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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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Advanced Scale-aware Attention for Enhanced Tiny Object Detection in remote sensing

Advanced Scale-aware Attention for Enhanced Tiny Object Dete...

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

作者： Tingting Qiao He Chen Bingqian Chai Wenchao Liu Guijie Qi Yizhuang Xie National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Recent advancements in remote sensing object detection have progressed, but distinguishing objects from complex backgrounds remains difficult, especially for tiny objects. To address this, we introduce the Advanced Scale-Aware Attention (ASA) module, which retains key channel information while minimizing computational cost. This module reorganizes channels into the batch dimension and splits them into multiple sub-features. The ASA module contains three branches: the first encodes weights based on scale and channel relationships. The second and third branches use different convolution kernel sizes to capture features across various receptive fields. These branches collaborate to enhance pixel-level relationships, improving foreground-background contrast. On the AI-TOD dataset, our method improved AP50 by 8.6% over state-of-the-art approaches.

关键词： image resolution Convolution Object detection Feature extraction Data processing Sensors Object recognition Kernel remote sensing signal resolution

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THE HYPERVIEW CHALLENGE: ESTIMATING SOIL PARAMETERS FROM HYPERSPECTRAL imageS 29

THE HYPERVIEW CHALLENGE: ESTIMATING SOIL PARAMETERS FROM HYP...

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IEEE International conference on image processing (ICIP)

作者： Nalepa, Jakub Le Saux, Bertrand Longepe, Nicolas Tulczyjew, Lukasz Myller, Michal Kawulok, Michal Smykala, Krzysztof Gumiela, Michal KP Labs Gliwice Poland Silesian Tech Univ Gliwice Poland European Space Agcy Lab Frascati Italy QZ Solut Opole Poland

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Improving agricultural practices through exploiting the recent imaging and machine learning advancements plays a key role nowadays to ensure sustainable food security, and to help us deal with the climate change. Quantifying soil parameters can lead to optimizing the fertilization process but it is cumbersome, time-consuming and difficult to scale, as it requires performing in-situ soil measurements that are later analyzed in the laboratory settings. In the HYPERVIEW challenge, we aim at automating the soil analysis thanks to the utilization of hyperspectral images that capture very detailed information about the scanned objects in hundreds of contiguous hyperspectral bands. Such imagery can be effectively analyzed using an array of classical and deep machine learning approaches. Also, the AI techniques can be deployed on-board the imaging satellites-it opens new doors related to the scalability of the solution. The winners of the challenge will be offered a unique opportunity to run their proposed solution in orbit, on-board the Intuition-1 satellite, equipped with a hyperspectral imager and on-board AI capabilities.

关键词： Hyperspectral imaging remote sensing agriculture applications soil analysis artificial intelligence

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Joint Semantic Segmentation of Optical and SAR image in Hazy Environments via Cross-modal Information Rectification and Cross-attention Fusion

Joint Semantic Segmentation of Optical and SAR Image in Hazy...

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

作者： Xinyue Fan Libao Zhang School of Artificial Intelligence Beijing Normal University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Semantic segmentation is crucial in remote sensing image processing. In recent years, semantic segmentation using optical and SAR images for multi-modal fusion is gaining attention for its good results. The current research primarily encompasses two problems: 1) Existing fusion methods are designed for clear images and struggle in harsh weather. 2) Current fusion methods insufficiently capture multi-modal information correlation. This paper presents a joint semantic segmentation of optical and SAR in hazy environments network that incorporates channel fusion for feature enhancement and cross-attention for feature fusion, enabling efficient segmentation of hazy optical images. First, we design a channel-guided crossmodal information correction module. This module regards fog as noise and uses high-confidence features of one modality to calibrate the other modality, thereby reducing the impact of fog. Secondly, to address the issue of insufficient fusion of multimodal information, we introduce a cross-attention fusion module to effectively combine the complementary information from two streams leveraging the large receptive field enabled by self-attention. The implementation results show that this method has better results for the semantic segmentation of blurry remote sensing images.

关键词： Integrated optics Semantic segmentation Optical fiber networks Optical imaging Adaptive optics Radar polarimetry Optical sensors Optical noise Optical signal processing remote sensing

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remote Surveillance Practices of Surface Displacements for Kazakhstan and Azerbaijan Oil and Gas Fields and Pipelines using Multi-Temporal Radar Satellite Interferometry 28

Remote Surveillance Practices of Surface Displacements for K...

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conference on image and signal processing for remote sensing XXVIII

作者： Bayramov, E. Buchroithner, M. Kada, M. Zhuniskenov, Y. Nazarbayev Univ Sch Min & Geosci Room 6-23953 KabanbayBatyr Ave Nur Sultan 010000 Kazakhstan Tech Univ Dresden Inst Cartog Westflugel Zimmer W13610 Hulsse BauHelmholtzstr D-01069 Dresden Germany Tech Univ Berlin Inst Geodesy & Geoinformat Sci Room H 5123Main Bldg D-10623 Berlin Germany

ISBN: (纸本)9781510655386;9781510655379

This paper focused on the applied remote surveillance practices of surface displacements for the subsidence and uplift risk assessments of the largest oil and gas fields and pipelines in Kazakhstan and Azerbaijan. In case of InSAR applications for Tengiz Oilfield in Kazakhstan, SBAS-InSAR remote sensing technique followed by 3D and 2D decompositions showed the continuous subsidence at the Tengiz oilfield with increasing velocity. 3D and 2D decompositions of LOS measurements to vertical movement showed that the Tengiz Oil Field 2018-2020 continuously subsided with the maximum annual vertical deformation velocity around 70 mm. The vertical deformation confirmed typical patterns of subsidence caused by oil extraction. However, detected east-west and north-south horizontal movements at the Tengiz field clearly indicated that the study area crossed by seismic faults is also affected by natural tectonic processes. In case of InSAR applications for pipelines, both PS-InSAR and SBAS-InSAR techniques showed continuous subsidence in the kilometer range of 13-70 crossing two seismic faults. The ground uplift deformations were observed in the pipeline kilometer range of 0-13. Although both PS-InSAR and SBAS measurements were highly consistent in deformation patterns and trends along pipelines, they showed differences in the spatial distribution of ground deformation classes and noisiness of produced results. SBAS showed better performance than PS-InSAR along buried petroleum and gas pipelines in the following aspects: the complete coverage of the measured points, significantly lower dispersion of the results, continuous and realistic measurements and higher accuracy of ground deformation rates against the GPS historical measurements. In case of InSAR applications for Absheron oil and gas fields, PS-InSAR showed the existence of ground deformation processes observed for the period of 2015-2017 with three hotspots of highest subsidence rates and three hotspots of highest uplift r

关键词： PS-InSAR SBAS-InSAR GIS remote sensing Radar

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Multispectral image Compression Algorithm Based on Silced Convolutional LSTM 10th

Multispectral Image Compression Algorithm Based on Silced Co...

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10th International conference on Communications, signal processing, and Systems

作者： Wang, Kang Zhang, Ning Hu, Kedi Cao, Tongbo Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing 210016 Peoples R China AVIC Xian Flight Automat Control Res Inst Facri Natl Key Lab Sci & Technol Aircraft Control Xian 710065 Peoples R China

ISBN: (纸本)9789811903908;9789811903892

The multispectral imaging which are used for remote sensing imaging has a large amount of data, so this paper proposed a deep learningmethod which is based on sliced convolutional LSTM for multispectral image compression. Compared with other algorithms, the proposed algorithm further compresses the multispectral images by considering the similarity between the spectra and removing the inter-spectral redundancy. The proposed algorithm is based on end to end framework which is consist of encoder, decoder, entropy coding and quantizer. In experiments, the PSNR of proposed model is compared with that of JPEG2000 to evaluate the performance of our algorithm at several different bit rates.

关键词： Sliced convolutional LSTM Multispectral image Spectral features

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GEOMETRIC LOW-RANK TENSOR APPROXIMATION FOR remoteLY SENSED HYPERSPECTRAL AND MULTISPECTRAL imageRY FUSION 47

GEOMETRIC LOW-RANK TENSOR APPROXIMATION FOR REMOTELY SENSED ...

引用

47th IEEE International conference on Acoustics, Speech and signal processing (ICASSP)

作者： Liu, Na Li, Wei Tao, Ran Beijing Inst Technol Sch Informat & Elect Beijing Peoples R China

ISBN: (纸本)9781665405409

Improving the spatial resolution of a hyperspectral image (HSI) is of great significance in the remotely sensed field. By fusing a high-spatial-resolution multispectral image (MSI) with an HSI collected from the same scene, hyperspectral and multispectral (HS-MS) fusion has been an emerging technique to address the issue. Extracting complex spatial information from MSIs while maintaining abundant spectral information of HSIs is essential to generate the fused high-spatial-resolution HSI ((HSI)-I-2). A common way is to learn low-rank/sparse representations from HSI and MSI, then reconstruct the fused (HSI)-I-2 based on tensor/matrix decomposition or unmixing paradigms, which ignore the intrinsic geometry proximity inherited by the low-rank property of the fused (HSI)-I-2. This study proposes to estimate the high-resolution (HSI)-I-2 via low-rank tensor approximation with geometry proximity as side information learned from MSI and HSI by defined graph signals, which we name GLRTA. Row graph G(r) and column graph G(c) are defined on the horizontal slice and lateral slice of MSI tensor M respectively, while spectral band graph G(b) is defined on a frontal slice of HSI tensor H. Experimental results demonstrate that the proposed GLRTA can effectively improve the reconstruction results compared to other competitive works.

关键词： Graph signal processing hyperspectral imagery low-rank tensor approximation remote sensing super-resolution

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White Gaussian Noise Removal From Computed Tomography images Using Python 4

White Gaussian Noise Removal From Computed Tomography Images...

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4th International conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Bhonsle, Devanand Pillai, Anu G. Rizvi, Tanu Mishra, Ravi Sahu, Anil Kumar Mishra, Rama Shri Shankaracharya Technical Campus Department of Electrical Engg. Bhilai India Kalinga University Department of Electrical Engg. Raipur India Ghriet Department of Electronics and Telecommunication Nagpur India Siddharth Institute of Technology and Sciences Department of Electronics and Communication Narapally Hydrabad India Aks University Department of Electrical Engineering Satna India

ISBN: (纸本)9798350343670

image processing one of the most important area of research as it is used to improve the quality of any images. It has vast application areas viz. law enforcement, entertainment, satellite imaging, remote sensing, weather forecasting, medical imaging etc. Medical imaging is one of the most emerging area in medical science and it helps the radiologists to understand any abnormalities or diseases in human body. There are different types of medical imaging techniques viz. Computed Tomography (CT) imaging, Ultra- Sound (US) imaging, X-ray imaging, Positron emission tomography (PET) imaging, Magnetic Resonance Imaging (MRI) etc. These all the imaging techniques are used for different purposes and each uses entirely different phenomena to capture the images. For each imaging techniques;expert radiologists are required who can examine the medical images and give the results accordingly. All the above mentioned medical images are used to examine internal organs of the body. However;almost all the imaging techniques suffer from a phenomenon called "Noise". In general noise is an unwanted signal which introduced in almost all the signals and deteriorates them. In general;images are two dimensional signals. All the aforementioned images suffer from different types are noise signals e.g. CT images are affected by Additive White Gaussian Noise (AWGN). Each imaging techniques use different frequency band hence noise introduced in the images are also different to each other. In this chapter;various types of imaging techniques and introduction of noise have been discussed. There are many techniques using which we can remove the noise of the signals. The process in which noise signals are suppressed from the images is called "image De-noising". © 2024 IEEE.

关键词： CT images image De-noising image processing MSE PSNR SNR Thresholding Wavelet Transform

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4th International conference on image processing and Capsule Networks, ICIPCN 2023

4th International Conference on Image Processing and Capsule...

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4th International conference on image processing and Capsule Networks, ICIPCN 2023

ISBN: (纸本)9789819970926

The proceedings contain 48 papers. The special focus in this conference is on image processing and Capsule Networks. The topics include: Multispectral Fusion of Multisensor image Data Using PCNN for Performance Evaluation in Sensor Networks;enhanced Feature Fusion from Dual Attention Paths Using Feature Gating Mechanism for Scene Categorization of Aerial images;Histopathology Breast Cancer Classification Using CNN;Brain Tumor Recognition from MRI Using Deep Learning with Data Balancing Methods and Its Explainability with AI;Multi-class Plant Leaf Disease Classification on Real-Time images Using YOLO V7;semantic image Segmentation of Agricultural Field Problem Areas Using Deep Neural Networks Based on the DeepLabV3 Model;u-Net-Based Segmentation of Coronary Arteries in Invasive Coronary Angiography;modern Challenges and Limitations in Medical Science Using Capsule Networks: A Comprehensive Review;securing Data in the Cloud: The Application of Fuzzy Identity Biometric Encryption for Enhanced Privacy and Authentication;Modified U-Net and CRF for image Segmentation of Crop images;flameGuard: A Smart System for Forest Fire Detection and Control;classification and Analysis of Chilli Plant Disease Detection Using Convolution Neural Networks;a New Multi-level Hazy image and Video Dataset for Benchmark of Dehazing Methods;change Detection for Multispectral remote sensing images Using Deep Learning;studies on Movie Soundtracks Over the Last Five Years;an Enhanced Real-Time System for Wrong-Way and Over Speed Violation Detection Using Deep Learning;tea Leaf Disease Classification Using an Encoder-Decoder Convolutional Neural Network with Skip Connections;EEG signal Feature Extraction Using Principal Component Analysis and Power Spectral Entropy for Multiclass Emotion Prediction;preface.

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Improved Radon Transform and Differential Autocorrelation Based Motion Blur Parameter Estimation Method

Improved Radon Transform and Differential Autocorrelation Ba...

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signal processing and Intelligent Computing (SPIC), International conference on

作者： Jiongming Xu Xiai Chen Tao Sun Jiayi Yan Huannan Qi Xinglin Hou School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China School of Automotive Engineering Changzhou Institute of Technology Changzhou China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

Restoring of motion blurred image is an important research direction in digital image processing technology, which is of great importance in many fields including aerospace, remote sensing technology, medical image processing and so on. However, the existing motion fuzzy parameter is still inaccurate when solving the length and angle imprecisely, which impact the restoration of motion blurred images. The paper proposes improvement methods for this problem. First, Radon transform is accepted in measuring relative motion angle in image pre-processing. Least squares line fitting based accurate angle estimation algorithm is proposed for small blur length. Second, an improved method based on differential autocorrelation length estimation is obtained for high accurate relative motion length estimation. In addition, this paper proposes the corresponding motion fuzzy parameter estimation algorithm for the images with different fuzzy degrees to make the estimation results more *** results demonstrate that the proposed method can estimate motion blur parameters more accurately. The restored image is superior to the existing methods in both subjective and objective. The proposed method is of great importance in engineering.

关键词： Accuracy Parameter estimation Fitting Noise reduction Estimation signal processing algorithms Transforms image restoration Autocorrelation remote sensing

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A Coarse-to-Fine Change Detection Method for remote sensing Sparse Cultivated Land

A Coarse-to-Fine Change Detection Method for Remote Sensing ...

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Asia-Pacific signal and Information processing Association Annual Summit and conference (APSIPA)

作者： Yuan Hu Yifan Zhang Mingyang Ma Shaohui Mei Northwestern Polytechnical University Xi’an P.R. China

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

remote sensing (RS) images contain rich geographic information. For specific application scenarios like cultivated land, it is necessary to select areas of interest to reduce data scale and focus on detailed features. In this article, an innovative coarse-to-fine change detection method (CFCD) for sparse cultivated land is proposed to address these problems. Coarse screening module (CSM) first removes irrelevant low-difference image pairs, and then fine detection module (FDM) accurately locate change areas in remaining images. Experimental results show that two coarse screening methods can take out many disturbed images, and provide strong support for subsequent fine detection methods to achieve performance improvement.

关键词： Deep learning Asia Information processing Feature extraction Frequency division multiplexing remote sensing

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