomposition Multi-sensor image fusion SVD

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Multispectral image Fusion based on Super Pixel Segmentation 48

Multispectral Image Fusion based on Super Pixel Segmentation

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48th IEEE International conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Ofir, Nati Kingston University London United Kingdom

ISBN: (纸本)9781728163277

Multispectral image fusion is a computer vision process that is essential to remote sensing. For applications such as dehazing and object detection, there is a need to offer solutions that can perform in real-time on any type of scene. Unfortunately, current state-of-the-art approaches do not meet these criteria as they need to be trained on domain-specific data and have high computational complexity. This paper focuses on the task of fusing color (RGB) and near-infrared (NIR) images as this the typical RGBT sensors, as in multispectral cameras for detection, fusion, and dehazing. Indeed, the NIR channel has the ability to capture details not visible in RGB and see beyond haze, fog, and clouds. To combine this information, a novel approach based on superpixel segmentation is designed so that multispectral image fusion is performed according to the specific local content of the images to be fused. There-fore, the proposed method produces a fusion that contains the most relevant content of each spectrum. The experiments re-ported in this manuscript show that the novel approach better preserves details than alternative fusion methods. © 2023 IEEE.

关键词： image Fusion Multispectral images Near-Infrared Superpixel Segmentation

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

7th International Conference on Vision, Image and Signal Pro...

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

ISBN: (纸本)9781839539848

The proceedings contain 47 papers. The topics discussed include: a modified UNet network bridged with multiscale context fusion for photovoltaic panel image segmentation;a new method for radar emitter individual identification based on VMD and multi-image feature combination;research on axle bearing fault detection method based on multilayer feature fusion under sparse training samples;diffusion posterior sampling for remote sensing image fusion;mutual reconstruction-based linear discriminant hashing for image retrieval;joint face super-resolution and deblurring using multi-task feature fusion network;AmpNorm: an effective style normalization method for single domain generalization;construction of marine target detection dataset and target detection methods;and deep learning sea surface object detection method based on infrared image.

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Airplane symmetry-based Convolutional Neural Network for airplane detection in remote sensing image

Airplane symmetry-based Convolutional Neural Network for air...

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IET International Radar conference 2023, IRC 2023

作者： Cheng, Bei Wang, Bo Liu, Zao Wang, Qingwang Shen, Tao Gu, Yebo Kunming University of Science and Technology No. 72 7 Jingming South Road Chenggong District Yunnan Kunming City China School of Information Engineering and Automation Kunming University of Science and Technology Kunming China

ISBN: (纸本)9781839539954

Airplane detection in remote sensing image has attracted increasing attention in recent years owing to the successful applications of civil and military. Usually, the structure of the airplane is symmetrical in order to ensure the flight safety, and this inherent character would benefit target detection from complex background. This paper proposes a novel airplane detection algorithm based on symmetry-based region proposal (SB-RP) method and convolutional neural network (CNN). Firstly, pre-trained convolutional neural network is used to extract the high-level features from the training data set that include positive samples (airplane) and negatives (background). Then a symmetry-based region proposal (SB-RP) method is developed to extract the regions of interest from the complex remote sensing image. At last, the convolutional neural network trained by high-level features is exploited to detect the airplane from these regions of interest. The major contribution of SB-RP CNN method is that the interference of background clutter is greatly decrease for the regions of interest are reduced significantly for the background isn't symmetrical. The extensive experiments are induced and the experimental results show that the SB-RP CNN method could greatly improve the performance of airplane detection in remote sensing image. © The Institution of Engineering & Technology 2023.

关键词： Convolutional neural networks

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Investigating the influence of hyperspectral data compression on spectral unmixing 28

Investigating the influence of hyperspectral data compressio...

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conference on image and signal processing for remote sensing XXVIII

作者： Kuester, Jannick Anastasiadis, Johannes Middelmann, Wolfgang Heizmann, Michael Fraunhofer Ctr Machine Learning Fraunhofer IOSB Ettlingen Germany Karlsruhe Inst Technol KIT Inst Ind Informat Technol IIIT Hertzstr 16 D-76187 Karlsruhe Germany

ISBN: (纸本)9781510655386;9781510655379

This work addresses the problem of hyperspectral data compression and the evaluation of the reconstruction quality for different compression rates. Data compression is intended to transmit the enormous amount of data created by hyperspectral sensors efficiently. The information loss due to the compression process is evaluated by the complex task of spectral unmixing. We propose an improved 1D-Convolutional Autoencoder architecture with different compression rates for lossy hyperspectral data compression. Furthermore, we evaluate the reconstruction by applying metrics such as SNR and SA and compare them to the spectral unmixing results.

关键词： hyperspectral data compression spectral unmixing feature extraction dimensionality reduction Autoencoder hyperspectral imaging spectral analysis remote sensing convolutional neural networks

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Weakly supervised ship detection in remote sensing images 28

Weakly supervised ship detection in remote sensing images

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conference on image and signal processing for remote sensing XXVIII

作者： Guo, Chen Tan, Zhiwen An, Meng Jiang, Zhiguo Zhang, Haopeng Beihang Univ Dept Aerosp Informat Engn Sch Astronaut Beijing 102206 Peoples R China CAST Inst Spacecraft Syst Engn Beijing 100094 Peoples R China Beijing Key Lab Digital Media Beijing 102206 Peoples R China Minist Educ Key Lab Spacecraft Design Optimizat & Dynam Simu Beijing 102206 Peoples R China

ISBN: (纸本)9781510655386;9781510655379

Ship detection in remote sensing images is important for maritime surveillance. With the rapid development of earth observation technology, high-resolution imaging satellites can provide more observational information. In the face of massive remote sensing data, object-level annotation requires a lot of time and manpower. Weakly supervised object detection is trained using only image-level annotations, thus reducing the requirement for object-level annotations. However, there are still some problems in the detection of weakly supervised ships in remote sensing images, because of the complex, dense distribution and diverse scale characteristics of the ship environment. We propose a weakly supervised object detection method that combines Transformer with weakly supervised learning for ship detection in remote sensing images. First, Proposal Clustering Learning (PCL) for weakly supervised object detection is used as the baseline to detect ships, and the network is continuously refined for better detection performance. Second, the prior location and size information is added to the features of the proposal through the transformer module. These additional information can be used as an important basis for judging whether the proposal is optimal, thereby improving the detection performance. To evaluate the effectiveness of our method, extensive experiments are conducted on a complex dataset of large-scene remote sensing ships. Experimental results show that our method achieves better detection performance than other methods.

关键词： Weak supervision ship detection proposal clustering transformer remote sensing

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remote sensing image Ship Detection Based on Improved YOLOv3 7

Remote Sensing Image Ship Detection Based on Improved YOLOv3

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7th International conference on Intelligent Computing and signal processing, ICSP 2022

作者： Da, Yifan Gao, Xing Li, Mengfei Beijing University of Chemical Technology School of Information and Communication Technology Beijing China Yanshan University Liren College Qinhuangdao China Beijing University of Posts and Telecommunications Computer Science and Technology Beijing China

ISBN: (纸本)9781665478571

With the improvement of remote sensing image resolution, remote sensing target detection has become a research hotspot, and it plays an important role in military reconnaissance, disaster rescue, urban traffic management, and other scenarios. However, remote sensing target detection still has problems such as a large amount of data and specific differences in shooting by different sensors. In order to meet these challenges and improve the speed and accuracy of remote sensing target detection, this paper proposes a target detection algorithm based on an attention mechanism. Based on the YOLOv3 network, the SE-YOLOv3 network is designed using an attention mechanism to increase expressiveness, focus on essential features, and suppress unnecessary features. The experimental results show that: on the DOTA data set, compared with the YOLOv3 network, adding the squeeze and excitation (SE) model to the bottleneck has the best effect and can better improve the performance of the model. Experiments show that the proposed SE-YOLOv3 network is more efficient and accurate than YOLOv3, so it is more suitable for target detection tasks in complex remote sensing scenes. © 2022 IEEE.

关键词： Object detection

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Semi-supervised classification method for PolSAR Based on vision transformer (ViT) 4

Semi-supervised classification method for PolSAR Based on vi...

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4th International conference on signal image processing and Communication, ICSIPC 2024

作者： Li, Xiangshen Yu, Jie Han, Lihua College of Resource Environment and Tourism Capital Normal University Beijing100048 China Beijing Laboratory of Water Resources Security Capital Normal University Beijing100048 China State Key Laboratory Incubation Base of Urban Environmental Process and Digital Simulation Capital Normal University Beijing100048 China Land Satellite Remote Sensing Application Center of MNR Beijing100048 China

ISBN: (纸本)9781510682467

Semi-supervised classification methods generate pseudo-labels from unlabeled data, where pseudo-labels' precisio n is vital for successful classification outcomes. Addressing the challenge of inaccuracies in pseudo-label catego ries during training, this paper introduces a semi-supervised classification strategy for PolSAR images, leveragin g the Vision Transformer (ViT) technology. In this study, the Simple Linear Iterative Clustering (SLIC) method was applied to create superpixel blocks with distinct boundaries and organized structures, facilitating the generat ion of pseudo-labels with a correctness rate of 85.9%. This paper introduces the Vision Transformer (ViT) net work, capitalizing on its multi-head attention mechanism. This approach enhances global image information extr action while simultaneously addressing local details for pseudo-label training. Specifically, on the Flevoland data set, an increase in pseudo-label category accuracy of 7.09% and 5.47% was achieved. For the Wuhan Tongshu n River dataset, improvements of 2.44% and 3.41% in pseudo-label category accuracy were recorded, thereby e levating the precision of PolSAR semi-supervised classification. © 2024 SPIE.

关键词： image enhancement

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