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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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PAN -SHARPENING BASED ON JOINT SALIENCY DETECTION FOR MULTIPLE remote sensing imageS

PAN -SHARPENING BASED ON JOINT SALIENCY DETECTION FOR MULTIP...

引用

IEEE International conference on image processing (ICIP)

作者： Zhang, Libao Zhu, Wanning Sun, Yang Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

ISBN: (纸本)9781728163956

Requirements of spectral and spatial quality differ from region to region in remote sensing images, which is a significant challenge for pan-sharpening. Joint saliency analysis not only fulfills these demands, but also ensures the consistency by considering the mutual information of multiple images. Thus, we propose a pan -sharpening method based on joint saliency analysis and improved intensity hue saturation (HIS) for multiple remote sensing images. Firstly, we introduce an improved HIS method to obtain an accurate estimation of the intensity component. Then, we design a joint saliency analysis method based on global contrast calculation and intensity feature extraction, which is subsequently compensated by texture features to generate adaptive injection gains. Finally, we use the injection gains to inject the detail into the multispectral (MS) image. Experimental results demonstrate that our method has better performance in guaranteeing consistency in multiple images, improving spatial quality and preserving spectral fidelity.

关键词： remote sensing image pan-sharpening joint saliency analysis co-clustering compensation strategy

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image Interpolation Using Non-adaptive Scaling Algorithms for Multimedia Applications—A Survey

Image Interpolation Using Non-adaptive Scaling Algorithms fo...

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International conference on Automation, signal processing, Instrumentation and Control, i-CASIC 2020

作者： Neetha, C.H. John Moses, C. Selvathi, D. Department of Electronics and Communication Engineering Sreyas Institute of Engineering and Technology Hyderabad Telangana500068 India Department of Electronics and Communication Engineering Mepco Schlenk Engineering College Sivakasi Tamil Nadu626005 India

ISBN: (纸本)9789811582202

image interpolation is a technology of maintaining the quality of digital images by introducing new high-quality pixels. The interpolation schemes are used in several image processing applications like computer graphics, reconstructing medical images, producing high resolution in still images, and in television pictures as well as processing remote sensing images. This proposed survey deals with the study of different non-adaptive image interpolation techniques used for multimedia applications. This study analyzes the characteristics of different non-adaptive image interpolation algorithms in terms of peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM). From the review on interpolation schemes, this work identifies that several low-complexity conventional non-adaptive image interpolation schemes outperform the recently developed non-adaptive image interpolation algorithms in terms of PSNR. Further, it suggests that for any decisive applications, the conventional non-adaptive interpolation schemes can also be used to maintain the quality of picture. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Surveys

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Machine Learning Techniques in Land Cover Classification using remote sensing Data

Machine Learning Techniques in Land Cover Classification usi...

引用

2021 International conference on Intelligent Technologies, CONIT 2021

作者： Mayani, Megharani B Itagi, Rajeshwari Govt. Polytechnic Dept. of E and C Belagavi India KLEIT Dept. of E and C Hubballi India

ISBN: (纸本)9781728185835

Replacement of traditional farming by modern tools and techniques is the need of the hour. To orient Indian agriculture system towards technology, a support system is required to give a better economy growth of the country. Hyper spectral data collected from a device can be analyzed on periodic basis by exploring the huge data collected in the area of interest. Various remote sensing satellite data in hyperspectral bands can be an advantage in monitoring very tiny variations in soil, land, water etc. remote sensing data are often degraded by many issues that may include the failure of on-board hardware, signal downlink, atmospheric conditions and overall quality of the sensors in terms of signal noise ratio or sharpness. These factors reduce the quality of the acquired data, making it often difficult or sometimes impossible to extract relevant information. Hence there is need for adaptive algorithms in the areas of data modelling and optimization. Using such data and design of a Deep Learning Network can provide a solution to farming, not only in detecting anomalies but also keep them prepared to handle effectively. This paper makes an attempt to bring an experimental outcome of selection of an efficient algorithm, tool and data to be used for land cover classification. A good spatial and temporal resolution remote sense data, open-source platform, deep learning techniques and different spectral indices recorded by a greater number of bands say hyper spectra range can be a boon to revolutionize farming and alleviate poverty line by improvement in sustainability factor. Satellite imagery converged with digital image processing can be a simple and cost-efficient solution for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's surface. © 2021 IEEE.

关键词： Landsat

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An Efficient Convolution Network with Adaptive receptive field feature for High-Resolution remote sensing Change Detection 22

An Efficient Convolution Network with Adaptive receptive fie...

引用

Proceedings of the 2022 4th International conference on Video, signal and image processing

作者： Dongling Xue Lin Bai Cheng Yao Xiangyuan Lin School of Electronics and Control Engineering Chang'an University 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.

关键词： convolution neural network (CNN) remote sensing (RS) images Change Detection

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YOLOv8-WRTNet: Instance Segmentation for Aerial images under Weak and Repetitive Texture Conditions

YOLOv8-WRTNet: Instance Segmentation for Aerial Images under...

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

作者： Zhenyu Zhang Xiaogang Yang Ruitao Lu Siyu Wang Zhengjie Zhu Bin Tang Missile Engineering Institute Rocket Force University of Engineering Xi’an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Aiming at the problem of insufficient accuracy of image detection and segmentation of traditional YOLOv8 methods in weak texture environments, this paper proposes an improved YOLOv8 method, YOLOv8-WRTNet, to enhance the accuracy and robustness of target detection segmentation. The algorithm firstly enhances the diversity and stability of feature expression by introducing an improved version of C2f module, which utilises feature separation, convolution processing and residual joining. Secondly, the BiFormer module is adopted to strengthen the model’s ability to capture and process multi-level features through dual-path feature fusion and multi-scale attention mechanism, which particularly enhances the target recognition ability in complex repetitive texture environments. Subsequently, combined with the FocalNext module, the model’s target detection and segmentation ability in complex backgrounds is enhanced through the integration of inflated depth convolution and jump connection, which effectively fuses fine-grained local information with coarse-grained global information. Finally, a custom Wise IoU loss function is designed and introduced to further improve the detection robustness of the model in complex scenes by dynamically adjusting the matching accuracy of the model to the target boundary. The experimental results show that the improved YOLOv8 method improves both accuracy and performance in the detection and segmentation tasks dealing with weak texture environments and complex backgrounds.

关键词： Instance segmentation Accuracy Attention mechanisms Convolution Target recognition Object detection Feature extraction Robustness Stability analysis remote sensing

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Range Imaging of Integrated Radar Communication signal Based on OFDM

Range Imaging of Integrated Radar Communication Signal Based...

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Information processing and Network Provisioning (ICIPNP), International conference on

作者： Zhivu Chen Jingcheng Zhao Haohui Hong Zhihua Chen Mohamed Cheriet Beihang University Beijing China Yunnan Innovation Institute • BUAA Kunming China Beijing Institute of remote sensing equipment Beijing China Southwest Technology and Engineering Research Institute Chongqing China École de Technologie Supérieure (ETS) University of Quebec Montreal QC Canada

First, based on the radar echo model of OFDM integrated radar communication signal, this article investigates the impact of target velocity and distance on the delay phase and Doppler frequency offset phase in radar echo signal by using the estimation method of Doppler frequency offset. Then, through formula derivation, the imaging principle of radar one-dimensional range image based on OFDM integrated radar communication signal is obtained, and MATLAB is used to simulate the multi-target radar one-dimensional range image of OFDM integrated signal. Finally numerical results verify that OFDM-based radar communication integrated signal can meet the design requirements of range resolution of 0.5m, and has more optimized resolution and imaging accuracy.

关键词： image resolution OFDM Imaging Radar imaging Radar signal processing Frequency estimation Doppler radar

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COUNTING DENSE OBJECTS IN remote sensing imageS

COUNTING DENSE OBJECTS IN REMOTE SENSING IMAGES

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

作者： Gao, Guangshuai Liu, Qingjie Wang, Yunhong Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou Peoples R China

ISBN: (纸本)9781509066315

Estimating accurate number of interested objects from a given image is a challenging yet important task. Significant efforts have been made to address this problem and achieve great progress, yet counting number of ground objects from remote sensing images is barely studied. In this paper, we are interested in counting dense objects from remote sensing images. Compared with object counting in natural scene, this task is challenging in following factors: large scale variation, complex cluttered background and orientation arbitrariness. More importantly, the scarcity of data severely limits the development of research in this field. To address these issues, we first construct a large-scale object counting dataset based on remote sensing images, which contains four kinds of objects: buildings, crowded ships in harbor, large-vehicles and small-vehicles in parking lot. We then benchmark the dataset by designing a novel neural network which can generate density map of an input image. The proposed network consists of three parts namely convolution block attention module (CBAM), scale pyramid module (SPM) and deformable convolution module (DCM). Experiments on the proposed dataset and comparisons with state of the art methods demonstrate the challenging of the proposed dataset, and superiority and effectiveness of our method.

关键词： Object counting remote sensing attention mechanism scale pyramid module deformable convolution

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2021 2nd International conference on signal processing and Computer Science, SPCS 2021

2021 2nd International Conference on Signal Processing and C...

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2021 2nd International conference on signal processing and Computer Science, SPCS 2021

The proceedings contain 68 papers. The topics discussed include: research on acoustic emission signal denoising based on autoencoder;application of photoplethysmography signal in diagnosis of cerebrovascular disease and its extraction method based on wavelet transform;prediction of polar vortex intensity signal based on convolution smoothing and long short-term memory;a methodology for removing biofouling of the hull based on ultrasonic guided waves;analysis on the applicability of high-resolution remote sensing images for highway construction;infrared image detection of insulators based on Centernet model;a new super-resolution restoration method with generated adversarial network for underground video images in coal;analysis and research on radar gesture micro-motion feature extraction method;an improved multi-target recognition method for vehicle linear frequency modulated continuous wave radar;and an optimal selection algorithm based on Q-learning for dual media communication system with full-duplex relays.

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Estimation of Free Parameters of Morphological Profiles for Building Extraction Using SAR images 7

Estimation of Free Parameters of Morphological Profiles for ...

引用

7th International conference on signal processing and Intelligent Systems, ICSPIS 2021

作者： Amjadipour, Fateme Ghassemian, Hassan Imani, Maryam Tarbiat Modares University Image Processing and Information Analysis Lab Tehran Iran

ISBN: (纸本)9781665409384

Nowadays, one of the most challenging issues in the field of remote sensing and satellite imagery is production of cadastral maps. Synthetic aperture radar (SAR) images are among the most widely used satellite images in the last decade. Due to radar nature of these images, the buildings in SAR images face with two problems: shadow and layover. Morphological mathematics are efficient tools for detection of buildings with providing a contextual profile containing shape and geometrical characteristics of the objects in radar images. By using the suggested method, two characteristics of shadow and brightness are detected separately. Then, the construction areas are extracted by using a fuzzy fusion approach. In this method, various parameters such as size and direction of the structural element and the weighting factor of the shadow, bright area, and the recursive parameter have to be determined independently. To this end, an iterative method using MSE is suggested. The experimental results show a detection rate of 94.3% achieved by the proposed method. © 2021 IEEE.

关键词： Satellite imagery

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Lightweight Convolutional Neural Network for High-Spatial-Resolution remote sensing Scenes Classification 12

Lightweight Convolutional Neural Network for High-Spatial-Re...

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12th International conference on Wireless Communications and signal processing (WCSP)

作者： Luo, Wang Yang, Wenqing Yu, Xian Wang, Ye Tan, Kai State Grid Elect Power Res Inst Co Ltd NARI Grp Co Ltd Nanjing 211000 Peoples R China Nanjing NARI Informat & Commun Technol Co Ltd Nanjing 211000 Peoples R China

ISBN: (纸本)9781728172361

High spatial resolution remote sensing (HSRRS) image classification and detection are playing an increasingly important role in land planning and resource utilization. At present, convolutional neural networks (CNN) have made great achievements in computer vision and natural language processing. CNN is also widely used in various tasks such as HSRRS image classification and detection. However, existing neural networks generally have the redundant parameters and the slow computation speeds, which are inconvenient to implement them into the mobile terminals. To solve this problem, in this paper, we propose a lightweight CNN model, which is named as the MobileNet. Simulation results are given to confirm that MobileNet can greatly reduce the parameters and accelerate the computation while ensuring high accuracy.

关键词： Convolutional neural network (CNN) lightweight high spatial resolution remote sensing classification MobileNet

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