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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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SCANPATH PREDICTION VIA SEMANTIC REPRESENTATION OF THE SCENE 29

SCANPATH PREDICTION VIA SEMANTIC REPRESENTATION OF THE SCENE

引用

IEEE International conference on image processing (ICIP)

作者： Zhang, Kepei Lu, Meiqi Lu, Zheng Zhang, Xuetao Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China China Acad Space Technol Inst Remote Sensing Satellite Beijing Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Aiming at the problem that the current scanpath prediction methods have insufficient representation of object association, we propose a scanpath prediction model based on semantic representation of the scene. Our model uses a panoramic segmentation network to separate object instances and backgrounds in scenes, and uses the attention mechanism to learn the semantic correlation between objects, which effectively extracts the deep image information related to the current task. We also propose a dual-branch structure predicting the fixation position and duration simultaneously, to fully simulate the temporal and spatial distribution of the human eye's attention in visual search. Experimental results show that our model has obvious advantages over the existing scanpath prediction methods in search efficiency and scanpath similarity, and can accurately predict the fixation duration.

关键词： Human Scanpath Visual Search Semantic Representation Inverse Reinforcement Learning

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A Study on Deep Learning-Based image Target Identification Techniques 5th

A Study on Deep Learning-Based Image Target Identification T...

引用

5th International conference on 3D Imaging Technologies—Multidimensional signal processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Wang, Yining College of Electronic Information and Optical Engineering Taiyuan University of Technology Shanxi Taiyuan China

ISBN: (纸本)9789819751808

This study aims to explore deep learning-based image target recognition methods to improve the performance of target detection and classification in the field of computer vision. The experiments use satellite-acquired remote sensing images of moving objects, including four target categories such as buses, cars, bicycles and ships. In model training, we performed parameter optimisation by stochastic gradient descent algorithm and set the training period to 12. To validate the model’s effectiveness, we compare the improved R-CNN (Region-based Convolutional Neural Network), traditional CNN (Convolutional Neural Network) and Faster-RCNN (Faster Region-based Convolutional Neural Network) models, and generate confusion matrices to evaluate their performance. The experimental results show that the improved model exhibits excellent performance in recognition of different types of target objects, especially for larger targets. This research provides reliable experimental support for the application of deep learning in the field of image target recognition and is expected to promote further development and innovation in this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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M2F2Net: Multi-stage Mixed Feature Fusion Network For remote sensing Change Detection

M2F2Net: Multi-stage Mixed Feature Fusion Network For Remote...

引用

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

作者： Binhao Gu Lei Song Youyong Kong Binjie Gu Southeast University Nanjing China Jiangnan University Wuxi China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

remote sensing image change detection (CD) seeks to analyze and discern changes in surface objects through the use of multi-temporal remote sensing imagery. However, as image resolution advances, existing methods often fall short in capturing comprehensive visual feature representations, and their networks are prone to spatial degradation. This results in incomplete boundary detection and difficulties in identifying subtle changes. To overcome these challenges, this paper introduces a Siamese U-Net architecture incorporating Multistage Mixed Feature Fusion (M 2 F 2 Net). The proposed model leverages a convolutional neural network (CNN) as the main encoder for local feature extraction, while employing a Transformer-based auxiliary encoder to capture global features. Furthermore, we introduce a Feature Fusion Module (FFM) to facilitate the efficient integration of local and global information. Additionally, we propose a novel convolutional unit and a Spatial Attention Module (SAM) designed to enhance the extraction of image features. Experimental results confirm that the proposed approach delivers substantial improvements across multiple evaluation criteria, while offering a superior accuracy compared to existing state-of-the-art change detection methods.

关键词： Degradation Visualization image resolution Convolution Semantics Feature extraction Transformers Convolutional neural networks Speech processing remote sensing

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Spaceborne SAR Ship Data Expansion Based on GAN 2

Spaceborne SAR Ship Data Expansion Based on GAN

引用

2nd International conference on image processing, Computer Vision and Machine Learning, ICICML 2023

作者： Jiang, Jiacheng Fang, Caiting Wang, Junrui Xie, Baorong Shanghai Academy of Spaceflight Technology Shanghai Institute of Aerospace Electronics Technology Shanghai China

ISBN: (纸本)9798350331417

In response to the lack of high-quality and sufficient synthetic aperture radar (SAR) data that can be paired with light ship images in practice, this paper proposes a method to convert optical ship images into SAR images based on GAN networks. In order to improve the coordination of generated images and stabilize the training process, this paper establishes a generator with a self-attention mechanism combined with spectral normalization operations. Secondly, the maximum spatial perturbation consistency loss is introduced to reduce the spatial changes between optical images and SAR images caused by changes in ship size and background noise. Finally, in order to speed up the convergence speed and stability of the model, a relative discriminator is introduced. Experiments conducted on the HRSID and LEVIR remote sensing ship data sets show that the FID (Fréchet Inception Distance) of the SAR ship images generated by this model is reduced to 57.25. At the same time, compared with the current optimal algorithm, the peak signal-to-noise ratio is increased by 5.89% on average. The structural similarity loss is increased by 18.75% on average, and the mean square error is reduced by 13.44% on average. © 2023 IEEE.

关键词： data expansion image generation SAR ship image

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Deep learning landslide extraction based on multi-temporal image difference local spatial information

Deep learning landslide extraction based on multi-temporal i...

引用

2023 International conference on image, signal processing, and Pattern Recognition, ISPP 2023

作者： Miao, Sheng Qu, Yu Liu, Xirong College of Geodesy and Geomatics Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9781510666351

Landslide as a natural disaster, it is important to obtain accurate information of landslide spatial distribution. The current method of landslide extraction is mainly based on the spectral features, spatial structure and texture features of landslides on remote sensing images, which is difficult to accurately extract the spatial distribution of landslides for surface structures with similar spectral features (bare soil, villages, exposed rocks, roads, water bodies, etc.) and does not make full use of local spatial information features and causes loss of landslide boundary information. To address the above problems, this paper proposes a method to extract landslides based on the fusion of local spatial information with multi-temporal image differences. The high-resolution images before and after the landslide are used to calculate the difference images, extract each band and combine the temporal difference images to combine the bands and use them as input features for deep learning. The ASPP module is introduced to the U-net network model for expanding the local spatial information perception field, enhancing the preservation of landslide boundary information, and guiding the attention mechanism of feature downsampling for extracting multi-scale contextual information. The evaluation shows that the method can ensure the detection error of landslide area range is less than 8.6%, the extraction accuracy is more than 95%, and the Kappa is 0.90. The experimental results prove the feasibility and accuracy of the method in rainfall landslide detection. © 2023 SPIE.

关键词： Landslides

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CFAR-CNN Detector of Ships from SAR image Using Generalized Gamma Distribution and Real Dataset 7

CFAR-CNN Detector of Ships from SAR Image Using Generalized ...

引用

7th International conference on image and signal processing and their Applications, ISPA 2022

作者： Chabbi, Souad Farah, Faical Guidoum, Nouh Département d'Electronique Constantine Algeria

ISBN: (纸本)9781665480420

The detection and localization of ships is important and must be accurate and rapid. The Synthetic Aperture Radar (SAR) is optimal for ship detection. Generally, Constant False Alarm Rate (CFAR) algorithms are used to detect ships from a SAR image on the base of satellite remote sensing imaging. However, due to the rapid development of technology, the remote sensing data have shown the features of big-data. The analyze of big-data improves the accuracy and speed of the ship detection. Therefore, Deep Learning (DL) is recommended and exactly the Convolutional Neural Network (CNN) model has greatly improved the static image recognition performance. In this paper, to improve the accuracy and speed of the ship detection from SAR image, we introduce the CFAR-CNN detector. First, after modeling the sea clutter by the Generalized Gamma (GΓ) distribution, a CFAR global detector is applied. Then, to improve the accuracy of the previous results, a CNN local detector is applied. To this end, a real dataset is used to obtain the optimal CNN model. We have shown that this detector is rapid and tends toward ideality. © 2022 IEEE.

关键词： Synthetic aperture radar

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Industrial Video Surveillance image Denoising based on Non-subsampled Contourlet Transform 4

Industrial Video Surveillance Image Denoising based on Non-s...

引用

4th IEEE International conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Zhang, Qingyun Chen, Zhuo Xie, Pengfei Wang, Yingxu Shi, Ruibo Zhou, Qian Pu, Yuduo Shandong University of Science and Technology Dept. of Electronic and Information Eng. Qingdao China Rizhao Guangming Electric Power Service Co. Ltd. Rizhao China State Grid Information & Telecommunication Branch Beijing China Wuhan Fiberhome Technical Services Co. Ltd Wuhan China

ISBN: (纸本)9798350363586

With the continuous expansion of industrial video scale, visualization technology has become an indispensable technical means. However, during the collection and transmission of images, they may be affected by factors such as the device itself and environmental factors, which can lead to noise pollution and a decrease in image quality, which is not conducive to image post-processing. Therefore, industrial video surveillance image denoising has become an important research topic, and using various methods to improve image quality has become an important research direction. In order to address the ringing effect caused by hard threshold denoising, this paper proposes an adaptive smooth non-subsampled contourlet transform (NSCT) denoising algorithm. The proposed method utilizes the correlation of each sub-band to improve the denoising function, making it adaptive. The simulation results show that the algorithm proposed in this paper improves subjective perception such as contrast and detail clarity of remote sensing images, as well as objective evaluation indicators such as peak signal-to-noise ratio, information entropy, and correlation coefficient © 2024 IEEE.

关键词： Contourlet transform

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A proposal learning strategy on CNN architectures for targets classification 6

A proposal learning strategy on CNN architectures for target...

引用

6th International conference on Advanced Technologies for signal and image processing, ATSIP 2022

作者： Toumi, Abdelmalek Cexus, Jean-Christophe Khenchaf, Ali Umr Cnrs 6285 Ensta Bretagne Lab-STICC 2 Rue François Verny Brest Cedex 9 29806 France

ISBN: (纸本)9781665451161

Synthetic Aperture Radar (SAR) imagery has a great potential in remote sensing field in order to maintain aerial, land and maritime surveillance. To improve this potential for several goals, Machine Learning (ML) techniques are used for effective and efficient classification of remotely sensed imagery. The strengths of machine learning include the capacity to handle data of high dimensionality with several modality and to map classes with very complex and varied *** focus in this paper on the ship classification from SAR imagery using a deep learning algorithms with transfer leaning mechanisms. To improve the recognition performances, several learning strategies are proposed. We illustrate these issues through applying a transfer learning on deep learning ship classification with several learning strategies. We used in this work especially no huge labelled dataset available for training. The aim is thus to see how to leverage knowledge from models pre-trained on other tasks (source tasks) and use them for target classification (target task). © 2022 IEEE.

关键词： remote sensing

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Enhancing remote sensing Vision-Language Models for Zero-Shot Scene Classification

Enhancing Remote Sensing Vision-Language Models for Zero-Sho...

引用

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

作者： Karim El Khoury Maxime Zanella Benoît Gérin Tiffanie Godelaine Benoît Macq Saïd Mahmoudi Christophe De Vleeschouwer Ismail Ben Ayed UCLouvain Belgium UMons Belgium ÉTS Montreal Canada

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Vision-Language Models for remote sensing have shown promising uses thanks to their extensive pretraining. However, their conventional usage in zero-shot scene classification methods still involves dividing large images into patches and making independent predictions, i.e., inductive inference, thereby limiting their effectiveness by ignoring valuable contextual information. Our approach tackles this issue by utilizing initial predictions based on text prompting and patch affinity relationships from the image encoder to enhance zero-shot capabilities through transductive inference, all without the need for supervision and at a minor computational cost. Experiments on 10 remote sensing datasets with state-of-the-art Vision-Language Models demonstrate significant accuracy improvements over inductive zero-shot classification. Our source code is publicly available on Github: https://***/elkhouryk/RS-TransCLIP

关键词： Scene classification Accuracy Sensitivity Computational modeling Source coding signal processing algorithms Computational efficiency Speech processing remote sensing Software development management

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Listless Block Cube Tree Coding for Low Resource Hyperspectral image Compression Sensors 5

Listless Block Cube Tree Coding for Low Resource Hyperspectr...

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5th International conference on Multimedia, signal processing and Communication Technologies (IMPACT)

作者： Chandra, Harshit Bajpai, Shrish Integral Univ Fac Engn & Informat Technol ECE Dept Lucknow Uttar Pradesh India

ISBN: (纸本)9781665476478

Hyperspectral (HS) image has rich spectral information content, which facilitates multiple applications including remote sensing. Due to the big data size of the HS image, compression is a required process for the efficiency of image storage and transmission. However, the complexity of the compression algorithms turns real-time compression into a very challenging task. A novel listless set partitioned hyperspectral image compression algorithm is proposed. The proposed compression algorithm uses zero block cube tree structure to exploit the inter and intra sub-band correlation to achieve the compression. From the result, it has been clear that the proposed compression algorithm has low coding complexity with at-par coding efficiency. Thus, it can be a suitable contender for low-resource hyperspectral image sensors.

关键词： Coding Complexity Compression Hyperspectral image Set Partitioned Hyperspectral image Compression Wavelet Transform

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