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High SNR Camera Electronics for Ocean Imaging

High SNR Camera Electronics for Ocean Imaging

引用

Space, Aerospace and Defence conference (SPACE), IEEE

作者： Pradeep Soni Ashok Kumar Nilesh B Singal Deepak Jain Ravi Kumar P Nandha Vishnukumar D Patel Space Applications Centre ISRO Ahmedabad India

ISBN: (数字)9798350367386

ISBN: (纸本)9798350367393

signal to Noise Ratio (SNR) is one of the performance metrics for remote sensing electro-optical payloads. In ocean imaging payloads, sea surface reflected signal is very feeble, which leads to very stringent electronics noise budget to maintain the high SNR requirement. In Ocean Color Monitor-3 payload, SNR budget is ~ 650 to 10000 after digital processing. Camera electronics (for 13 spectral bands) is developed to provide 12-bit radiometric accuracy with onboard programmable processing capabilities like frame oversampling, frame/spatial binning, Time Delay Integration (TDI), compression and CCSDS formatting. This paper discusses the challenges in the design and development of camera electronics.

关键词： Sea surface image color analysis Space missions Cameras Radiometry Optical sensors remote sensing

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Group-wise Semantic-enhanced Interaction Network for remote sensing Spatio-Temporal Fusion

Group-wise Semantic-enhanced Interaction Network for Remote ...

引用

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

作者： Baoluo Zhu Shenglong Hu Huihui Song Kaihua Zhang Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

remote sensing spatio-temporal fusion (STF) aims at fusing temporally-dense coarse-resolution images and temporally-sparse fine-resolution images to reconstruct high spatio-temporal resolution images. Multi-band remote sensing images are often accepted as inputs for STF that have complementary characteristics for high-fidelity land surface reconstruction, yet the existing STF framework often treats different bands uniformly without considering the statistical correlation between different bands, resulting in unsatisfying results. To address this problem, this paper presents a group-wise semantic-enhanced interactive network for STF, dubbed as GSINet. Based on statistical observations, the feature correlation between the visible-light group and the invisible-light group is weak, while the intra-group correlation is strong. Therefore, the GSINet first separates the inputs into visible-light and invisible-light groups, which are fed into different branches with independent encoders for feature extraction and fusion. Afterwards, to address the issue of land cover changes between the prediction coarse- and reference fine-resolution images, a Semantic-Enhancement Fusion Module (SEFM) is designed to interact with the features from the same group with enhanced semantic information captured in an unsupervised learning manner. Then, the semantic-enhanced fused features from different bands are fed into an Interleaved Cross-attention Module (ICM) for further fusion. Finally, the output fusion features fully encode the intra- and inter-group information, which are fed into the decoder, reconstructing the spatio-temporal high-resolution images. Extensive experiments on CIA and LGC benchmark datasets demonstrate that the GSINet outperforms a variety of state-of-the-art methods in terms of multiple metrics.

关键词： Surface reconstruction Correlation Land surface Benchmark testing Speech enhancement signal processing remote sensing image reconstruction Unsupervised learning Surface treatment

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Multi-model image matching based on feature map matching method

Multi-model image matching based on feature map matching met...

引用

6th International conference on Intelligent Computing and signal processing (ICSP)

作者： Wei Qi Wenhui Lang School of Computer and Information of Hefei University of Technology Hefei China

In the field of remote sensing, multimodal data matching is a problem with great challenges, and the matching between SAR and visible light images is tremendously difficult, because there is obvious speckle noise in SAR images, and the imaging methods of SAR images and optical images are very different. At present, although lots of excellent results have been fulfilled in multimodal image matching, it is difficult to meet the rapid and even real-time processing requirements of massive remote sensing data such as disaster emergency and military applications due to the complexity of the matching task. In view of the above problems, this paper proposes a feature map matching method based on the deep convolution neural network model of the pseudo-Siam architecture, which can quickly accomplish the multimodal image matching.

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Dynamic Sparse Encoding and Cross-Temporal Attention for remote sensing image Change Detection

Dynamic Sparse Encoding and Cross-Temporal Attention for Rem...

引用

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

作者： Shaoxiong Lin Tao Lei Tongfei Liu Shuxin Zhang Chongdan Min Asoke K. Nandi Shaanxi Joint Laboratory of Artificial Intelligence Shaanxi University of Science and Technology Xi’an China Department of Electronic and Electrical Engineering Brunel University London London United Kindom

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Due to the inherent inductive bias of operations, convolutional neural networks (CNN) cannot model global information of remote sensing (RS) images. In contrast, Transformer-based methods can establish long-range dependencies of images through self-attention (SA) mechanism, but it faces the challenges of computational complexity and memory requirements, but also ignores the exploration on the feature redundancy removal of RS images. To address these two issues, we propose a network based on dynamic sparse encoding and cross-temporal collaborative attention (DSECTCA-Net) for RS image change detection (CD). First, we implement dynamic sparse encoding (DSE) by designing hierarchical sparse Transformer module (HSTM), which decreases the correlation calculation of the SA mechanism and effectively reduces the computational complexity and parameter amount of Transformer. Secondly, we propose cross-temporal collaborative attention (CTCA) to model RS images in time series and fully explore the interactivity between dual-temporal RS images, so as to better extract the global understanding of visual scenes. Extensive experiments on two large-scale public RS datasets show that the proposed method not only provides higher detection accuracy, but also achieves lower computational complexity and required storage space than most popular CD networks.

关键词： Visualization image coding Correlation Computational modeling Collaboration Feature extraction Transformers Convolutional neural networks Computational complexity remote sensing

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Research and design of image super-resolution algorithm based on attention mechanism 2

Research and design of image super-resolution algorithm base...

引用

2nd International conference on Advanced Algorithms and signal image processing, AASIP 2022

作者： Tang, Yu Wang, Zi-Yi College of Computer and Information Science Chongqing Chongqing Normal University Chongqing China

ISBN: (纸本)9781510660571

image super-resolution algorithm is a technology that changes the image from low-resolution to high-resolution. It is widely used in various fields, including but not limited to image compression technology, satellite remote sensing imaging, urban traffic monitoring, medical imaging and so on. At present, the method based on deep learning has a very good effect on the super-resolution processing of low-resolution images, but some algorithms still have some problems, such as the loss of detail texture of the reconstructed image, and the large gap between the reconstruction results and the computational resources. To solve the above problems, this paper proposes a network model based on channel attention mechanism. The network model consists of three parts: the first part is a shallow feature extraction block, which is composed of a convolution layer and an activation layer, which is mainly used to extract the low-level features of the input image;The second part is the deep extraction block based on the channel attention mechanism, and the depth separable convolution is added to the local residual block of this part to effectively reduce the huge parameters generated by training. This module mainly extracts the high-level features of the input image;The third part is the reconstruction module, which is used to fuse the original image features extracted from the previous two parts and output the reconstructed image. Finally, the experimental results show that using this method will effectively improve the peak signal-to-noise ratio and structural similarity index of the reconstructed image. © 2022 SPIE.

关键词： Convolution

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Robust Nonnegative Matrix Factorization Based Background Reconstruction for Hyperspectral image Anomaly Detection 7

Robust Nonnegative Matrix Factorization Based Background Rec...

引用

7th International conference on signal and image processing, ICSIP 2022

作者： Xiaorui, Song Lingyan, Chen Caiping, Li Tao, Dong Haojun, Zhu Heng, Wang Beijing Institute of Remote Sensing Information Beijing100192 China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

In the anomaly detection of hyperspectral images (HSIs), aiming at the difficulty of distinguishing the abnormal target from the background and the low accuracy of background prediction, a new HSI anomaly detection approach based on robust nonnegative matrix factorization(NMF) background reconstruction is proposed. In this approach, a more robust new form objective function is introduced into the NMF process to improve the adaptability to noise and outliers, and a new background reconstruction objective function can be obtained. However, the new objective function is non-convex. The global optimal solution of one variable can be obtained when the other is fixed. Thus, we propose an efficient multiplicative updating algorithm with the theory of the iterative reweighted least squares. Finally, when iterative convergences, we can obtain the extracted endmembers and estimated abundances to reconstruct the background. Then, the reconstructed background image was subtracted from the origin image to obtain the residual image. Finally, the anomaly detection is achieved by using the local anomaly detector to traverse the residual image. Furthermore, the effectiveness of the HSI anomaly detection algorithm proposed based on robust NMF background reconstruction is illustrated in a series of real-world data experiments. © 2022 IEEE.

关键词： Anomaly detection

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Unsupervised Multi-level Segmentation Framework for PolSAR Data using H-Alpha features and the Combined Edge-Region based segmentation

Unsupervised Multi-level Segmentation Framework for PolSAR D...

引用

IEEE Aerospace conference

作者： Elenean, M. Abo Hafez, A. T. Helmy, A. K. ElTohamy, Fawzy Azouz, Ahmed Mil Tech Coll Aircraft Elect Syst Dept Cairo Egypt Natl Authoritie Remote Sensing & Space Sci 23 Joseph Tito St Cairo Egypt Mil Tech Coll Cairo Egypt

ISBN: (纸本)9781665490320

PolSAR (Polimetric Synthetic Aperture Radar) has been shown to be a powerful source of information. As a result of using up to four measurement channels at the same time, which increases the processing depth, it offers information about the geometrical and physical characteristics of objects. However, operating the PolSAR system to its full imaging potential requires significant computing power. In this study, a framework for fully polarimetric SAR image segmentation is proposed, in which the PolSAR signal is decomposed into four components that represent the eigenvectors of the autocovariance matrix corresponding to signals and clutter. The Unsupervised segmentation framework possesses two main processing levels. First level is the data preprocessing, including mean coherency matrix calculation, speckle reduction and polarimetric feature decomposition. Second level include the initial cluster Centers estimation, and edge-region based algorithm. This is achieved by using the combined H-Alpha and (averaged Intensity) lambda features derived from the target decomposition of the PolSAR data. Finally, k-Means clustering based on the Wishart distribution is used to optimize the iterative clustering by merging the clusters with the minimum Wishart distance. The proposed framework is applied on (Flevoland and San Francisco Bay). The images are selected to react differently with different polarization. The performance evaluation based on qualitative (Visual) and quantitative assessments. Visual assessment provides an excellent information on clarity and delineation of different *** is applicable for applications need an accurate statistical information. Quantitative evaluations provide more accurate results for separating different classes in the images. The proposed algorithm is compared to the traditional CloudePottier classification method. The results demonstrate that the proposed algorithm accuracy reaches (88.6%) with error (0.114), advances over the traditiona

关键词： Synthetic aperture radar

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Spatio-Semantic Prompt guided Adaptive Segment Anything for remote sensing Change Detection

Spatio-Semantic Prompt guided Adaptive Segment Anything for ...

引用

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

作者： Shenglong Hu Zhidong Han Gang Dong Lingyan Liang Dongchao Wen Kaihua Zhang NUIST IEIT Systems Co. Ltd.

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Existing leading remote sensing change detection (RSCD) often takes a semantic-agnostic learning paradigm, which uses a binary ground-truth mask as supervision for model training. Despite the demonstrated success, due to the intrinsic characteristic of extremely complicated scene changes in RS images, this paradigm is prone to be misled by irrelevant semantic category changes, leading to a noisy CD mask prediction. To address this issue, this paper presents a Spatio-Semantic Prompt (SSP) guided adaptive Segment Anything Model (SAM) for RSCD, dubbed as SSP-SAM. The SSP-SAM introduces sparse textual and dense mask prompts into SAM to encode the task-specific semantic knowledge for RSCD. Specifically, we first encode the powerful textual semantic knowledge using Contrastive Language-image Pre-training (CLIP) to determine the desired change semantic category. Then, we design a spatial dense prompt module that yields an attention map as prompt features to further refine the desired changed regions. Subsequently, we fine-tune the SAM through an adaptor to integrate the spatial-semantic prompt cues, yielding a coarse CD mask prediction. Finally, guided by the coarse CD mask, a multi-scale mask attention mechanism is adopted to learn the refined semantic representations of the changed targets, predicting the accurate CD mask. Extensive experiments on a variety of benchmark datasets demonstrate that the proposed SSP-SAM achieves state-of-the-art performance.

关键词： Training Adaptation models Accuracy Semantics Benchmark testing signal processing Sensors Noise measurement Speech processing remote sensing

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SINGLE remote sensing image DEHAZING USING A DUAL-STEP CASCADED RESIDUAL DENSE NETWORK

SINGLE REMOTE SENSING IMAGE DEHAZING USING A DUAL-STEP CASCA...

引用

IEEE International conference on image processing (ICIP)

作者： Huang, Yufeng Chen, Xiang Shenyang Aerosp Univ Coll Elect & Informat Engn Shenyang Peoples R China

ISBN: (纸本)9781665441155

remote sensing (RS) dehazing is an extremely challenging task since the non-uniform distribution of haze and fog severely degrade the images and difficult to extract features. To address these issues, we propose an end to end Dual-step Cascaded Residual Dense Network called DCRD-Net, which can exactly remove haze from the hazy RS image and precisely restoring the details. The architecture of network contains two cascaded task-driven subnetworks, in order to deal with the coarse and fine haze-relevant features separately. Besides, the Residual Dense Enhancement Block ( RDEB) is involved to guide the feature extraction, so that multi-scale information can be used to estimate the local and global features. Further, the Squeeze and Excitation (SE) Block is employed to optimize the RDEB, for the purpose to get the contextual feature and reduce computation complex. Quantitative and qualitative results illustrate that the designed framework outperforms the recent outstanding dehazing methods on promoting haze remove and restoring detailed in the synthetic and real-world RS images under various scenes. To encourage more comparisons, we release our codes on GitHub https://***/cxtalk /DCRD-Net.

关键词： Residual Dense Enhancement Block image dehazing remote sensing image cascade network

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image Restoration Based on Blur Kernel Estimation Using Vibration Data

Image Restoration Based on Blur Kernel Estimation Using Vibr...

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IEEE International conference on signal and image processing (ICSIP)

作者： Weida Xing Weixiao Tuo Xingfei Li Chenxi Yang State Key Laboratory of Precision Measuring Technology and Instruments Tianjin University Tianjin China Taihu Laboratory of Deepsea Technological Science Wuxi China

remote sensing images are usually blurred due to platform vibrations and camera defocus, which seriously limit its application. To solve the problem of blurring remote sensing images caused by in-orbit angular vibration, an image restoration approach is proposed. Magnetohydrodynamics (MHD) angular rate sensor is used to measure the angular vibration experiencing by the camera. The blur kernel is estimated with detected vibration and defocusing radius. A regularized image deblurring algorithm with total variation (TV) regularization is used to restore the blurred image. The modulation transfer function (MTF) is used to evaluate quality of the restored image. Simulations and experiments are conducted to validate the proposed approach. Results indicate that the proposed method can improve the image quality while preserving the detail information.

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