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Multi-class Object Detection Model in Satellite Images Using Convolutional Neural Network

Ibrahim Goni, Asabe Sandra Ahmadu, Yusuf Musa Malgwi
2021 Communications  
The aim of this research work was to design a robust multi-class object detection model in satellite images using image processing techniques and convolutional neural network with a particular concern  ...  on image preprocessing, image denoising and image enhancement to enable address the issue of noise in satellite images.  ...  [5] proposed a dense local feature compression network for extraction of water body in high resolution remote sensing images and they also constructed a datasets with Gaofen-2 (GF-2) satellite images  ... 
doi:10.11648/ fatcat:yh3bwgli7vd6bnbrkmp5a7yx4u

First Earth-Imaging CubeSat with Harmonic Diffractive Lens

Nikolay Ivliev, Viktoria Evdokimova, Vladimir Podlipnov, Maxim Petrov, Sofiya Ganchevskaya, Ivan Tkachenko, Dmitry Abrameshin, Yuri Yuzifovich, Artem Nikonorov, Roman Skidanov, Nikolay Kazanskiy, Victor Soifer
2022 Remote Sensing  
alloy; and the on-Earth image post-processing with a convolutional neural network resulting in images comparable in quality to classical refractive optics used for remote sensing before.  ...  We describe the CubeSat platform we used; our 10 mm diameter and 70 mm focal length lens synthesis, design, and manufacturing; a custom 3D-printed camera housing built from a zero-thermal-expansion metal  ...  Level of gain impact on the image quality: (a) gain = 4; (b) gain = 18; (c) a neural network reconstruction of the image with a gain of 4; (d) a neural network reconstruction of the image with a gain of  ... 
doi:10.3390/rs14092230 fatcat:aptdovaqojbkhkws46l247ga4q

Image Enlargement Using Multiple Sensors

Wei Wu, Marco Anisetti, Chehri Abdellah, Gwanggil Jeon
2016 Journal of Sensors  
. 61271330 and no. 6141101009).  ...  Acknowledgments We would like to express our appreciation to all the authors for their informative contributions and the reviewers for their support and constructive critiques in making this special issue  ...  Jeong "Efficient Deep Neural Network for Digital Image Compression Employing Rectified Linear Neurons" proposed a compression technique for still digital images which uses deep neural networks (DNNs) and  ... 
doi:10.1155/2016/1498796 fatcat:2d55j4emejfbhhloh6jphdomny

Functional Link Artificial Neural Network for Denoising of Image

Charu Pandey
2013 IOSR Journal of Electronics and Communication Engineering  
Digital image denoising is crucial part of image preprocessing. The application of denoising procesn satellite image data and also in television broadcasting.  ...  In the presence of additive white Gaussian noise, salt and pepper noise, Random variable impulse noise and mixed noise in the image the performance of the proposed network is compared with that of MLP  ...  Figure 1 : 1 Multilayer perceptron neural network architecture Figure 2 : 2 Structure of FLANN with a single output Functional Link Artificial Neural Network for Denoising of Image a) Learning with the  ... 
doi:10.9790/2834-046109115 fatcat:5xbwhve7xjfx3i7bff2cyu2bue


F. Chouteau, L. Gabet, R. Fraisse, T. Bonfort, B. Harnoufi, V. Greiner, M. Le Goff, M. Ortner, V. Paveau
2022 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Recent advances leverage Convolutional Neural Networks (CNNs) to improve the image restoration quality (K.  ...  To further enhance the quality of the images, the in-space imaging system is usually complemented by on-ground image restoration processing, such as deconvolution and denoising (Latry et al., 2012).  ...  and denoising properties of a given network.  ... 
doi:10.5194/isprs-archives-xliii-b1-2022-9-2022 fatcat:7khrzzqlj5hq5nqreurpfgmqx4

Review of Improvement of Echo Cardiography Image based on Hybrid Technique in Data Mining

Gagan Sharma
2019 International Journal for Research in Applied Science and Engineering Technology  
In this dissertatione a hybrid of improvement method based on wavelet transform function and neural networks is proposed.  ...  The experimental results show the mean with the traditional enhancement methods, in this technique threshold-based enhancement digital image enhancement algorithm for mixed digital image enhancement is  ...  But some related work in the field of image compression in concern of wavelet and neural network, discuss by the name of authors and their respective title.  ... 
doi:10.22214/ijraset.2019.6128 fatcat:4ogvjmnclzf6hksfo22bes2tke

Hybrid Algorithm Based on Hyper Spectral Noise Removal for Satellite Image

N. Rajini
2018 EAI Endorsed Transactions on Energy Web  
Teng, D. (2008) Efficient hardware implementation of an image compressor for wireless capsule endoscopy applications, Proceedings of IEEE International Joint Conference Neural Networks. pp. 2761-2765.  ...  The proposed algorithms utilize the advantages of both DCT and DWT transformation effectively for denoising the images obtained from satellite.  ... 
doi:10.4108/eai.13-7-2018.163843 fatcat:gmmmckkn5bg5peero4736imexm

Table of contents

2021 IEEE Transactions on Geoscience and Remote Sensing  
Zhang 3444 Remote-Sensing Image Scene Classification With Deep Neural Networks in JPEG 2000 Compressed Domain ....... .................................................................... A.  ...  Surface and Subsurface Properties Detecting Ground Deformation in the Built Environment Using Sparse Satellite InSAR Data With a Convolutional Neural Network ...........................................  ... 
doi:10.1109/tgrs.2021.3063896 fatcat:miqrb4or7jbujhr2aqlvhxq3oi

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 5900-5911 Efficient and Fast Real-World Noisy Image Denoising by Combining Pyramid Neural Network and Two-Pathway Unscented Kalman Filter.  ...  ., +, TIP 2020 9204-9219 Cellular neural networks Hyperspectral Image Denoising via Matrix Factorization and Deep Prior Regularization.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

Hyperspectral Image Denoising With Dual Deep CNN

Wei Shan, Peng Liu, Lin Mu, Caihong Cao, Guojin He
2019 IEEE Access  
INDEX TERMS Hyperspectral image denoising, deep dual neural network, feature learning, activation function.  ...  A new hyperspectral image denoising algorithm, called the dual deep convolutional neural network (DD-CNN), is proposed in this paper.  ...  (fast and flexible denoising convolutional neural network) FFDNet [31] directly trains the denoised clear image with a neural network, which inputs the noise level map and the noise image in the network  ... 
doi:10.1109/access.2019.2955810 fatcat:37dbksms5bembehvln6mph6mqi

Front Matter: Volume 10427

Lorenzo Bruzzone, Francesca Bovolo, Jon Atli Benediktsson
2018 Image and Signal Processing for Remote Sensing XXIII  
.  The last two digits indicate publication order within the volume using a Base 36 numbering system employing both numerals and letters. These two-number sets start with 00, 01, 02, 03,  ...  Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication.  ...  23] 10427 0O Target detection with compressive sensing hyperspectral images [10427-52] SESSION 6 ESTIMATION AND MODELLING TECHNIQUES 10427 0P Convolutional neural networks for estimating spatially-distributed  ... 
doi:10.1117/12.2303933 fatcat:jvysug3k7jfn7a23bcbgwerg4e

A Generalized Deep Learning Model for Denoising Image Datasets

2020 International Journal of Engineering and Advanced Technology  
In this work, evaluation of the performance of convolutional neural network (CNN) against existing image denoising algorithms has been successfully executed .  ...  Utilization of Convolutional neural networks designed based on the dataset requirements along with the noise removal filter can yield better results.  ...  Typically a noisy medical imaging can result in inaccurate diagnosis of the patient. Without proper denoising of satellite images, crucial geographical information can be lost.  ... 
doi:10.35940/ijeat.f1555.1010120 fatcat:lcguwgolxjgyxiq5lkcml3ed2e

2019 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 57

2019 IEEE Transactions on Geoscience and Remote Sensing  
and Hanssen, R.F., Incorporating Temporary Coherent Li, X., Yeo, T.S., Yang, Y., Chi, C., Zuo, F., Hu, X., and Pi, Y., Refo-cusing and Zoom-In Polar Format Algorithm for Curvilinear Spotlight SAR Imaging  ...  Hu, C., Zhang, B., Dong, X., and Li, Y., Geosynchronous SAR Tomography: Theory and First Experimental Verification Using Beidou IGSO Satellite; TGRS Sept. 2019 6591-6607 Hu, F., Wu, J., Chang, L.,  ...  Atrous Convolution Neural Network for Hyperspectral Image Denoising.  ... 
doi:10.1109/tgrs.2020.2967201 fatcat:kpfxoidv5bgcfo36zfsnxe4aj4

Table of contents

2021 IEEE Transactions on Geoscience and Remote Sensing  
Liang, and M. Ao 7051 Geophysical Data Generative Adversarial Network for Desert Seismic Data Denoising ..................... H. Wang, Y. Li, and X.  ...  Chen 6510 Mapping Subsurface Utility Pipes by 3-D Convolutional Neural Network and Kirchhoff Migration Using GPR Images ........................................................................Miscellaneous  ... 
doi:10.1109/tgrs.2021.3090240 fatcat:kbcbgsnrv5fuzbozvjynhj3c3y

Data-driven geophysics: from dictionary learning to deep learning [article]

Siwei Yu, Jianwei Ma
2020 arXiv   pre-print
Artificial intelligence applications on geoscience involving deep Earth, earthquake, water resource, atmospheric science, satellite remoe sensing and space sciences are also reviewed.  ...  "Data-driven" techniques may overcome these issues with increasingly available geophysical data.  ...  Acknowledgments The work was supported in part by the National Key Research and Development Program Data Availability Statement Data were not used, nor created for this research.  ... 
arXiv:2007.06183v2 fatcat:ow45ejo7izbkpmssedwd74rbym
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