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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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image based Land Cover Classification for remote sensing Applications-A review 3

Image based Land Cover Classification for Remote Sensing App...

引用

3rd International conference on signal processing and Communication (ICPSC)

作者： Manikandan, Mishaa M. Andrushia, A. Diana Neebha, T. Mary Karunya Inst Technol & Sci Dept Elect & Commun Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9781665428644

Land cover is a material present in the earth surface. It consists of forest, farming land, fertile, infertile lands, desert, water bodies and agricultural areas etc., The basic need for capturing the information of the land cover is to classify the land resources according to their soil, water, climatic condition and to utilize it in the proper manner. Land use gives the knowledge about the uses and the benefits of the land used for man like mining, industrial and cultivation land. The aim of this article is to perform a detailed review of image-based land cover classification with respect to remote sensing applications.

关键词： land cover classification remote sensing image processing urban development

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Research on Target Recognition Based on Deep Feature Fusion of Infrared and Visible Light images

Research on Target Recognition Based on Deep Feature Fusion ...

引用

signal processing, Communications and Engineering Management conference (ISPCEM), International

作者： Jiahao Wang Juan Ren Jianjie Wang Xiuping Wang Department of Electronic and Information Engineering Zhejiang Gongshang University Hangzhou Zhejiang China Hangzhou Normal University Zhejiang Hangzhou China Xi'an University of Electronic Science and Technology Xi’an China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

It is expected that unmanned aerial vehicle remote sensing target recognition will have a broad range of applications in fields such as smart cities, traffic monitoring, and disaster monitoring. However, remote sensing images have relatively small targets, and they are also affected by factors such as weather, lighting, and occlusion. This makes target detection and recognition difficult. In order to improve the accuracy of target recognition, this paper proposes a dual modal image depth feature fusion method for recognizing unmanned aerial vehicle remote sensor images. The method includes dual modal image registration, and a dual modal image depth feature fusion method based on the yolov7 model. By constructing a bimodal dataset and using the HOPC operator for image registration, strict registration was achieved with manual annotation assistance. The yolov7-dual model proposed in this article, through pixel level fusion and CAFM attention mechanism, ultimately increased the mAP value from 0.424 to 0.657, significantly exceeding expectations.

关键词： image registration Target recognition Smart cities Lighting Object detection signal processing Autonomous aerial vehicles remote sensing Monitoring Meteorology

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Wavelet-Based Data Compression for remote sensing and image processing

Wavelet-Based Data Compression for Remote Sensing and Image ...

引用

Optimization Computing and Wireless Communication (ICOCWC), International conference on

作者： Dhyan Chandra Yadav Atul Dadhich Ananya Saha Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electrical Engineering Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

Wavelet-based totally records compression is a form of information compression used to method far flung sensing and picture processing. This technique makes use of wavelets, that are mathematical features that divide a signal into separate frequency components, and gift the signal as a sum of the components. Wavelet-primarily based compression permits for compression of statistics into a smaller, extra efficient form without sizeable lack of best. furthermore, it could additionally produce precise reconstructions of the original sign or image, making it a feasible tool for remote sensing and image processing. This approach is utilized in a selection of applications including medical imaging, television transmission, satellite tv for pc imagery and far flung sensing. via utilising wavelets, more efficient and special records may be received. moreover, due to its low time complexity, wavelet-based compression is ideal for processing big quantities of data speedy and efficaciously.

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Unrolling Alternating Direction Method of Multipliers for Visible and Infrared image Fusion 5

Unrolling Alternating Direction Method of Multipliers for Vi...

引用

5th IEEE International conference on image processing Applications and Systems (IPAS)

作者： Bakan, Altug Erer, Isin Istanbul Tech Univ Dept Elect & Commun Engn Istanbul Turkey ASELSAN Microelect Guidance & Electroopt Business Sect Ankara Turkey

ISBN: (纸本)9781665462198

In this paper a new infrared and visible image fusion (IVIF) method which combines the advantages of optimization and deep learning based methods is proposed. This model takes the iterative solution used by the alternating direction method of the multiplier (ADMM) optimization method, and uses algorithm unrolling to obtain a high performance and efficient algorithm. Compared with traditional optimization methods, this model generates fusion with 99.6% improvement in terms of image fusion time, and compared with deep learning based algorithms, this model generates detailed fusion images with 99.1% improvement in terms of training time. Compared with the other state-of-the-art unrolling based methods, this model performs 26.7% better on average in terms of Average Gradient (AG), Cross Entropy (CE), Mutual Information (MI), Peak signal-to-Noise Ratio (PSNR), and Structural Similarity Loss (SSIM) metrics with a minimal testing time cost.

关键词： visible and infrared image fusion algorithm unrolling model based deep network ADMM

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Multiple Endmember Extraction Using Spatial-Spectral Information

Multiple Endmember Extraction Using Spatial-Spectral Informa...

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

作者： Nan Xu Huadong Yang Juntong Wang School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China School of Information Science and Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

Spectral variability often occurs in hyperspectral remote sensing scenes. The traditional hyperspectral endmember extraction method ignores the influence of spectral variability, resulting in low accuracy of mixed pixel decomposition. In this work, a multiple endmember extraction algorithm based on spatial-spectral information is proposed for hyperspectral image(HSI). Firstly, the original hyperspectral image is divided into non-overlapping image blocks, and the traditional single endmember extraction algorithm is used to extract candidate endmembers on the image blocks. Secondly, the spectral similarity analysis is performed on the neighborhood pixels of the candidate endmembers to refine candidate endmembers. Finally, the clustering algorithm is used to cluster all endmember sets to obtain the final multiple endmember set. The experimental results show that the proposed algorithm has the ability to characterize the spectral variability in remote sensing images and can effectively improve the accuracy of mixed pixel decomposition.

关键词： image segmentation Accuracy image analysis signal processing algorithms Clustering algorithms signal processing Feature extraction Data mining Hyperspectral imaging Information analysis

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Improved GMM High-Resolution remote sensing image Land Cover Classification

Improved GMM High-Resolution Remote Sensing Image Land Cover...

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signal processing, Communications and Engineering Management conference (ISPCEM), International

作者： Wang Chunyan Fu Kaixin Wang Xiang School of Software Liaoning Technical University Huludao China Department of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

To address the increased complexity of surface cover, increased heterogeneity within homogeneous regions, and increased similarity between different regions in high-resolution remote sensing images, which lead to increased classification difficulty, a supervised learning method based on a double neighborhood relationship Gaussian Regression Mixture Model (GRMM) is proposed. Firstly, supervised sampling of image regions is conducted, fitting histograms using the least squares method to establish Gaussian Mixture Models (GMM) for each land cover to represent the complex gray-scale features of land cover. Secondly, local spatial information of adjacent pixels is introduced into the image gray-scale space to construct a Gaussian Regression Model (GRM). Finally, in the membership degree space, the neighborhood relationship is processed again to achieve classification decision. The kappa coefficients of GRMM on synthetic images and real high-resolution remote sensing images respectively reaches 97.2% and 98.5%. Compared to existing mainstream models, GRMM demonstrates strong classification efficiency, noise reduction capability, and generalization ability, with clear classification boundaries, effectively improved the land cover classification capability of remote sensing images.

关键词： Adaptation models Accuracy Noise Supervised learning Land surface Mixture models Gray-scale Surface fitting remote sensing signal resolution

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Proximal Femur Segmentation from the Fluoroscopy image Using DeeplabV3+ Model 2

Proximal Femur Segmentation from the Fluoroscopy Image Using...

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

作者： Naik, Niva Mehta, Mayuri A. Joshi, Vivek Sardar Vallabhbhai National Institute of Technology Computer Science and Engineering Surat India Sarvajanik College of Engineering and Technology Computer Engineering Department Surat India R.N.G.Patel Institute of Technology Information Technology Bardoli India

ISBN: (纸本)9798350366570

Segmenting the proximal femur from a fluoroscopy image is crucial for precise surgical treatment of proximal femur fracture. Accurate segmentation of the proximal femur helps the surgeon to insert screws and metal pins correctly during the treatment of the proximal femur fracture. Traditional machine learning-based segmentation methods have limitations in precisely capturing the complex and variable anatomy of the proximal femur. Deep learning-based segmentation methods offer promising solutions to overcome the limitations of machine learning-based methods. However, they struggle with problems like hyperparameter tuning and spatial context handling. Hence, this paper proposes a novel proximal femur segmentation approach utilizing encoder-decoder-based architecture. The proposed segmentation approach has been evaluated utilizing three encoder-decoder-based architectures, namely U-net, U-net++, and DeeplabV3+. Its performance is assessed using four metrics such as dice coefficient, Intersection over Union (IoU), precision, and recall. Among the three encoder-decoder-based architectures, DeeplabV3+ outperforms the U-net and U-net++. DeeplabV3+ yields a Dice coefficient of 0.9566 and an IoU of 0.9171 on the training dataset, surpassing the performance of U-net and U-net++. © 2024 IEEE.

关键词： Deep learning

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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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A METHOD FOR VELOCITY ESTIMATION OF MOVING TARGETS BASED ON MULTI-ANGLE SAR image SEQUENCES

A METHOD FOR VELOCITY ESTIMATION OF MOVING TARGETS BASED ON ...

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IET International Radar conference 2023, IRC 2023

作者： Wang, Zhibin Zhang, Qingjun Lv, Zheng Liu, Jiuli Zhou, Yashi Yang, Taoli Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781839539954

Multi-angle Synthetic Aperture Radar (SAR) is a new SAR working mode to acquire multi-dimensional information of target. By observing the target area from different observation angles, the multi-angle SAR has the ability to acquire moving target information. This paper constructs a multi-angle SAR moving target observation signal model, derives a method for estimating the speed of a moving target based on the SAR image acquired from forward and backward observation, and obtains the target azimuthal and radial speeds using this method. The validity of this method is verified through simulation 濸瀋瀃濸瀅濼瀀濸瀁瀇, and it can be used to accurately estimate the speed of moving targets. © The Institution of Engineering & Technology 2023.

关键词： Radar target recognition

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A deep learning approach based on morphological profiles for Hyperspectral image unmixing 6

A deep learning approach based on morphological profiles for...

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

作者： Ayed, Mariem Hanachi, Refka Sellami, Akrem Farah, Imed Riadh Mura, Mauro Dalla University of Manouba Riadi Laboratory Manouba2010 Tunisia Loria Laboratory Campus Scientifique Vandoeuvre lès Nancy F54506 France Univ. Grenoble Alpes CNRS Grenoble Inp GIPSA-Lab Grenoble38000 France

ISBN: (纸本)9781665451161

Hyperspectral image (HSI) unmixing is a critical problem in remote sensing image processing. It aims to estimate the pure spectral signatures and their fractional abundances in each HSI pixel. Generally, most spectral-spatial unmixing methods do not exploit the contextual structure of HSI data cube. Therefore, in order to fully utilize the spatial information, we propose a new approach for spectral-spatial unmixing based on deep learning and Extended Morphological Profiles (EMPs). First, a latent representation from the original HSI data (spectral features) and the morphological profiles (spatial features) is obtained through the AutoEncoder (AE) network. Then, the estimation of both endmembers and abundance maps is achieved by the Convolutional AE (CAE). Experiments are performed using two HSI benchmarks which are Jasper Ridge and Samson. Compared to a spectral-spatial unmixing method, our proposed method shows significantly higher performance. © 2022 IEEE.

关键词： Deep learning

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