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International Conference on Computer Vision and Machine Intelligence, CVMI 2022

作者： Gupta, Shubham Uma, D. Hebbar, R. PES University KA Bangaluru560085 India Regional Remote Sensing Centre–South ISRO KA Bengaluru560037 India

ISBN: (纸本)9789811978661

Monitoring water is a complex task due to its dynamic nature, added pollutants, and land build-up. The availability of high-resolution data by Sentinel-2 multispectral products makes implementing remote sensing applications feasible. However, overutilizing or underutilizing multispectral bands of the product can lead to inferior performance. In this work, we compare the performances of ten out of the thirteen bands available in a Sentinel-2 product for water segmentation using eight machine learning algorithms. We find that the shortwave-infrared bands (B11 and B12) are the most superior for segmenting water bodies. B11 achieves an overall accuracy of 71 % while B12 achieves 69 % across all algorithms on the test site. We also find that the Support Vector Machine (SVM) algorithm is the most favorable for single-band water segmentation. The SVM achieves an overall accuracy of 69 % across the tested bands over the given test site. Finally, to demonstrate the effectiveness of choosing the right amount of data, we use only B11 reflectance data to train an artificial neural network, BandNet. Even with a basic architecture, BandNet is proportionate to known architectures for semantic and water segmentation, achieving a 92.47 mIOU on the test site. BandNet requires only a fraction of the time and resources to train and run inference, making it suitable to be deployed on web applications to run and monitor water bodies in localized regions. Our codebase is available at https://***/IamShubhamGupto/BandNet. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural networks

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SAR-Optical image Matching Model Based on Contrastive Learning 20

SAR-Optical Image Matching Model Based on Contrastive Learni...

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20th International Computer Conference on Wavelet Active Media Technology and Information processing, ICCWAMTIP 2023

作者： Yaoting, Xiong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350318982

Due to the strong complementary nature of synthetic aperture radar (SAR) and optical imagery in remote sensing, matching SAR images with optical images enables the complementary extraction of ground surface feature information. This approach is beneficial for addressing tasks such as change detection, target recognition, and land cover classification. With the development of deep learning, most current models for SAR-optical image matching utilize siamese neural networks and have achieved good matching performance. However, in practical applications, test data is often unknown, and there may be differences between training and test data. A matching model that performs well on the training dataset may not generalize well to the test data. Domain generalization research for heterogeneous image matching aims to improve the generalization performance of matching models on unknown test domains, and this task has practical significance and application prospects. In this paper, we propose a contrastive learning-based SAR-optical image matching model specifically designed to enhance the generalization performance of matching models trained on multiple datasets. When dealing with cross-domain scenarios, enforcing feature consistency of matching points preserves more information conducive to matching. We propose a contrastive learning method to limit the feature consistency of matching points, thereby improving the generalization performance of cross-source image matching models. © 2023 IEEE.

关键词： image matching

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Fractional Fourier-Based Frequency-Spatial-Spectral Prototype Network for Agricultural Hyperspectral image Open-Set Classification

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2024年 62卷 1页

作者： Chen, Maoyang Feng, Shou Zhao, Chunhui Qu, Bo Su, Nan Li, Wei Tao, Ran Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Harbin Engn Univ Key Lab Adv Marine Commun & Informat Technol Minist Ind & Informat Technol Harbin 150001 Peoples R China Shaanxi Key Lab Opt Remote Sensing & Intelligent I Xian 710119 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Chinese Acad Sci Xian Inst Opt & Precis Mech Key Lab Spectral Imaging Technol CAS Xian 710119 Shaanxi Peoples R China Xi An Jiao Tong Univ Key Lab Intelligent Networks & Network Secur Minist Educ Xian 710049 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China

At present, hyperspectral image classification (HSIC) technology has been warmly concerned in all walks of life, especially in agriculture. However, existing classification methods operate under the closed-set assumption, which deviates from the real world with open properties. At the same time, there are more serious phenomena of different crops with similar spectrums and the same crops with different spectrum in agricultural hyperspectral data, which is also a great challenge to existing methods. In this work, a fractional Fourier-based frequency-spatial-spectral prototype network (FrFSSPN) is proposed to address the challenges of open-set HSIC in agricultural scenarios. First, fractional Fourier transform (FrFT) is introduced into the network to combine the information in the frequency domain with the spatial-spectral information, so as to expand the difference between different classes on the premise of ensuring the similarity between classes. Then, the prototype learning strategy is introduced into the network to improve the feature recognition capability of the network through prototype loss. Finally, in order to break the stubbornly closed-set property of the closed-set classification (CSC) method, the open-set recognition module is proposed. The difference between the prototype vector and the feature vector is used to judge the unknown class. Experiments on three agricultural hyperspectral datasets show that this method can effectively identify unknown classes without sacrificing the classification accuracy of closed-set, and has satisfactory classification performance.

关键词： Agricultural hyperspectral image classification (HSIC) fractional Fourier transform (FrFT) open-set classification (OSC) prototype learning

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DEEP ENSEMBLE LEARNING MODEL BASED ON COVARIANCE POOLING OF MULTI-LAYER CNN FEATURES 29

DEEP ENSEMBLE LEARNING MODEL BASED ON COVARIANCE POOLING OF ...

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IEEE International Conference on image processing (ICIP)

作者： Akodad, Sara Bombrun, Lionel Puscasu, Maria Xia, Junshi Germain, Christian Berthoumieu, Yannick Univ Bordeaux CNRS IMS UMR 5218Grp Signal & Image F-33405 Talence France Ctr Natl Etud Spatiales CNES 18 Ave Edouard Belin F-31400 Toulouse France RIKEN RIKEN Ctr Adv Intelligence Project AIP Tokyo 1030027 Japan

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Compared to standard deep convolutional neural networks (CNN) which include a global average pooling operator, second-order neural networks have a global covariance pooling operator which allows to capture richer statistics of CNN features. They have been shown to improve representation and generalization abilities. However, this covariance pooling is performed only on the deepest CNN feature maps. To benefit from different levels of abstraction, we propose to extend these models by using a multi-layer approach. In addition, to obtain better predictive performance, an end-to-end ensemble learning architecture is proposed. Experiments are conducted on four datasets and have confirmed the potential of the proposed model for various image processing applications such as remote sensing scene classification, indoor scene recognition and texture classification.

关键词： Covariance pooling multi-layer representation ensemble learning CNN

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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 ...

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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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A Semantic Segmentation Method of Buildings in remote sensing image Based on Improved UNet 2

A Semantic Segmentation Method of Buildings in Remote Sensin...

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2nd International Conference on Signal image processing and Communication, ICSIPC 2022

作者： Li, Zhongyu Liu, Yang Kuang, Yin Wang, Huajun Liu, Cheng College of Computer Science Chengdu Normal University Chengdu611130 China College of Geophysics Chengdu University of Technology Chengdu610059 China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Chengdu610106 China Artificial Intelligence Key Laboratory of Sichuan Province Zigong643000 China Key Laboratory of interior Layout optimization and Security Institutions of Higher Education of Sichuan Province Chengdu Normal University Sichuan Chengdu611130 China College of Movie and Media Sichuan Normal University Chengdu610066 China

ISBN: (纸本)9781510657694

Aiming at the problem of model instability and overfitting of deep neural networks with the deepening of the number of network layers, the current mainstream method is to use batch normalization (BN) to alleviate them. However, since the BN method is more sensitive to batch size when the batch size is small, the model performance will be poor. For a relatively large model, due to the limitation of video memory, the batch size cannot take a large value, limiting the model's performance. Because of the dependence of BN on batch size, this paper adopts group normalization (GN) instead of batch normalization (BN) in the UNet network to alleviate the impact of the model on batch size. Then experiments are carried out on the WHUBuilding dataset. The experimental results show that the improved model (UNet-GN) improves the mean intersection over union (MIoU) and mean pixel accuracy (MPA) by 10.66% and 1.65% respectively compared with the original model (UNet-BN). © 2022 SPIE.

关键词： Deep neural networks

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Deblurring of Beamformed images in the Ocean Acoustic Waveguide Using Deep Learning-Based Deconvolution

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remote sensing 2024年第13期16卷 2411页

作者： Zha, Zijie Yan, Xi Ping, Xiaobin Wang, Shilong Wang, Delin Zhejiang Univ Ocean Coll Zhoushan 316021 Peoples R China Key Lab Ocean Observat Imaging Testbed Zhejiang Pr Zhoushan 316000 Peoples R China

A horizontal towed linear coherent hydrophone array is often employed to estimate the spatial intensity distribution of incident plane waves scattered from the geological and biological features in an ocean acoustic waveguide using conventional beamforming. However, due to the physical limitations of the array aperture, the spatial resolution after conventional beamforming is often limited by the fat main lobe and the high sidelobes. Here, we propose a method originated from computer vision deblurring based on deep learning to enhance the spatial resolution of beamformed images. The effect of image blurring after conventional beamforming can be considered a convolution of beam pattern, which acts as a point spread function (PSF), and the original spatial intensity distributions of incident plane waves. A modified U-Net-like network is trained on a simulated dataset. The instantaneous acoustic complex amplitude is assumed following circular complex Gaussian random (CCGR) statistics. Both synthetic data and experimental data collected from the South China Sea Experiment in 2021 are used to illustrate the effectiveness of this approach, showing a maximum 700% reduction in a 3 dB width over conventional beamforming. A lower normalized mean square error (NMSE) is provided compared with other deconvolution-based algorithms, such as the Richardson-Lucy algorithm and the approximate likelihood model-based deconvolution algorithm. The method is applicable in various acoustic imaging applications that employ linear coherent hydrophone arrays with one-dimensional conventional beamforming, such as ocean acoustic waveguide remote sensing (OAWRS).

关键词： acoustic remote sensing deconvolution conventional beamforming image processing ocean acoustic waveguide remote sensing

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Prediction of Fish Location by Combining Fisheries Data and Sea Bottom Temperature Forecasting 21st

Prediction of Fish Location by Combining Fisheries Data and ...

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21st International Conference on image Analysis and processing (ICIAP)

作者： Ospici, Matthieu Sys, Klaas Guegan-Marat, Sophie Atos CVLab F-38130 Echirolles France Flanders Res Inst Agr Fisheries & Food Anim Sci Unit Ankerstr 1 B-8400 Oostende Belgium

ISBN: (纸本)9783031064333;9783031064326

This paper combines fisheries dependent data and environmental data to be used in a machine learning pipeline to predict the spatiotemporal abundance of two species (plaice and sole) commonly caught by the Belgian fishery in the North Sea. By combining fisheries related features with environmental data, sea bottom temperature derived from remote sensing, a higher accuracy can be achieved. In a forecast setting, the predictive accuracy is further improved by predicting, using a recurrent deep neural network, the sea bottom temperature up to four days in advance instead of relying on the last previous temperature measurement.

关键词： Computer vision Machine learning Spatiotemporal modelling Fisheries remote sensing data

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remote sensing image Object Detection Method with Feature Denoising Fusion Module

Remote Sensing Image Object Detection Method with Feature De...

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IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Penghui Chen Qishen Li Qiufeng Li Zhongyu Wu School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China School of Software Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Nondestructive Testing (Ministry of Education) Nanchang Hangkong University Nanchang Jiangxi China

remote sensing object detection is an important research area in computer vision, widely applied in both military and civilian domains. However, challenges in remote sensing image object detection such as large image sizes, complex backgrounds, and significant variations in target scales are prevalent. To address these issues, this paper proposes a new Feature Denoising and Fusion Module (FDFM) aimed at enhancing the accuracy and robustness of object detection. This module comprises a Multi-Scale Denoising Submodule(MDS) and an Attention Optimization Submodule(AOS). The Multi-Scale Denoising Module aims to suppress lower-level texture noise by utilizing higher-level semantic features before the fusion process, reducing the impact of lower-level noise on subsequent multi-scale feature fusion. Meanwhile, the Attention Optimization Module seeks to enhance the precision of self-attention computations within the Multi-Scale Denoising Module without increasing the parameter count. The efficacy of this method was evaluated on public datasets DOTA, VisDrone, VOC and COCO, showing improvements in comparison to baseline models.

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Industrial objects recognition in intelligent manufacturing for computer vision

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INTERNATIONAL JOURNAL OF INTELLIGENT UNMANNED SYSTEMS 2022年第4期10卷 401-415页

作者： Jain, Tushar Meerut Inst Engn & Technol Mech Engn Meerut Uttar Pradesh India Natl Inst Technol Kurukshetra Kurukshetra Haryana India

Purpose The overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are custom-designed systems, which can only handle a specific application. This is not surprising, since different applications have different geometry, different reflectance properties of the parts. Design/methodology/approach Computer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. Object recognition is a type of pattern recognition. Object recognition is widely used in the manufacturing industry for the purpose of inspection. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing and nanotechnology to multimedia databases. In this work, recognition of objects manufactured in mechanical industry is considered. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such mechanical part. Red, green and blue RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial neural network (ANN) is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects as well as the effect of learning rate and momentum. Findings One important finding is that there is not any considerable change in the network performances after 500 iterations. It has been found that

关键词： Industrial objects recognition Fuzzy image classification Generic fourier descriptor Artificial neural network Intelligent manufacturing Robot vision

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