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

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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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A Dual-Stream Network with Non-Stationary Characteristics-Enhanced for SST image Prediction

A Dual-Stream Network with Non-Stationary Characteristics-En...

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

作者： Min Ye Jiahao Du Ning Song Xinyue Liang Bohan Li Jie Nie Fac. of Information Science and Engineering Ocean University of China Qingdao China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Sea surface temperature (SST) prediction is crucial for understanding global climate and marine ecosystems, and its anomalies can lead to extreme weather events. SST exhibits complex non-stationary over natural spatio-temporal processes. However, most of the existing deep learning methods for SST prediction only extract non-stationary features through the simple state transitions of classic CNNs or RNNs, which are too simplistic to capture higher-order non-stationary trends in complex SST sequences. Therefore, we propose a DSNet_SST network, aiming to enhance the extraction of non-stationary information from the spatio-temporal SST evolution. It incorporates two parallel modules: one for capturing high-order temporal non-stationarity based on stacked memory in memory (MIM) blocks and the other one for extracting spatial correlation non-stationarity by multiscale difference operation. A third module adaptively integrates these features to improve the accuracy and stability of SST prediction. The experimental results, relying on the OISST data obtained from both remote sensing satellites and in situ platforms, demonstrate the advantages of the proposed DSNet_SST over baseline methods in terms of SST prediction accuracy.

关键词： Temperature sensors Accuracy signal processing Feature extraction Spatiotemporal phenomena Data mining Speech processing Thermal stability Surface treatment Meteorology

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HyperDiff: Masked Diffusion Model with High-efficient Transformer for Hyperspectral image Cross-Scene Classification

HyperDiff: Masked Diffusion Model with High-efficient Transf...

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

作者： Pei Zhang Dong Wang Chanyue Wu Jing Yang Lei Kang Zongwen Bai Ying Li Qiang Shen School of Computer Science Northwestern Polytechnical University Xi’an China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan’an University Yan’an China School of Automation and Software Engineering Shanxi University Taiyuan China Qingdao Topscomm Communication Co. Ltd Qingdao China Department of Computer Science Aberystwyth University UK

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Hyperspectral image (HSI) cross-scene classification is a challenging task in remote sensing, particularly when real-time processing of Target Domain (TD) HSI is required, and data cannot be reused for training. While deep learning methods have shown promising results, the generalization ability of HSI representations remains limited, mainly due to class label imbalance. This paper introduces a dual-stage learning framework based on transfer learning to enhance classification accuracy in the TD. The framework includes a self-supervised learning stage and a supervised fine-tuning stage. The self-supervised stage focuses on learning robust representations by leveraging inherent structures within HSI data, while the fine-tuning stage uses training labels to extract semantic information. A masked diffusion model predicts masked tokens from unmasked ones, capturing both high-level structures and fine details in HSI data. An efficient spatiospectral Transformer, which removes self-attention from the decoder, is proposed to enhance the self-supervised process. This design allows mask tokens to obtain information from visible tokens without interacting with each other, reducing sequence length and computational costs. By decoding each mask token conditionally independently, only a subset of masked tokens is processed. Extensive experiments on two public HSI datasets demonstrate that the proposed method outperforms state-of-the-art techniques.

关键词： Training Transfer learning Self-supervised learning Transformers Diffusion models Real-time systems Decoding Computational efficiency Speech processing Hyperspectral imaging

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A remote sensing satellite image compression method based on conditional generative adversarial network 29

A remote sensing satellite image compression method based on...

引用

conference on image and signal processing for remote sensing xxix

作者： Cheng, Kan Zou, Yafang Zhao, Yuting Jin, Hao Li, Chengchao China Acad Space Technol CAST Inst Remote Sensing Satellite IRSS Beijing Peoples R China

ISBN: (纸本)9781510666955;9781510666962

With the sharp increase of images on satellites, the efficiency of satellite-to-ground data transmission has become a bottleneck that restricts the effectiveness of remote sensing satellites. To alleviate the pressure of data transmission, we conducted in-depth research on remote sensing satellite image compression technology. Traditional methods and existing deep learning methods are prone to losing detailed information when dealing with remote sensing satellite images with complex textures and rich details. Given that generative adversarial networks (GAN) have advantages in texture generation and detail restoration, we propose a remote sensing satellite image compression method based on conditional GAN. Our main innovations are: 1) proposing a compression framework for remote sensing satellite images based on conditional GAN, which improves the reconstruction quality through adversarial learning between the conditional generator and discriminator. 2) introducing the Laplacian of Gaussian loss to train the model, which emphasizes details such as edges, contours, and textures in remote sensing images. 3) introducing multiple perceptual metrics to calculate the similarity between images, which comprehensively evaluates the quality of reconstructed images. Experimental results show that our method has better visual effects and objective evaluation indicators than traditional methods and existing deep learning methods at the same compression ratio.

关键词： remote sensing satellite image compression deep learning conditional GAN Laplacian of Gaussian perceptual metric

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remote sensing image Completion Using a Diffusion-Based Propagation Algorithm 29

Remote Sensing Image Completion Using a Diffusion-Based Prop...

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conference on image and signal processing for remote sensing xxix

作者： Rolland, Iain Selvakumaran, Sivasakthy Marinoni, Andrea Univ Cambridge Engn Dept Cambridge England UiT Arctic Univ Norway Tromso Norway

ISBN: (纸本)9781510666955;9781510666962

In the field of remote sensing it is common to have image data which can be considered in some way to be incomplete. This may relate to missing information caused by sensor failures, cloud cover or partially overlapping data acquisitions. In each of these cases it is of interest to consider how best this data can be completed. Whereas previous work has employed techniques such as low-rank tensor completion to tackle this problem, we present a graph-based propagation algorithm which diffuses entries around the incomplete image tensors. We show this approach is robust in even extreme circumstances for which large regions of image data are missing and compare the quality of our completions against the state of the art. In addition to improved performance as measured by reduced errors versus ground truth in experiments we also provide a comparison of our method's efficiency against benchmark methods and show that the approach is scalable as well as robust.

关键词： image reconstruction missing data tensor completion graph theory graph propagation remote sensing

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Super-resolution algorithm of lunar panchromatic image based on random degradation model 29

Super-resolution algorithm of lunar panchromatic image based...

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conference on image and signal processing for remote sensing xxix

作者： Lan, Lin Lu, Chunling DFH satellite CO LTD Beijing 100094 Peoples R China

ISBN: (纸本)9781510666955;9781510666962

In recent years, lunar exploration has become a hot spot in the world again. High-resolution lunar surface images are of great significance to lunar research, and at the same time are crucial to the safe landing of lunar probes. Due to the limitation of the orbital height and hardware, the resolution of the lunar remote sensing images is restricted, so it is particularly important to carry out super-resolution reconstruction of the lunar surface image. At present, most image super-resolution algorithms use a single fixed degradation model, such as using only bicubic interpolation algorithm for down-sampling, or adding specified blur, noise, etc. However, the real image degradation model is extremely complex and difficult to express with specific formulas, so this paper introduces a more complex degradation model when super-resolving the lunar image, and simulates the complex degradation process in reality by adding more randomness. Secondly, this paper uses a deep learning network that combines a CNN network with residual structure and a Transformer architecture for image super-resolution reconstruction, where the Transformer architecture is used for deep feature extraction. The proposed method is experimented on Chang'e-2 7- meter resolution lunar surface remote sensing images, which verifies the effectiveness of the super-resolution algorithm proposed in this paper and outperforms the current popular methods in terms of visual effects and commonly used evaluation metrics. This work aims to improve the image clarity of the lunar surface in order to enhance the environment-awareness capability of the lunar probe and further improve its autonomous capability on the lunar surface.

关键词： remote sensing image super-resolution lunar image CNN Transformer degradation model

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Onboard deep learning for efficient small satellite reflectance retrievals and downlink 29

Onboard deep learning for efficient small satellite reflecta...

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conference on image and signal processing for remote sensing xxix

作者： Garimella, Sarvesh MyRadar 111 W Jefferson St Orlando FL 32801 USA

ISBN: (纸本)9781510666955;9781510666962

The rapid pace of commercial space launches has drastically increased opportunities for novel small satellite missions. Though small satellites benefit from reduced launch costs, they are constrained by their Size, Weight, and Power (SWaP) limitations. The satellites in MyRadar's HORIS (Hyperspectral Orbital remote Imaging Spectrometer) constellation have SWaP constraints of a 1U CubeSat form factor (10 cm3 and < 1.33 kg), which place significant limits on mission design and duty cycle. In particular, downlinking large amounts of raw data, like that generated by the HORIS narrow band hyperspectral sensor, can be prohibitively bandwidth and power intensive. This study explores using deep learning inference to optimize onboard data processing and to mitigate the impacts of data volume on downlinking hyperspectral information from LEO (Low Earth Orbit). In particular, deep learning inference on the visible wavelength context imagery is used to constrain the aerosol model effects on reflectance retrieval calculations to convert at-sensor radiance measurements into surface (or cloud top) reflectance estimates. Also, a transfer learning approach utilizing an adversarially trained autoencoder to compress data from other satellites is used for HORIS data compression to reduce the required power and bandwidth for alerting use cases.

关键词： remote sensing satellite CubeSat artificial intelligence alerting wildfires compression radiative transfer

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Improved Homographic Adaptation for Keypoint Generation in Cross-spectral Registration of Thermal and Optical imagery 29

Improved Homographic Adaptation for Keypoint Generation in C...

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conference on image and signal processing for remote sensing xxix

作者： Yagmur, Ismail Can Ates, Hasan F. Ozyegin Univ Dept Comp Sci Istanbul Turkiye Ozyegin Univ Fac Engn Istanbul Turkiye

ISBN: (纸本)9781510666955;9781510666962

Autonomous navigation is an important area of research for aerial vehicles. Visual odometry and simultaneous localization and mapping algorithms are critical for the three-dimensional understanding of the environment. For that purpose, consistent multi-spectral maps of the environment should be generated. Existing pixel-based image registration methods are accurate but too slow to operate in real-time. Recently deep learning is used to develop feature-based data-driven methods for generating interest points and associated descriptors for registering multi-spectral image pairs. These methods are fast and perform better than existing methods for optical images. However, the results are less convincing for thermal image registration. In this work, we propose an improved multi-spectral homographic adaptation technique to generate highly repeatable ground truth interest points that are invariant across viewpoint changes in both spectra. These interest points are used to train the MultiPoint image registration network. Simulation results show that our improved model outperforms existing techniques for feature-based image alignment of optical and thermal images.

关键词： Robot vision Multi-spectral image Registration Homographic Adaptation

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A Theoretical Framework for Unsupervised Land Cover Change Detection in Dense Satellite image Time Series 29

A Theoretical Framework for Unsupervised Land Cover Change D...

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conference on image and signal processing for remote sensing xxix

作者： Listiani, Indira Aprilia Bovolo, Francesca Zanetti, Massimo Fdn Bruno Kessler Digital Soc Via Sommarive 18 I-38123 Trento Italy Univ Trento Dept Informat Engn & Comp Sci I-38123 Trento Italy

ISBN: (纸本)9781510666955;9781510666962

This paper addresses the complex task of detecting and characterizing changes in dense Satellite image Time Series (SITS). Although Change Vector Analysis (CVA) is widely used for Change Detection (CD), it has limitations due to missing prior information on changes, such as: optimal spectral channels and change timing. Time series data can help overcome these limitations, but working with them is challenging. To address these challenges, the paper introduces a novel framework called Time Series Change Vector Analysis (TSCVA), which builds upon the principles of CVA. In TSCVA, the paper redefines CVA in the time series feature space and introduces new definitions for change in time series magnitude and direction. This allows for a detailed analysis of change components in the time and spectrum domain within the SITS, enabling unsupervised CD. We utilize the expectation-maximization algorithm to estimate parameters of statistical distributions for change and nochange classes. The effectiveness of the proposed TSCVA method is evaluated using Sentinel-2 time series data. The results, both quantitative and qualitative, confirm the robustness of this approach in effectively addressing the CD problem in dense SITS.

关键词： remote sensing Multi-Temporal Multi-spectral Unsupervised Change Detection (CD) Satellite image Time Series (SITS) Change Vector Analysis (CVA)

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A GPU-accelerated Algorithm for Copy Move Detection in large-scale satellite images 29

A GPU-accelerated Algorithm for Copy Move Detection in large...

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conference on image and signal processing for remote sensing xxix

作者： Barni, Mauro Costanzo, Andrea Dimitri, Giovanna Maria Tondi, Benedetta Univ Siena DIISM Via Roma 56 I-53100 Siena Italy

ISBN: (纸本)9781510666955;9781510666962

With the diffusion of advanced image editing software, image manipulation is becoming an impelling aspect also for satellite images. In a copy-move (CM) forgery, part of the image is copied and pasted elsewhere into the same image. In the satellite domain, CM can be performed with the intent of propagating misleading information on the geography and morphology of the landscapes pictured in the images. The best algorithms for CM detection rely on a multi-step procedure involving extraction of image descriptors (keypoints), keypoint matching and finally clustering, for the localization of the forged area. The large size of many satellite images and their richness of details, often prevent the adoption of off-the-shelf tools developed for multimedia images. Due to the large number of keypoints typically present in satellite images, in fact, the computational complexity and memory requirements for SIFT keypoints extraction, matching, clustering and forgery localisation is prohibitive. In this paper, we propose a CM detection algorithm that can successfully process very high resolution satellite images, where off-the-shelf alternatives are crashing due to system memory exhaustion. The proposed algorithm is based on three main strategies powered by GPU acceleration: i) multi-threaded tile-based SIFT keypoints extraction, ii) optimised batch-based descriptors matching, iii) clustering and localisation of manipulated pixels exploiting tensors instead of a sliding window approach. Experiments carried out on images belonging to the ESA WorldView-2 European Cities dataset and on a set of hand-made copy-move forgeries with resolution above 1 Gigapixel, show the good performance of the proposed algorithm in terms of processing time and memory consumption.

关键词： Copy Move detection Satellite image forgery detection remote sensing image Forgery

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