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Study on denoising of deep neural networks in spatial heterodyne interferometric spectral information 15

Study on denoising of deep neural networks in spatial hetero...

引用

15th International conference on Information Optics and Photonics, CIOP 2024

作者： Luo, Wei Ye, Song Zhang, Ziyang Dong, Baijun School of Optoelectronic Engineering Guilin University of Electronic Technology Guangxi Guilin541004 China Guangxi Key Laboratory of Optoelectronic Information Processing Guangxi Guilin541004 China

ISBN: (数字)9781510686175

ISBN: (纸本)9781510686168

Spatial heterodyne spectroscopy is a new type of spatially modulated Fourier interference spectroscopy technology that can achieve ultra-high spectral resolution within a determined spectral range. It has unique advantages in atmospheric remote sensing, astronomical observation, and ore detection. Due to the complex detection environment and interference from electronic components themselves, spatial heterodyne spectrometers may also be affected by noise during the detection process. With the continuous improvement of signal detection accuracy, there is an urgent need for new methods to reduce the impact of noise on the spectral information contained in spatial heterodyne interferograms. In recent years, deep neural networks have experienced rapid development and achieved good results in regression prediction of micro nano devices and image denoising. Therefore, we prepared a training set using the principle of spatial heterodyne spectroscopy to train the constructed deep neural network, and then used the trained network model to directly output the denoised spatial heterodyne spectra from the spatial heterodyne interferogram containing Gaussian noise. The results show that deep neural networks have excellent ability to extract denoised spatial heterodyne spectra from noise spatial heterodyne interferograms. This study provides a new and effective solution for denoising spatial heterodyne interferometric spectral information. © 2024 SPIE.

关键词： Spectral resolution

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Correction Method for Digital Beamforming in the Range-Doppler Domain for Tandem Bistatic SAR

Correction Method for Digital Beamforming in the Range-Doppl...

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

作者： Huancheng Guo Jiahan Lou Zhibin Wang Qi Zhang Zhengfang Li Qingjun Zhang National Key Laboratory of Radar Signal Processing Xidian University Xi’an China Institute of Information Sensing Xidian University Xi’an China Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

This paper investigates a correction method for digital beamforming (DBF) based on tandem bistatic Synthetic Aperture Radar (SAR). Due to its unique geometric configuration and electromagnetic characteristics, the tandem bistatic SAR system offers significant advantages in enhancing resolution and target recognition capabilities. However, traditional DBF techniques encounter performance limitations when processing elevation-direction signals in tandem bistatic SAR systems. To address this issue, the paper first analyzes the imaging principles and characteristics of the tandem bistatic SAR system, and then establishes a linear approximation model for the relationship between point target azimuth angles and transmitter-receiver distances, providing a theoretical foundation for DBF correction. A range-Doppler domain DBF processing method is introduced to enhance elevation-direction signal processing performance. Simulation experiments confirm the effectiveness of the proposed approach.

关键词： Analytical models image resolution Array signal processing Azimuth Target recognition Imaging Linear approximation Radar imaging Synthetic aperture radar signal resolution

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MDDNet: Multilevel Difference-Enhanced Denoise Network for Unsupervised Change Detection in SAR images

MDDNet: Multilevel Difference-Enhanced Denoise Network for U...

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

作者： He Zong Erlei Zhang Xinyu Li Hongming Zhang Jinchang Ren College of Information Engineering Northwest A&F University Yangling China School of Computing and Engineering Robert Gordon University Aberdeen United Kingdom

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Change detection in synthetic aperture radar (SAR) images is a hot yet highly challenging task in remote sensing. Existing unsupervised SAR change detection methods often struggle with inherent speckle noise and insufficiently utilize pseudo-labels, particularly neglecting uncertain areas. In this paper, we propose a multilevel difference-enhanced denoise dual-branch network (MDDNet), comprising representation learning and change detection branches. First, fuzzy c-means clustering is employed to generate pseudo-labels, categorizing the image areas as changed, nochanged, and uncertain. Second, we design a denoise representation loss function in the representation learning branch to maximize the use of pseudo-labels, while mitigating speckle noise. Furthermore, a multilevel difference computation module is proposed to focus on changes in ground objects and capture more comprehensive change information. Experimental results on three public SAR datasets show that the proposed method outperforms six state-of-the-art methods, achieving the best performance with an average overall accuracy of 98.86% and an average Kappa coefficient of 89.36%.

关键词： Representation learning Noise Speckle Radar imaging Feature extraction Radar polarimetry Radar signal processing Speech processing Synthetic aperture radar remote sensing

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Cross-sensor radiometric normalization of Planet smallsat data using Sentinel-2 to improve consistency across scenes and environments 27

Cross-sensor radiometric normalization of Planet smallsat da...

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conference on image and signal processing for remote sensing XXVII

作者： Hemingway, Benjamin L. Frazier, Amy E. Arizona State Univ Sch Geog Sci & Urban Planning 975 S Myrtle Ave Tempe AZ 85281 USA

ISBN: (数字)9781510645691

ISBN: (纸本)9781510645691;9781510645684

Commercial smallsats are emerging as a key resource for the remote sensing community, with Planet Labs, Inc. operating the largest constellation. Global coverage is achieved through the combined efforts of many different sensors, but the unique spectral response of each sensor results in radiometric inconsistencies across the constellation. These inconsistencies have been an obstacle for researchers as the images do not match the quality the remote sensing community has come to expect from traditional platforms such as Landsat or Sentinel. Various approaches have been offered to correct cross-sensor radiometric inconsistencies in Planet image acquisitions, with many utilizing high radiometric quality platforms such as Landsat or Sentinel to normalize Planet surface reflectance via a linear transformation. These approaches have largely been applied in homogenous regions, and performance in heterogeneous landscapes is not well understood. The Planet surface reflectance images were transformed linearly using Sentinel-2 surface reflectance as a reference. Transformations were tested in six heterogeneous National Ecological Observatory Network sites where airborne hyperspectral data was available for validation. The probability density functions of the transformed Planet images and the corresponding Sentinel reference images were compared using the D statistic of the Kolmogorov-Smirnov test. The absolute spectral accuracy of the transformed Planet images was evaluated against airborne hyperspectral data. Results show that the transformations were effective in transforming the empirical cumulative distribution functions of the Planet images to be more similar to that of Sentinel regardless of initial offsets.

关键词： cubesat constellation remote sensing radiometric correction signal processing

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Single Band NIR-to-RGB image Colorization using Attention Guided Conditional GAN

Single Band NIR-to-RGB Image Colorization using Attention Gu...

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Space, Aerospace and Defence conference (SPACE), IEEE

作者： Moordhan Songade Sunita Arya S. Manthira Moorthi Dept. of Computer Science and Engineering Malaviya National Institute of Technology Jaipur India Signal and Image Processing Area Space Applications Centre ISRO Ahmedabad India

ISBN: (数字)9798350367386

ISBN: (纸本)9798350367393

NIR images, capturing light beyond visible spectrum, are useful in remote sensing, astronomy, etc. Converting them to RGB format makes them better to interpret for humans. This paper introduces a novel approach for NIR to RGB image colorization using GAN. Our primary contribution is a Conditional Generative Adversarial Network (cGAN) with an enhanced Attention UNet serving as its generator. With this model, we have achieved an Angular Error of 15.55, PSNR of 16.41 and SSIM of 0.69 which is comparable to the other prepared models’ like pix2pix & Attention UNet as well as more visually appealing than all of them.

关键词： Deep learning Visualization Statistical analysis image color analysis Generative adversarial networks Generators remote sensing

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Leveraging ambient sensing for the estimation of curiosity-driven human crowd 16

Leveraging ambient sensing for the estimation of curiosity-d...

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16th Annual IEEE International Systems conference (SysCon)

作者： Das, Anirban Narayan, Kartik Chakraborty, Suchetana IIIT Guwahati Dept CSE Gauhati India IIT Jodhpur Dept CSE Jodhpur Rajasthan India

ISBN: (纸本)9781665439923

Identification and characterization of human crowd formulation have been a topic of immense interest in recent times due to its applicability in a wide range of smart-city applications covering infrastructure automation to targeted advertising. The core idea is to extract the dynamics and associated behavioural patterns of mass gatherings within an environment through a continuous remote monitoring of the crowd. In general, the existing approaches heavily rely on computer vision and image processing based algorithmic tools and techniques to address this problem or mandate the crowd entities to carry a smartphone with them. However, considering the ubiquitous design goals of futuristic smart applications, camera and smartphone driven active sensing is not suitable to honour users' right to privacy by requiring an active user participation. In this work, we introduce a novel approach towards measuring the spatio-temporal significance of an object in terms of the curious crowd it has attracted over the others. The proposed approach utilizes a set of passive sensors and Wireless signal properties for the necessary estimation. We validate the idea using a room-scale testbed with rigorous experimentation in a real-world scenario. The low cost solution has minimal invasive footprints towards privacy and is capable to reach beyond 90% of accuracy for this measurement.

关键词： Crowd IoT Ambient sensing CSI Passive

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Improved SRCNN remote sensing image spatio-temporal fusion based on multi-stream data input and attention mechanism: Taking Landsat8 and MODIS remote sensing images as examples

Improved SRCNN remote sensing image spatio-temporal fusion b...

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2021 International conference on signal image processing and Communication, ICSIPC 2021

作者： Ping, Liu Xiangru, Jia Bo, Li Xinrui, Li Feilong, Wang Mengrou, Yao College of Big Data Taiyuan University of Technology Shanxi Province Jinzhong City030600 China

ISBN: (数字)9781510645417

ISBN: (纸本)9781510645400

In order to improve the experimental effect of SRCNN under spatio-temporal fusion, the paper, taking Landsat8 OLI and MODIS images as examples, adopts the detailed information of adjacent time-phase high-resolution images as a prior information into the network input on the basis of SRCNN and introduces the attention mechanism. It proposes an improved SRCNN remote sensing image spatio-temporal fusion on the basis of multi-stream data input and attention mechanism (MBA-SRCNN). The improved SRCNN remote sensing image spatio-temporal fusion based on multi-stream data input (M-SRCNN) is taken as a comparative experiment. The results show that M-SRCNN had significantly improved the experimental effect when compared with SRCNN. In the evaluation of the entire image, PSNR reached 32.9252 and SSIM reached 0.8712, which were optimized by 4.0667 and 0.0840 respectively, and the optimization range of RED band was the largest one. M-SRCNN improved the distortion and edge blur of SRCNN, and the specific performance was significantly enhanced. On the basis of M-SRCNN, MBA-SRCNN had optimized PSNR by 1.3847 and SSIM by 0.0247, which enhances the reconstruction effect of low-frequency information. The MBA-SRCNN constructed in this study can generate remote sensing images with high temporal and spatial resolution more accurately, which has a certain significance for the research in the field of spatio-temporal fusion of remote sensing images. © 2021 SPIE.

关键词： image enhancement

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3D Expansion of SRCNN for Spatial Enhancement of Hyperspectral remote sensing images 4

3D Expansion of SRCNN for Spatial Enhancement of Hyperspectr...

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4th International conference on signal processing and Information Security (ICSPIS)

作者： Aburaed, Nour Alkhatib, Mohammed Q. Marshall, Stephen Zabalza, Jaime Al Ahmad, Hussain Univ Dubai Coll Engn & IT Dubai U Arab Emirates Univ Strathclyde Dept Elect & Elect Engn Glasgow Lanark Scotland

ISBN: (纸本)9781665437967

Hyperspectral imagery (HSI) have high spectral resolution but suffer from low spatial resolution due to sensor tradeoffs. This limitation hinders utilizing the full potential of HSI. Single image Super Resolution (SISR) techniques can be used to enhance the spatial resolution of HSI. Since these techniques rely on estimating missing information from one Low Resolution (LR) HSI, they are considered ill-posed. Furthermore, most spatial enhancement techniques cause spectral distortions in the estimated High Resolution (HR) HSI. This paper deals with the extension and modification of Convolutional Neural Networks (CNNs) to enhance HSI while preserving their spectral fidelity. The proposed method is tested, evaluated, and compared against other methodologies quantitatively using Peak signal-to-noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), and Spectral Angle Mapper (SAM).

关键词： Hyperspectral remote sensing single image super resolution 3D convolution

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MPFFNet:Semantic Segmentation of remote sensing image Buildings Based on Multipath Feature Fusion

MPFFNet:Semantic Segmentation of Remote Sensing Image Buildi...

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Communication, image and signal processing (CCISP), International conference on

作者： Jie Dong Jingkun Mao Kun Liu Liangyong Cheng School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China Interplanetary Space Technology Development Co. LTD Tianjin China

The buildings with variable scales and complex background information in remote sensing images often result in missed and misclassified segmentation of buildings. To address this issue, this paper designs an multipath feature fusion network (MPFFNet). MPFFNet has an overall encoding and decoding structure, embedded with an residual depth Inception block(RDI) module to extract multi-scale features. At the same time, MPFFNet uses the multipath residual channel attention (MPRCA) module to expand the Receptive field of the feature map to obtain rich multi-scale context information. The validity of the proposed algorithm is proved on the WHU Buildings data set, with F1 reaching 94.70%, IOU reaching 89.93%, and Recall reaching 94.98%.

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Morphological Segmentation with Modified Otsu’s Method and Multi-Scale Morphological processing for Shape Recognition

Morphological Segmentation with Modified Otsu’s Method and ...

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Innovations in High Speed Communication and signal processing (IHCSP), International conference on

作者： Manish Nagpal N.T. Velusudha Deepak Minhas Supriya Rai Ashwini Ranjit Nawadkar D. Bhanu Centre of Research Impact and Outcome Chitkara University Punjab India Department of ECE Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement UG ManagementJAIN (Deemed to be University) Bangalore Karnataka India AI & Ds Vishwakarma Institute of Technology Pune India Department of Information Technology Karpagam Institute of Technology Coimbatore India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Motological segmentation is essential in the domains of medical imaging, object recognition, and remote sensing. Classical approaches, such as Otsu's thresholding, fail miserably in the presence of noise, light, and complex backgrounds. These concerns are addressed by creating Modified Otsu's Method (MOM). Slow and precise segmentation, adaptive thresholding, and morphological processes are two new areas that MOM aims to tackle. The proposed MOM makes both local and global adjustments taking into account the image and text in order to mitigate the impact of variations in light intensity and noise level. Accurate segmentation and less noise artefacts are achieved by the use of dilation and erosion. On datasets including medical, nature, and remote sensing images, the suggested MOM algorithm achieves better results than previous top-tier segmentation approaches as well as Otsu's approach to generic thresholding based on experimental quantities. This study investigates the optimal execution time of the method for image processing in real time in terms of computing cost. Automated safety devices and medical imaging analysis are two examples of applications where MOM stands out for its exceptional performance in picture segmentation. Therefore, in order to make MOM more versatile and effective in a wide range of imaging contexts, future studies will use machine learning methods.

关键词： image segmentation Accuracy Thresholding (Imaging) Noise Real-time systems Object recognition Method of moments remote sensing Biomedical imaging Noise level

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