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

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Innovative Cumulative Intensity Projection Registration for Vertical Shift Calibration in HoloSAR

Innovative Cumulative Intensity Projection Registration for ...

引用

IEEE International conference on signal and image processing (ICSIP)

作者： Fengzhuo Huang Dong Feng Xiaotao Huang College of Electronic Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

Synthetic aperture radar (SAR) holography (HoloSAR) is a vital remote sensing technique widely employed in extracting three-dimensional (3-D) scattering information of anisotropic targets. In the case of airborne SAR, factors such as flight trajectory errors can introduce phase errors during the imaging process, thereby degrading the quality of HoloSAR images. This paper modifies the phase gradient autofocus (PGA) method based on cumulative intensity projection registration to improve imaging quality. The proposed method begins with an initial phase calibration using the traditional PGA method. Subsequently, the 3-D imaging results, after preliminary compensation, are projected onto two-dimensional (2-D) planes via cumulative intensity projection. The residual linear phase error is estimated based on the vertical shifts observed in the projection results of different sub-apertures. This error is then corrected by adjusting the imaging results, thereby resolving the issue of vertical shift between sub-apertures. Compared to other mainstream methods, the proposed method offers simplicity in principle and high computational efficiency. Experiments on simulation and GOTCHA data validate the effectiveness of the proposed method.

关键词： Two-dimensional displays Scattering Radar imaging Holography Radar signal processing Calibration Trajectory

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Small Object Detection on the Water Surface Based on Radar and Camera Fusion

Small Object Detection on the Water Surface Based on Radar a...

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

作者： Qiancheng Wei Xiaoping Jiang Ying Liu Qiya Su Muyao Yu University of Chinese Academy of Sciences China Beijing Institute of Remote Sensing Equipment China

With the growing applications of water operations, water surface object detection tasks are facing new challenges. In this paper, we focus on improving the performance of water surface small object detection. Due to the limitations of single-sensor in water environments, we propose RCFNet, a novel small object detection method based on radar-vision fusion. RCFNet fuses features captured by radar and camera in multiple stages to generate more effective target feature representations for small object detection on water surfaces. In particular, we propose a multi-frame radar feature fusion module and an image-guided radar feature enhancement module to enhance the radar features. This method fully utilizes radar and camera data information, improving the performance of small object detection on water surfaces. RCFNet was evaluated on the publicly released water surface floating dataset Flow, achieving an Average Precision (AP50) of 0.9316, which is a state-of-the-art result.

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Oriented Object Detection in remote sensing image Based on YOLOV5

Oriented Object Detection in Remote Sensing Image Based on Y...

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International conference on signal processing Systems, ICSPS

作者： Dan Pan Lifang Wu Faculty of Information Technology Beijing University of Technology Beijing China

In recent years, remote sensing satellites have developed rapidly and accumulated massive high-resolution image data. Using deep learning to solve remote sensing image object detection has become an important research direction. However, due to shooting from a bird's eye view, the object has an oblique Angle. Because of the perspective problem, remote sensing images have boundary problem when the object is near horizontal. When there are a large number of densely tilted object boxes, using horizontal box detection method will cause box occlusion thus missing or mislabeling. Therefore, it is a challenging task to solve remote sensing image object detection. In this paper, we propose the network structure which based on YOLO-V5 and combined with CSL method. Moreover, the loss function is improved to deal with the overlapping problem of tilted object boxes effectively. As revealed by extensive experiments, the average accuracy of this method on DOTA data sets shows a competitive advantage. At the same time, the size of the network is only 16.3m, which is obviously better than the comparison method.

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L-MFFN: Lightweight Multiscale Feature Fusion Network with Limited Samples for HSI Classification

L-MFFN: Lightweight Multiscale Feature Fusion Network with L...

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IEEE International conference on signal and image processing (ICSIP)

作者： Aamir Ali Caihong Mu Yafeng Wang Yi Liu School of Artificial Intelligence Xidian University Xi'an China School of Electronic Engineering Xidian University Xi'an China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

Hyperspectral image (HSI) classification is valuable in remote sensing due to its rich spectral and spatial information. In the last decade, deep learning methods, especially Convolutional Neural Networks (CNNs), have revolutionized HSI classification by extracting intangible semantic features and maintaining the spatial structure during feature extraction. However, the efficacy of these techniques can be constrained by the limited availability of labeled samples in HSI data. To address the issue of small-sample HSI classification, a Lightweight Multiscale Feature Fusion Network (L-MFFN) is introduced. The Multiscale Feature Extraction Module (MFEM) and the enhanced Spectral-Spatial Attention Module (SSAM) are designed and combined in L-MFFN, optimizing the use of deep and shallow features. This integration improves the extraction and fusion of multiscale spectral-spatial features, enhancing classification performance. The proposed model demonstrates state-of-the-art performance across two HSI datasets and stands out in situations with limited labeled samples, highlighting its capability to effectively tackle the challenge of small-sample HSI classification.

关键词： Deep learning Accuracy Semantics Feature extraction Convolutional neural networks Hyperspectral imaging image classification

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Aspects of signal processing for Multistatic Terahertz Imaging Systems 46

Aspects of Signal Processing for Multistatic Terahertz Imagi...

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46th International conference on Infrared, Millimeter and Terahertz Waves (IRMMW-THz)

作者： Hussung, Raphael Keil, Andreas Kahl, Matthias Souliman, Aya Weisenstein, Christian Bolivar, Peter Haring Friederich, Fabian Fraunhofer Inst Ind Math ITWM Kaiserslautern Germany Becker Photon GmbH Porta Westfalica Germany Univ Siegen High Frequency & Quantum Elect Siegen Germany

ISBN: (纸本)9781728194240

Applications of multistatic sensor arrays, which operate in the terahertz-region, are increasingly gaining importance for non-destructive testing purposes. Continuous enhancements in processing capabilities of CPUs and GPUs allow to generate volumetric images with superior speeds compared to alternative raster-scan systems. However, this requires efficient reconstruction-and correction algorithms. In addition to the system design, the choice of signal processing methods has a decisive influence on the resolution, image quality and measurement duration. In this contribution, we discuss some aspects of this signal processing along with specific examples.

关键词： image quality image resolution signal processing algorithms Imaging System analysis and design signal resolution image reconstruction

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Iterative Outlier Removal Using Close-and-Compatible Neighbors for Robust Feature Correspondence 6

Iterative Outlier Removal Using Close-and-Compatible Neighbo...

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6th International conference on signal and image processing, ICSIP 2021

作者： Shen, Liang Zhang, Pengyu Xin, Qin Jin, Tian Huang, Xiaotao National University of Defense Technology College of Electronic Science Changsha China

ISBN: (纸本)9780738133737

Establishing reliable feature correspondence for the image matching is a fundamental problem in computer vision and remote sensing. Currently, many state-of-the-art correspondence selection methods are based on the neighborhood consensus, while the following two problems limit their performance: 1. It is difficult to select the consensus neighbors while preserving spatial smoothness. 2. The location-based consensus constraint is loose and may lead to low precision. In this paper, we propose a simple iterative outlier removal to address these two issues. It searches close-and-compatible neighbors to measure the neighborhood consensus based on the local affine frame, and works by rejecting outliers sequentially and iteratively. The proposed approach is much more robust than state-of-the-art methods when dealing with outliers and large-scale geometric transformation. Numerous experimental results demonstrate the superiority of our method. © 2021 IEEE.

关键词： remote sensing

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Indoor Airflow Imaging Using Physics-Informed Schlieren Tomography

Indoor Airflow Imaging Using Physics-Informed Schlieren Tomo...

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

作者： Arjun Teh Wael H. Ali Joshua Rapp Hassan Mansour Carnegie Mellon University Pittsburgh PA USA Mitsubishi Electric Research Laboratories Cambridge MA USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

remote temperature sensing of volumetric flows has a variety of applications, such as promoting thermal comfort, heat dissipation, or data center cooling. The emergence of background-oriented schlieren (BOS) imaging in recent years has enabled transparent flow visualization at minor costs. In this paper, we develop a framework for non-invasive volumetric indoor airflow estimation from a single viewpoint using BOS measurements and physics-informed reconstruction. Our framework utilizes a light projector that projects a pattern onto a target back wall and a camera that observes small distortions in the light pattern due to the change in the refractive index of the air as a result of the temperature variation. While the single-view BOS tomography problem is severely ill-posed, we regularize the reconstruction using a physics-informed neural network (PINN) that ensures that the reconstructed airflow is consistent with the coupled Boussinesq approximation of the incompressible Navier– Stokes and the heat transfer equations.

关键词： Temperature measurement Temperature sensors Temperature distribution Volume measurement Neural networks Refractive index Tomography Sensors Speech processing image reconstruction

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An Efficient Convolution Network with Adaptive receptive field feature for High-Resolution remote sensing Change Detection 4

An Efficient Convolution Network with Adaptive receptive fie...

引用

4th International conference on Video, signal and image processing, VSIP 2022

作者： Xue, Dongling Bai, Lin Yao, Cheng Lin, Xiangyuan School of Electronics and Control Engineering Chang'An University Xi'an China

ISBN: (纸本)9781450397810

Change detection is a challenging problem in remote sensing applications. In recent years, many Convolutional Neural Network (CNN)-based change detection methods have been proposed due to the rapid development of deep learning techniques. First, the Neural network receptive field single problem prevails. Besides, most existing methods enhance the network performance by adding an attention mechanism between layers, but this cannot obtain fine-grained features. In addition, the methods with transformer lack a priori information and can waste computational resources. Therefore, to solve these problems, this paper integrates attention with convolution and proposes an Efficient selective receptive field (ESRF) module to adjust the receptive field adaptively. Based on this, we design an efficient change detection network, ESRFnet, to achieve the fusion of multi-scale features. In addition, to focus more on the change region, we also introduce a spatial attention mechanism (SAM) in the network. Finally, we conduct extensive experiments on LEVIR-CD and SYSU datasets, and the results show that the proposed method can handle change regions at different scales well and effectively suppresses the interference of factors such as illumination, as well as has advantages in terms of execution efficiency and accuracy compared with several state-of-the-art methods currently available. © 2022 ACM.

关键词： Change detection

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Building Change Detection Using Cross-Temporal Feature Interaction Network

Building Change Detection Using Cross-Temporal Feature Inter...

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

作者： Yuchao Feng Jiawei Jiang Honghui Xu Jianwei Zheng Zhejiang University of Technology

Building change detection of remote sensing images is in full flourishing accompanied by the prosperity of convolutional neural networks. For spatial-temporal context modeling, existing solutions disregard the inter-image interactions, albeit their positive contribution to the acquisition of differences. To fill the gap, we propose a cross-temporal feature interaction network to effectively derive the change representations. Specifically, we propose a linearized cross-attention, which motivates each counterpart to glimpse the representation of another image while preserving its own features. In addition, to circumvent the misalignment caused by step-down sampling in the backbone, we introduce multi-level feature alignment using learnable affine transformation and stepwise aggregation. Based on a naive backbone (ResNet18) without sophisticated structures, our model outperforms other state-of-the-art methods on three datasets in terms of both efficiency and effectiveness.

关键词： Buildings Neural networks signal processing Feature extraction Sensors Convolutional neural networks Task analysis

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Online Graph-Based Change Point Detection in Multiband image Sequences 28

Online Graph-Based Change Point Detection in Multiband Image...

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28th European signal processing conference (EUSIPCO)

作者： Borsoi, R. A. Richard, C. Ferrari, A. Chen, J. Bermudez, J. C. M. Univ Cote dAzur OCA CNRS Nice France Univ Fed Santa Catarina Florianopolis SC Brazil Northwestern Polytech Univ Xian Peoples R China Univ Catolica Pelotas Pelotas RS Brazil

ISBN: (纸本)9789082797053

The automatic detection of changes or anomalies between multispectral and hyperspectral images collected at different time instants is an active and challenging research topic. To effectively perform change-point detection in multitemporal images, it is important to devise techniques that are computationally efficient for processing large datasets, and that do not require knowledge about the nature of the changes. In this paper, we introduce a novel online framework for detecting changes in multitemporal remote sensing images. Acting on neighboring spectra as adjacent vertices in a graph, this algorithm focuses on anomalies concurrently activating groups of vertices corresponding to compact, well-connected and spectrally homogeneous image regions. It fully benefits from recent advances in graph signal processing to exploit the characteristics of the data that lie on irregular supports. Moreover, the graph is estimated directly from the images using superpixel decomposition algorithms. The learning algorithm is scalable in the sense that it is efficient and spatially distributed. Experiments illustrate the detection and localization performance of the method.

关键词： Hyperspectral images change detection graphs multitemporal superpixels

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