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Local and Global Feature Adaptive Adjustment Network for remote sensing image Scene Classification

Local and Global Feature Adaptive Adjustment Network for Rem...

引用

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

作者： Feng Cao Chang Liu Deyu Li Yuhua Qian Chao Zhang Hu Zhang School of Computer and Information Technology (School of Big Data) Shanxi University Taiyuan China Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education Shanxi University Taiyuan China Institute of Big Data and Industry Shanxi University Taiyuan China

Convolutional neural network (CNN)-based methods have been extensively used for remote sensing scene classification (RSSC) and have obtained remarkable classification results. However, its limitations in extracting global features have hindered further improvement. Transformers can directly capture global features through self-attention mechanisms, but they have deficiencies in modeling local features. Currently, an approach that directly combines CNN and Transformer features may lead to feature imbalance, and introduce redundant information. To address these problems, we propose a local and global feature adaptive adjustment network (LGFAANet) for RSSC. First, we employ a dual-branch network structure to extract local and global features from remote sensing scene images. Second, we design a local and global feature adaptive adjustment module (LGFAA) to dynamically allocate weights to the features. Third, we use a multi-layer feature aggregation module (MLFA) to aggregate the adjusted features, thereby further enhancing feature representation. Finally, we introduce joint loss to accelerate network convergence, while reducing intra-class distance and increasing inter-class distance. Experimental results demonstrate that our proposed method displays enhanced feature representation ability and outperforms existing state-of-the-art methods.

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Change Feature Refinement and Cross-Scale Interaction Network for High-Resolution remote sensing image Change Detection

Change Feature Refinement and Cross-Scale Interaction Networ...

引用

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

作者： Yongqi Chen Shou Feng Yingjie Tang Nan Su Chunhui Zhao College of Information and Comminication Engineering Harbin Engineering University Harbin China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Change detection represents a fundamental research area within remote sensing technology. Nevertheless, in practical applications, the spatial arrangement of terrestrial features is frequently highly complex. The changing objects vary significantly in size and exhibit irregular, complex shapes. Many mainstream methods are hindered by the intricate spatial scales of these changed objects, culminating in suboptimal detection performance. To overcome these obstacles, we propose a change feature refinement and cross-scale interaction network (FRCI-Net) for effective change detection. To capture more robust change features, FRCI-Net first employs a feature interaction module (FIM) and a differential feature refinement module (DFRM) to accurately capture change regions while enhancing the boundary details of the change features. To further enhance the recognition ability for multi-scale change targets, FRCI-Net leverages a cross-scale interaction module (CIM) to enable attention-driven integration and consolidation of change features across divers scales, thereby improving the network’s scale awareness of various change targets. Experiments conducted on two benchmark datasets validate that the proposed method surpasses six advanced methods.

关键词： Target recognition Shape Aggregates Benchmark testing Feature extraction Data processing remote sensing

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Hazy remote sensing images Semantic Segmentation for Weakly Annotation Based on Saliency-Aware Alignment Strategy

Hazy Remote Sensing Images Semantic Segmentation for Weakly ...

引用

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

作者： Junda Xu Libao Zhang School of Artificial Intelligence Beijing Normal University Beijing China

The technique of semantic segmentation (SS) holds significant importance in the domain of remote sensing image (RSI) processing. The current research primarily encompasses two problems: 1) RSIs are easily affected by clouds and haze; 2) SS based on strong annotation requires vast human and time costs. In this paper, we propose a weakly supervised semantic segmentation (WSSS) method for hazy RSIs based on saliency-aware alignment strategy. Firstly, we design alignment network (AN) and target network (TN) with the same structure. After training the AN with clear images, we extract the class activation maps of the two networks and construct a consistency loss to train the TN with hazy images. Secondly, we design a multi-scale channel-spatial attention module in the two classification networks to solve the problems of unclear foreground-background boundary and blurred target texture in hazy images. Finally, the pseudo-labels generated by the TN are utilized to train a feedback saliency analysis network, which is subsequently employed for obtaining segmentation results during the testing phase. The experiment results demonstrate that our approach achieves superior SS performance for hazy RSIs.

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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...

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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...

引用

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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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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