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Complex-valued Butterfly Transform for Efficient Hyperspectral image processing

Complex-valued Butterfly Transform for Efficient Hyperspectr...

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IEEE International conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Singhal, Utkarsh Yu, Stella X.

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Hyper-spectral imaging (HSI) is a critical remote sensing modality that captures high-resolution spectral information in addition to high spatial resolution. Due to its ability to capture rich information about the material properties of the target, HSI has found applications such as agriculture, ecological monitoring, urban planning, and medicine. However, HSI images tend to have high spatial resolution and many channels, thus requiring models that can integrate information over a large context and identify spectral signatures. HSI classification datasets are also highly imbalanced, sparsely labeled, and significantly smaller than standard vision datasets like imageNet or CIFAR, thus motivating smaller models with high sample efficiency. This work combines the strengths of multi-scale representations and data-driven feature learning. We generalize the butterfly transform to a learned complex-valued butterfly layer, allowing for parameter-efficient extraction of hierarchical complex-valued features from 1D signals. This allows us to create a lean yet highly accurate hyperspectral image classification model. Benchmarked on the Indian Pines, ROSIS-03 Pavia University, and Salinas datasets, our method demonstrates accuracy on par with SSDGL while using 7x fewer parameters. On the most imbalanced and sparsely labeled dataset, our method outperforms SSDGL.

关键词： complex-valued deep learning FFT butterfly transform signal processing hyper-spectral imaging

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Semantic Segmentation of High Spatial Resolution remote sensing imagery Based on Weighted Attention U-Net 14

Semantic Segmentation of High Spatial Resolution Remote Sens...

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14th International conference on Graphics and image processing (ICGIP)

作者： Zhang, Yue Wang, Leiguang Yang, Ruiqi Chen, Nan Zhao, Yili Dai, Qinling Southwest Forestry Univ Fac Forestry Kunming Yunnan Peoples R China Southwest Forestry Univ Inst Big Data & Artificial Intelligence Kunming Yunnan Peoples R China Southwest Forestry Univ Coll Big Data & Intelligent Engn Kunming Yunnan Peoples R China Southwest Forestry Univ Art & Design Coll Kunming Yunnan Peoples R China

ISBN: (纸本)9781510666313;9781510666320

In recent years, with the development of deep learning and attention mechanism, more research has been carried out to realize semantic image segmentation based on deep learning integrated attention mechanisms. However, the current semantic segmentation methods have low segmentation accuracy, high computation cost, and serious loss of detailed information. In this paper, a lightweight designed attention gate model was introduced to reduce the computation cost. And because it can suppress irrelevant regions in the input image, while highlighting the salient features of specific tasks, the combination of the two weighting factors input features (x(l)) and gating signal (g) in this structure can improve segmentation accuracy and reduce loss of detail. Therefore, this study used the weighted attention U-Net network to perform semantic segmentation on the GID dataset and finally evaluated it on the four indicators of Precision, Recall, F1-Sorce, and mIoU. This result shows that different weight values have a more significant impact on the experimental results. The attention U-Net with the best weight combination compared with the traditional U-Net network, Precision, Recall, F1-Sorce, and mIoU are increased by 0.88%, 1.4%, 1.13%, and 1.2%, respectively. Compared with the original attention U-Net, Precision, Recall, F1-Sorce, and mIoU are increased by 0.86%, 1.24%, 1.04%, and 1.75%, respectively.

关键词： Semantic segmentation deep learning attention gate model weighted attention U-Net GID dataset

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Multi-scale Frequency Attention Fusion Network for Optical and SAR image Registration

Multi-scale Frequency Attention Fusion Network for Optical a...

引用

International conference on signal processing Proceedings (ICSP)

作者： Xin Hu Yan Wu Zhikang Li Zhifei Yang Ming Li School of Electronic Engineering Xidian University Xi’an China National Key Laboratory of Radar Signal Processing Xidian University Xi’an China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

image registration is a prerequisite for remote sensing image fusion and classification, and registration accuracy affects the performance of these tasks. However, there are significant nonlinear radiation differences between optical and synthetic aperture radar (SAR) images and speckle noise in SAR, which limits the ability of pure convolutional neural networks (CNNs) to obtain modality-shared features. To solve these problems, this paper proposes a Multi-scale Frequency Attention Fusion Network for Optical and SAR image Registration (MFAF-OS-RegNet), which includes a Multi-scale Frequency Channel Attention Fusion (MFCAF) module and a Momentum Contrastive Learning (MCL) strategy. Firstly, we extract repeatable keypoints distributed on the edges and develop a novel MFCAF module to obtain modality-shared features. In the MFCAF module, multi-scale feature fusion is used to improve the positioning accuracy of matching points, and frequency channel attention is used to mine more favorable features. Secondly, we design a MCL strategy to reduce the interference of speckle noise in network training. The momentum teacher corrects handcrafted labels by learning the essential characteristics of the data for robust matching. Finally, we filter outliers and estimate transformation parameters based on the initial matching correspondence output by MFAF-OS-RegNet. Experimental results on a publicly available OS-dataset show that the proposed method outperforms other state-of-the-art methods.

关键词： Optical filters Integrated optics Optical fiber networks Optical imaging Radar polarimetry Adaptive optics Optical sensors Optical signal processing Nonlinear optics Synthetic aperture radar

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Multispectral image Fusion based on Super Pixel Segmentation

Multispectral Image Fusion based on Super Pixel Segmentation

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

作者： Nati Ofir Kingston University London

Multispectral image fusion is a computer vision process that is essential to remote sensing. For applications such as dehazing and object detection, there is a need to offer solutions that can perform in real-time on any type of scene. Unfortunately, current state-of-the-art approaches do not meet these criteria as they need to be trained on domain-specific data and have high computational complexity. This paper focuses on the task of fusing color (RGB) and near-infrared (NIR) images as this the typical RGBT sensors, as in multispectral cameras for detection, fusion, and dehazing. Indeed, the NIR channel has the ability to capture details not visible in RGB and see beyond haze, fog, and clouds. To combine this information, a novel approach based on superpixel segmentation is designed so that multispectral image fusion is performed according to the specific local content of the images to be fused. There-fore, the proposed method produces a fusion that contains the most relevant content of each spectrum. The experiments re-ported in this manuscript show that the novel approach better preserves details than alternative fusion methods.

关键词： image segmentation image color analysis signal processing Sensor fusion Real-time systems Sensors Task analysis

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Cloud Cover Removal from remote sensing Data using GANs Based on Attention Mechanism

Cloud Cover Removal from Remote Sensing Data using GANs Base...

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International conference on image Information processing

作者： B Siva Jyothi Natha Reddy D Sasikala Computer Science and Engineering Amrita Vishwa Vidyapeetham School of Computing Chennai

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

Cloud interference is a significant challenge in remote sensing applications, impacting the quality and reliability of data used for environmental analysis, disaster monitoring, and urban planning. In this study, we present an innovative approach to remove clouds from remote sensing data by employing Generative Adversarial Networks (GANs) with an integrated attention mechanism. Our method is designed to effectively restore regions obscured by clouds while preserving the integrity of the remaining image. We introduce a specialized architecture called U-net based Attention GAN (UAGAN) to address cloud cover issues. In this architecture, the generator network utilizes attention mechanisms to identify and rectify cloud-affected areas. The attention mechanism dynamically emphasizes relevant spatial regions during cloud removal, enhancing the precision of the restoration process. To evaluate our approach, we conducted experiments using the RICE dataset and trained our UAGAN model. Our method’s effectiveness was assessed using quantitative metrics, including the Structural Similarity Index (SSIM), Root Mean Squared Error (RMSE), and Peak signal-to-Noise Ratio (PSNR). Comparative analysis of three different models, namely vanilla GAN, U-net GAN, and UAGAN, demonstrates that UAGAN outperforms the others with impressive results, achieving a PSNR of 29.901 dB, RMSE of 7.891, and SSIM of *** research contributes to the advancement of cloud removal techniques in remote sensing, offering a promising solution for improving data quality and enhancing the accuracy of downstream applications. The integration of GANs and attention mechanisms showcases the potential for innovative approaches to address complex challenges in remote sensing data analysis.

关键词： Measurement PSNR Clouds Disasters Urban planning Weather forecasting Generative adversarial networks

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PolSAR image Classification Via a Novel Selective Kernel Encoder-Decoder Fully Convolutional Network 14

PolSAR Image Classification Via a Novel Selective Kernel Enc...

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14th International conference on signal processing Systems, ICSPS 2022

作者： Fang, Zheng Zhang, Gong Dai, Qijun Xue, Biao School of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798350336313

In recent years, convolutional neural networks (CNN) have made significant advances in polarimetric synthetic aperture radar (PolSAR) image classification. However, the current CNN-based PolSAR classifiers are patch-wise frameworks. The images need to be divided into overlapping patches, which have the disadvantage of repeated calculation. Additionally, the receptive field in CNN-based methods has fixed sizes, which restricts feature extraction capability. In this paper, an attention-based selective kernel encoder-decoder fully convolutional network (SK-EDNet) is introduced to PolSAR classification. Compared to CNN-based methods, the encoder-decoder fully convolutional network (EDNet) can use arbitrary-size images as input and output the whole image classification results. Meanwhile, it is utilized to extract global spatial information, which can improve the performance of classification. Besides, a selective kernel module is added to the EDNet, in which softmax attention is used to fuse several branches with different receptive field sizes. Hence, the selective kernel module can capture features with different scales and improve classification performance. The experiment results demonstrate that the SK-EDNet can obtain superior performance compared with CNN-based and traditional fully convolutional network methods. © 2022 IEEE.

关键词： image classification

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Performance of pansharpening methods varying with input data formats 27

Performance of pansharpening methods varying with input data...

引用

conference on image and signal processing for remote sensing XXVII

作者： Arienzo, A. Aiazzi, B. Alparone, L. Garzelli, A. Zoppetti, C. Inst Appl Phys IFAC CNR Res Area Florence I-50019 Sesto Fiorentino Italy Univ Florence Dept Informat Engn I-50139 Florence Italy Univ Siena Dept Informat Engn & Math I-53100 Siena Italy

ISBN: (数字)9781510645691

ISBN: (纸本)9781510645691;9781510645684

In this work, we investigate on how the performance of pansharpening methods depends of their input data format, either floating-point, e.g. spectral radiance, or digital numbers (DN) in a packed fixed-point representation. It is theoretically proven and experimentally demonstrated that methods based on multiresolution analysis are unaffected by the data format, which instead is crucial for methods based on component-substitution (CS), unless the intensity component is calculated by means of a multivariate linear regression between the upsampled bands and the lowpass-filtered Pan, as it occurs for the most advanced CS methods. In an experimental setup, WorldView-2 data are either fused in their original 11-bits DN format, or converted to spectral radiance before fusion, by applying the gains and offsets provided in the file header. In the former case, fusion results are converted to spectral radiance before quality is measured. Nine fusion methods have been considered: results exactly match the theoretical investigations. For the majority of CS fusion methods, which do not feature a regression-based intensity calculation, results are better whenever they are obtained from floating-point calibrated data.

关键词： Data formats pansharpening multispectral images remote sensing reproducibility

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Computational methods to retrieve soil moisture using remote sensing data: A review

Computational methods to retrieve soil moisture using remote...

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International conference on Advanced Technologies for signal and image processing (ATSIP)

作者： Raja Inoubli Daniel Enrique Constantino-Recillas Alejandro Monsiváis-Huertero Lilia Bennaceur Farah Imed Riadh Farah National School of Computer Science Labratoire RIADI Manouba Tunisia Tecnolóogico Nacional de Méexico / TES de Ecatepec Mexico City Mexico ESIME Ticomáan Instituto Politéecnico Nacional Mexico City Mexico LTSIRS laboratory Preparatory Institute for Engineering Studies El Manar and National Institute of Engineering Sciences of Tunis Manar Tunisia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Recently, soil moisture (SM) retrieval has become the first interest of the scientific community due to the increasing demand for water resources in several fields, especially the agricultural sector. A lack of such resources has been detected along with the increasing climate changes and disasters occurring such as drought. It has become very essential to search for efficient approaches and data sources that can serve effectively the SM retrieval goals. This paper is a literature review set to identify the strengths and weaknesses of the most relevant works in the area of SM retrieval and remote sensing (RS) published from 2017 to 2024. Summarizing the selected studies serves to detect the relevant information that tackles the SM retrieval challenges. Thus, creating new horizons for new research. One of the main fields of interest is SM retrieval over agriculture based on the synergistic use of Sentinel-1 (S-1) and Sentinel-2 (S-2) images. High accuracy has been achieved by combining different approaches such as backscattering models and Machine Learning (ML) algorithms (e.g., AIEM and NN algorithms). The application of such a combination is highly recommended due to its promising potential.

关键词： Accuracy Machine learning algorithms Bibliographies Soft sensors Soil moisture Estimation Sentinel-1

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Innovative Cumulative Intensity Projection Registration for Vertical Shift Calibration in HoloSAR

Innovative Cumulative Intensity Projection Registration for ...

引用

IEEE International conference on signal and image processing (ICSIP)

作者： Fengzhuo Huang Dong Feng Xiaotao Huang College of Electronic Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

Synthetic aperture radar (SAR) holography (HoloSAR) is a vital remote sensing technique widely employed in extracting three-dimensional (3-D) scattering information of anisotropic targets. In the case of airborne SAR, factors such as flight trajectory errors can introduce phase errors during the imaging process, thereby degrading the quality of HoloSAR images. This paper modifies the phase gradient autofocus (PGA) method based on cumulative intensity projection registration to improve imaging quality. The proposed method begins with an initial phase calibration using the traditional PGA method. Subsequently, the 3-D imaging results, after preliminary compensation, are projected onto two-dimensional (2-D) planes via cumulative intensity projection. The residual linear phase error is estimated based on the vertical shifts observed in the projection results of different sub-apertures. This error is then corrected by adjusting the imaging results, thereby resolving the issue of vertical shift between sub-apertures. Compared to other mainstream methods, the proposed method offers simplicity in principle and high computational efficiency. Experiments on simulation and GOTCHA data validate the effectiveness of the proposed method.

关键词： Two-dimensional displays Scattering Radar imaging Holography Radar signal processing Calibration Trajectory

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A Fast SAR-Optical Registration Algorithm for Land-Water Interface Area

A Fast SAR-Optical Registration Algorithm for Land-Water Int...

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IEEE International conference on signal and image processing (ICSIP)

作者： Keke Zhang Anxi Yu Wenhao Tong Zhen Dong Huatao Yu College of Electronic Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

In recent years, the field of registration algorithms for synthetic aperture radar (SAR) and optical images has witnessed substantial advancements. However, most existing algorithms mainly rely on the structural features of images for registration, which still face significant challenges when processing remote sensing images containing a large number of weakly textured scenes in land-water interface areas(e.g., port regions). This paper proposes an area-aware SAR-optical registration algorithm that combines texture features and structural features to address the challenge above. The algorithm first employs an improved edge density(ED)function to calculate texture features to ensure that feature points are uniformly distributed in texture-rich regions such as land. Next, the algorithm extracts the gradient orientation and amplitude of local regions, which are typical structural features, and constructs a HOG-like(Histogram of Orientation Gradients) descriptor to improve computational efficiency. Finally, accurate registration of images is achieved through feature matching. The application of the proposed algorithm to the matching task of land-water interface areas has demonstrated enhanced accuracy and robustness in registration, as evidenced by the results.

关键词： image registration Accuracy Feature extraction Optical imaging Adaptive optics Robustness Radar polarimetry

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