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检索条件"任意字段=Conference on Image Processing and Pattern Recognition in Remote Sensing"

共 4027 条记录，以下是221-230 订阅

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3DRRDB: Super Resolution of Multiple remote sensing images using 3D Residual in Residual Dense Blocks

3DRRDB: Super Resolution of Multiple Remote Sensing Images u...

引用

IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Ramzy Ibrahim, Mohamed Benavente, Robert Lumbreras, Felipe Ponsa, Daniel Arab Acad Sci & Technol Comp Engn Dept Alexandria Egypt Univ Autonoma Barcelona Dept Comp Sci Barcelona Spain Comp Vis Ctr Campus UAB Barcelona Spain

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The rapid advancement of Deep Convolutional Neural Networks helped in solving many remote sensing problems, especially the problems of super-resolution. However, most state-of-the-art methods focus more on Single image Super-Resolution neglecting Multi-image Super-Resolution. In this work, a new proposed 3D Residual in Residual Dense Blocks model (3DRRDB) focuses on remote sensing Multi-image Super-Resolution for two different single spectral bands. The proposed 3DRRDB model explores the idea of 3D convolution layers in deeply connected Dense Blocks and the effect of local and global residual connections with residual scaling in Multi-image Super-Resolution. The model tested on the Proba-V challenge dataset shows a significant improvement above the current state-of-the-art models scoring a Corrected Peak Signal to Noise Ratio (cPSNR) of 48.79 dB and 50.83 dB for Near Infrared (NIR) and RED Bands respectively. Moreover, the proposed 3DRRDB model scores a Corrected Structural Similarity Index Measure (cSSIM) of 0.9865 and 0.9909 for NIR and RED bands respectively.

关键词： Training Solid modeling Three-dimensional displays PSNR Convolution Superresolution pattern recognition

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EDO-SANet: Shape-Aware Network with Edge Detection Operator for Polyp Segmentation 24

EDO-SANet: Shape-Aware Network with Edge Detection Operator ...

引用

2nd Asia conference on Computer Vision, image processing and pattern recognition (CVIPPR)

作者： Zhao, Yifei Wang, Xiaoying Li, Yang Yin, Junping North China Elect Power Univ Beijing Peoples R China Northeast Normal Univ Changchun Peoples R China Inst Appl Phys & Computat Math Beijing Peoples R China

ISBN: (纸本)9798400716607

Automatic and accurate segmentation of colonic polyps can effectively help physicians quickly identify the size and location of polyps during endoscopy. However, due to the variable intestinal environment, polyps are highly similar to the surrounding tissue leading to over-segmentation or under-segmentation. To tackle these concerns, we propose a novel network with shape-awareness. Specifically, the network employs a dual-stream encoder, including the mainstream Transoformer-based encoder and an auxiliary encoder to extract global and local representations. The auxiliary encoder is built on deformable convolutional v3 for sensing the shape of polyps. In order to better identify the edges of polyps, the Edge Detection Guided Module (EDGM) is proposed to utilize edge detection operators to highlight the importance of edge information in low-level features. Furthermore, we introduce a Semantic Interaction Module (SIM) to better localize and calibrate targets by integrating global and local semantics in high-level features. Compared to other state-of-the-art methods, our model achieves better performance both in learning and generalization ability on five polyp segmentation datasets. The potential applicability of our model to other biomedical fields is demonstrated through its outstanding performance. The code is available at https://***/xff12138/EDOSANet

关键词： Polyp segmentation Medical image processing Deep learning Deformable convolution Edge detection operator

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Characterization of Out-of-distribution Samples from Uncertainty Maps Using Supervised Machine Learning 45th

Characterization of Out-of-distribution Samples from Uncerta...

引用

45th Annual conference of the German-Association-for-pattern-recognition (DAGM GCPR)

作者： Budde, Lina E. Bulatov, Dimitri Strauss, Eva Qiu, Kevin Iwaszczuk, Dorota Tech Univ Darmstadt Dept Civil & Environm Engn Remote Sensing & Image Anal D-64287 Darmstadt Germany Fraunhofer Inst Optron Syst Technol & Image Explo Scene Anal Div D-76275 Ettlingen Germany

ISBN: (纸本)9783031546044;9783031546051

The quality of land use maps often refers to the data quality, but distributional uncertainty between training and test data must also be considered. In order to address this uncertainty, we follow the strategy to detect out-of-distribution samples using uncertainty maps. Then, we use supervised machine learning to identify those samples. For the investigations, we use an uncertainty metric adapted from depth maps fusion and Monte-Carlo dropout based predicted probabilities. The results show a correlation between out-of-distribution samples, misclassifications and uncertainty. Thus, on the one hand, out-of-distribution samples are identifiable through uncertainty, on the other hand it is difficult to distinguish between misclassification, anomalies and out-of-distribution.

关键词： Potsdam dataset error detection semantic segmentation

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2024 IEEE International conference on image processing, ICIP 2024 - Proceedings

2024 IEEE International Conference on Image Processing, ICIP...

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31st IEEE International conference on image processing, ICIP 2024

ISBN: (纸本)9798350349399

The proceedings contain 593 papers. The topics discussed include: MDBFUSION: a visible and infrared image fusion framework capable for motion deblurring;prune channel and distill: discriminative knowledge distillation for semantic segmentation;imbalanced data robust online continual learning based on evolving class aware memory selection and built-in contrastive representation learning;privacy-preserving visual cues communication for hearing-impaired people using deep learning;transformer-based clipped contrastive quantization learning for unsupervised image retrieval;attention enhancement with parallel groups for remote sensing object detection;cross-domain few-shot in-context learning for enhancing traffic sign recognition;and recurrent 3-D multi-level visual transformer for joint classification of heterogeneous 2-D and 3-D radiographic data.

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Implementation of the image super-resolution DWT based algorithm on Raspberry Pi platform for real-time applications

Implementation of the image super-resolution DWT based algor...

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conference on Real-Time processing of image, Depth, and Video Information

作者： Osorno-Ortiz, Raul J. Ponomaryov, Volodymyr, I Reyes-Reyes, Rogelio Cruz-Ramos, Clara Garcia-Salgado, Beatriz P. Sadovnychiy, Sergiy Inst Politecn Nacl ESIME Culhuacan Ciudad De Mexico Mexico Inst Mexicano Petr Ciudad De Mexico Mexico

ISBN: (纸本)9781510673199;9781510673182

Super resolution (SR) is a technique designed for increasing the spatial resolution in an image from a low resolution (LR) to high resolution (HR) size. SR technology has had a considerable demand in a wide variety of applications to recover HR images, such as medicine, engineering, computer vision, pattern recognition and video production, etc. In contrast to interpolation-based algorithms that often introduce distortions or irregular borders, this study proposes an implementation that can preserve the edges and fine details of an original image through the computation of the wavelet decomposition. Different Discrete Wavelet Transform (DWT) families such as: Daubechies, Symlet, and Coiflet were evaluated. The proposed system was implemented on a Raspberry Pi 4 model B, an embedded device, to get around the PC's mobility limitations, making it possible to create an in-expensive and energy- efficient SR system, reducing their complexity in realtime applications. To investigate the visual performance, SR images have been analysed in subjective matter via human perception view, guaranteeing good perception for the images of different nature from three different datasets such as FullHD (DIV2K), medical (Raabin WBC), and remote sensing (Sentinel- 1). The experimental results of designed implementations appear to demonstrate good performance in commonly used objective criteria: execution time, SSIM, and PSNR (0.742 sec., 0.9164, and 38.72 dB), respectively for images with a super resolution size of 1356 x 2040 pixels.

关键词： Super resolution Discrete Wavelet Transform (DWT) Parallel programing Raspberry Pi real-time processing

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Hyperspectral image classification based on spectral feature extraction

Hyperspectral image classification based on spectral feature...

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2023 International conference on image, Signal processing, and pattern recognition, ISPP 2023

作者： Wang, Li Wang, Wei Department of Electronic Engineering Xi'an Aeronautical Institute Shaanxi Xi'an710077 China

ISBN: (纸本)9781510666351

Hyperspectral remote sensing can synchronously obtain the surface coverage space image and continuous spectral data, and can realize fine classification and recognition of ground objects. The motivation for this paper is to search an effective specteal feature extraction method for hyperspectral classification. The main methods used to extract spectral features of hyperspectral images are principal component analysis (PCA) and linear discriminant analysis (LDA). The realization of classification based on k-nearest neighbor (KNN) classifier is discussed. The training set and test set of Pavia University, Indian Pines and Salinas are selected as the data source, and the selection experiment of algorithm parameters is set, and the classification performance under different feature extraction methods is compared and analyzed. The experimental results show that with the parameters of OA, PA and Kappa based on the confusion matrix as the evaluation indicators, the classification accuracy based on LDA method is higher, and the effective classification of ground objects can be achieved. The results could provide a basis for further research on hyperspectral image classification based on spatial spectral feature extraction. © 2023 SPIE.

关键词： Principal component analysis

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FG-PIH: A Fusion of Fresnelet Transform and Gradient Directional pattern for Perceptual image Hashing 2

FG-PIH: A Fusion of Fresnelet Transform and Gradient Directi...

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2nd International conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Meesala, Pavani Thounaojam, Dalton Meitei Computer Science and Engineering National Institute of Technology Silchar Computer Vision Laboratory Silchar India

ISBN: (纸本)9798350366570

Perceptual image hashing is pivotal in various image processing applications, including image authentication, content-based image retrieval, tampered image detection, and copyright protection. This paper proposes a novel approach for perceptual image hashing by combining the Fresnelet Transform with Gradient Directional patterns. Using the FG-PIH technique, the proposed method achieves superior robustness against common image processing attacks while maintaining perceptual similarity for near-duplicate images. Experimental results on standard benchmark datasets demonstrate the effectiveness and efficiency of the proposed Fresnelet Transform-based perceptual image hashing scheme. Furthermore, comparative analysis against state-of-the-art methods underscores the competitiveness of our approach in terms of hash quality and computational complexity. © 2024 IEEE.

关键词： Hamming distance

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Exploring CNN for urban area Extraction from Polarimetric SAR data

Exploring CNN for urban area Extraction from Polarimetric SA...

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2024 IEEE Mediterranean and Middle-East Geoscience and remote sensing Symposium, M2GARSS 2024

作者： Hammouda, I. Latrache, H. Souissi, B. Ouarzeddine, M. Faculty of Electronics and Computer Science Image Processing and Radiation Laboratory Algiers Algeria CRSTDLA Computational Linguistic Departement Algiers Algeria

ISBN: (纸本)9798350358582

The convolutional neural networks CNNs have been widely used in pattern recognition and image processing in the last ten years. This paper intends to identify the urban area from polarimetric Synthetic aperture radar (PolSAR) data using the CNN model. This model has shown its effectiveness in classification. For this purpose, we propose to use a feature vector that contains the elements of the circular matrix and the calculated parameters from Cloude decomposition (entropy, alpha angle and anisotropy). The used elements are roll invariants, which make them not sensitive to the orientation angle, which can create ambiguity between oriented buildings and vegetation. To test the performance of the proposed method, ALOS-2/PALSAR-2L-band and AIRSAR C-band over the San-Francisco, USA were used. The results show that man-made targets could be effectively discriminated from natural scenes using the proposed method. © 2024 IEEE.

关键词： Synthetic aperture radar

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remote sensing Few-shot Object Detection Using Adaptive Feature Modification and Gaussian Dynamic Dilated Convolution 16

Remote Sensing Few-shot Object Detection Using Adaptive Feat...

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2024 16th International conference on Graphics and image processing, ICGIP 2024

作者： Zhiwei, Zhang Jiangtao, Wang Penglei, Chen Cheng, He School of Physics and Electronic Information Huaibei Normal University Anhui Province Huaibei City China Anhui Province Key Laboratory of Intelligent Computing and Applications Anhui Province Huaibei City China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Object detection in remote sensing images relies on a large amount of annotated data for training, but adequately annotating novel classes is difficult. Few-shot object detection(FSOD) address this problem by fine-tuning on finitely annotated novel classes, this results in its within-class variation being limited, making it difficult to adequately fit the within-class distribution. In order to alleviate this problem, this paper proposed a few-shot object detection method in remote sensing based on adaptive feature modification and guided by Gaussian dynamic dilated convolution. Firstly, in view of the difficulty of novel classes recognition, an adaptive feature modification algorithm is proposed. By using the characteristics of the two-branch network architecture of meta-learning itself, the support features are integrated into the query feature extraction network as convolutional bias to adaptively modify the query features. Secondly, in order to simulate intra-class variation of noise (feature perturbation caused by noise in the foreground), the backbone network weights were reparameterized to make them fit Gaussian distribution variables during fine-tuning, and the feature perturbation was achieved with parameter variation. Finally, in order to simulate intra-class scale variation (different foreground sizes), the convolution is changed into a dynamic dilation convolution, and different dilation rates are used to replace feature scale variation. Extensive experiments on two widely used remote sensing datasets (NWPU.V2, dior) demonstrate the effectiveness of the proposed method. © 2025 SPIE.

关键词： Gaussian distribution

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Ship and Course Detection in remote sensing images Based on Key-Point Extraction

Ship and Course Detection in Remote Sensing Images Based on ...

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2023 International conference on Algorithm, Imaging processing, and Machine Vision, AIPMV 2023

作者： Zhang, Tao Yang, Xiaogang Lu, Ruitao Li, Qi Xia, Wenxin Su, Shuang Tang, Bin Xi'an Research Institute of High Technology Xi'an710025 China

ISBN: (纸本)9781510672444

remote sensing image ship target detection and course discrimination is one of the important supports for building a maritime power. Since ship target in remote sensing images are generally in strips, the IOU score is very sensitive to the angle of bounding box. Moreover, the angle of the ship is a periodic function, this discontinuity will cause performance degeneration. Meanwhile, methods generally use oriented bounding boxes as anchors to handle rotated ship target and thus introduce excessive hyper-parameters such as box size, aspect ratios. Aiming at the problem of complex calculation of anchor frame traversal mechanism and discontinuity of angle regression caused by increasing angle attribute in ship target detection of remote sensing image, a ship target heading detection method based on ship head point is proposed. The discontinuous angle regression problem is transformed into a continuous key point estimation problem, and the ship target detection and heading recognition are unified. Second, CA attention mechanism is added to the feature extraction network to enhance the attention to the ship target and predict the center point of the ship target. The offset and target width at the center point are regressed. Then, return the heading point and offset to obtain the accurate heading point position. Next, the rotation angle of the ship is determined according to the coordinates of the center point and the ship head point. Combined with the predicted width and height of the ship, the rotation frame detection of the ship target is completed. Finally, the center point and the bow point are connected to determine the course of the ship target. The effectiveness of the proposed method is verified on the RFUE and open source HRSC2016 datasets, respectively, and it also has good robustness in complex environments. © 2024 SPIE.

关键词： Ships

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