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FPGA design of video processing circuit based on 4Kx4K Large array infrared detector 7

FPGA design of video processing circuit based on 4Kx4K Large...

引用

7th International conference on Electromechanical Control Technology and Transportation, ICECTT 2022

作者： Wang, Lei Huang, Yi Shen, YiWei Yu, Dong Geng, Shufang Liu, Ping Beijing Institute of Space Mechanics & Electricity Beijing China

ISBN: (纸本)9781510656659

As the core component of the camera, infrared focal plane detector assembly is widely used in the field of aerospace infrared remote sensing because of its excellent performance in thermal image viewing, forward-looking early warning, prevention and control monitoring, infrared recognition and so on. Aiming at the 4kx4k large area array infrared video circuit spliced by four 2kx2k infrared detectors, firstly this paper considers the difficulties of piecewise adjustment of integration time and sampling point position delay compensation of spliced detectors from the perspective of FPGA. Then the design idea is given, including configuring and using management chip to reduce clock jitter and improve the reliability of data transmission;the functions of programmable adjustment of pixel blind coordinates and real-time inconsistency correction are realized, and the image quality is improved;using DDR2 to store inconsistency correction parameters;finally, the channel image data synthesis and transmission are completed. All functions such as focal plane control, signal processing and plug-in chip control are completed with one FPGA, which has the advantages of miniaturization and lightweight. The test shows that the design idea and scheme in this paper are feasible and practical, which provides reliable technical support for video signal processing of aerospace remote sensing large array detector, and is of great significance. © 2022 SPIE

关键词： Field programmable gate arrays (FPGA)

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Analysis of underwater noise characteristics of ships in offshore waters 2

Analysis of underwater noise characteristics of ships in off...

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2nd International conference on signal image processing and Communication, ICSIPC 2022

作者： Meng, Lingyi Tang, Yunfeng Marine Acoustics and Remote Sensing Laboratory Zhejiang Ocean University Zhoushan China

ISBN: (纸本)9781510657694

Ship noise has always been one of the important sources of marine noise. In order to study the sound source characteristics of underwater radiated noise from ships, this paper is based on the monitoring data collected in the sea area of Zhoushan Zhairuo Island, to get the underwater noise signal of the measured ship under different driving speeds. Through the time-frequency domain feature analysis, the short-term energy spectrum and spectrogram of the noise signal are drawn, and the following conclusions are obtained: The frequency of ship underwater noise is mainly concentrated in the interval of 0~700Hz, with the increase of ship speed, the main frequency range of underwater noise also increases, and the noise energy at different frequencies also increases. The sound pressure level of the underwater noise of the ship selected in this experiment is in the range of 75dB~100dB. With the increase of ship speed, the sound pressure level of ship underwater noise also increases, and has a certain linear relationship. The slope estimate m is 2.442dB/knot. © 2022 SPIE.

关键词： Frequency domain analysis

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TeAw: Text-Aware Few-Shot remote sensing image Scene Classification

TeAw: Text-Aware Few-Shot Remote Sensing Image Scene Classif...

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

作者： Kaihui Cheng Chule Yang Zunlin Fan Dayan Wu Naiyang Guan Defense Innovation Institute (DII) Academy of Military Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China

The recent advance has shown that few-shot learning may be a promising way to alleviate the data reliance of remote sensing image scene classification. However, most existing works focus on extracting distinguishable features only from visual modality, while the problem of learning knowledge from multiple modalities has barely been visited. In this work, we propose a text-aware framework for few-shot remote sensing image scene classification (TeAw). Specifically, TeAw converts the class names to more detailed text descriptions and extracts text features using a pre-trained text encoder. Mean-while, TeAw obtains image features via an image encoder. Then we compute the correlation between the text and the image features, which helps the model grasp the core concept of the input image. Finally, TeAw calculates the similarity of local features between supports and queries to get the predictions. Extensive experiments show the outperformance of our TeAw compared with other SOTA methods.

关键词： Visualization Correlation Computational modeling signal processing Feature extraction Transformers Sensors

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GRAD-CAM AWARE SUPERVISED ATTENTION FOR VISUAL QUESTION ANSWERING FOR POST-DISASTER DAMAGE ASSESSMENT 29

GRAD-CAM AWARE SUPERVISED ATTENTION FOR VISUAL QUESTION ANSW...

引用

IEEE International conference on image processing (ICIP)

作者： Sarkar, Argho Rahnemoonfar, Maryam Univ Maryland Baltimore Cty Comp Vis & Remote Sensing Lab Bina Lab Baltimore MD 21228 USA

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

In this paper, we present a Grad-Cam aware supervised attention framework for visual question answering (VQA) tasks for post-disaster damage assessment purposes. Visual-attention in visual question-answering tasks aims to focus on relevant image regions according to questions to predict answers. However, the conventional attention mechanisms in VQA work in an unsupervised manner, learning to give importance to visual contents by minimizing only task-specific loss. This approach fails to provide appropriate visual attention where the visual contents are very complex. The content and nature of UAV images in FlooNet-VQA dataset are very complex as they depict the hazardous scenario after Hurricane Harvey from a high altitude. To tackle this, we propose a supervised attention mechanism that uses explainable features from Grad-Cam to supervise visual attention in the VQA pipeline. The mechanism we propose operates in two stages. In the first stage of learning, we derived the visual explanations through Grad-Cam by training a baseline attention-based VQA model. In the second stage, we supervise our visual content for each question by incorporating the Grad-Cam explanations from the previous phase of the training process. We have improved the model performance over the state-of-the-art VQA models by a considerable margin on FloodNet dataset.

关键词： Visual Question Answering Supervised Attention Post-Disaster Assessment Natural Disaster Grad-Cam

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Enhanced Single remote sensing image Dehazing via Vision Transformer with Silencing Map Transmission and advanced image processing Techniques

Enhanced Single Remote Sensing Image Dehazing via Vision Tra...

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Intelligent Systems for Cybersecurity (ISCS), International conference on

作者： Sivasubramanyam Medasani B. Shireesha B. Hemanth Reddy C.Charan Teja E. Venkata Sainath Chowdary School of Engineering Electronics and Communication Engineering Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Dept. of Electronics and Communications Engineering (ECE) Erstwhile Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

- This research provides an improved single remote sensing picture dehazing method using sophisticated image processing techniques and Vision Transformer with Silencing Map Transmission. The dehazing output generation, morphological analysis, dark channel prior, silencing map transmission, recurrent neural networks (RNN), and input image processing are some of the components that are included in the suggested method. The objective is to far outperform current techniques in terms of dehazed image quality. The initial action in the process is getting ready the input image, after which morphological analysis is performed to determine the properties of the haze. The key characteristics of the haze are retrieved by using the dark channel before. Transmitting silencing maps improves our comprehension of scene depth, which is important for precise dehazing. RNN is used to learn from sequential data and propagate information effectively. Clearer and more aesthetically pleasing photos are produced by the dehazing output, which is generated based on the learned attributes. Mean Squared Error (MSE), Peak signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM), and Time of Execution (TOE) are among the metrics used to assess the efficacy of the suggested approach. The supremacy of the suggested strategy is shown in contrast to current approaches. The trial results demonstrate considerable improvements in PSNR, SSIM, and TOE measures across a variety of test images. In particular, the suggested approach outperforms current methods in terms of PSNR improvements of up to 4.23 dB and TOE reductions of up to 2.58 seconds. To sum up, the enhanced single remote sensing picture dehazing approach that has been described provides significant improvements in both image quality and processing efficiency. As such, it is a promising solution that might be used for a variety of remote sensing applications.

关键词： image quality Recurrent neural networks PSNR image processing Current measurement Measurement uncertainty Transformers

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Cross-Band Fusion for Multispectral Scene Classification

Cross-Band Fusion for Multispectral Scene Classification

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

作者： Jin Li Yu Liu Zhizhuo Jiang Xueqian Wang Bolun Zheng Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Electronic Engineering Tsinghua University Beijing China School of Automation Hangzhou Dianzi University Hangzhou China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

With the daily acquisition of vast amounts of remote sensing images by satellites, effectively utilizing these images for Earth observation has become a significant research focus. Multispectral image scene classification plays a fundamental role in advancing the understanding of remote sensing imagery. Existing methods for multispectral image scene classification do not sufficiently explore the interrelationships between different bands, which limits the performance of models in this task. Unlike these, we propose a cross-band fusion method for multispectral scene classification. Specifically, we employ two appropriate visual backbones to extract band-level visual features from different bands, followed by a cross-band fusion module to fuse the features from various bands adaptively. This approach fully explores the complementary information between different bands, achieving enhanced accuracy in multispectral scene classification. Extensive experiments on a commonly used dataset BigEarthNet validate that our proposed cross-band fusion method outperforms state-of-the-art (SOTA) methods.

关键词： Earth Scene classification Visualization Adaptation models Satellites Fuses Feature extraction Data processing Sensors remote sensing

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A Water Flare Avoidance Method of the Agile Commercial Optical remote sensing Satellite image Based on Observation Geometry Data Optimization 12th

A Water Flare Avoidance Method of the Agile Commercial Optic...

引用

12th International conference on signal and Information processing, Networking and Computers, ICSINC 2024

作者： Deng, Chao Zeng, Yong Zhang, Shuai Hao, Dengcheng He, Huaying China Centre for Resources Satellite Data and Application Beijing100094 China

ISBN: (纸本)9789819649662

When commercial remote sensing satellites image water areas, if the satellite observation vector and the solar incident light vector satisfy the condition of specular or near-specular reflection, it is effortless for images of water areas to have excessive brightness or even overflow to pollute the adjacent pixels. The current observation geometric model of water flare is only applicable to the ocean flare, and it could not dispel the inland water flare phenomenon in the commercial remote sensing agile satellite imaging process. This paper proposes a method of effectively avoiding the flare phenomenon in the water region of remote sensing satellite images. By analyzing the relationship between the observation vector of the satellite imaging view axis and the sun light irradiation vector, it provides an imaging angle reference for the commercial remote sensing agile satellite imaging of the earth. This method can effectively eliminate the flare phenomenon in the water area due to the specular or near-specular reflection and guarantee the quality of the commercial remote sensing satellite image of the water application. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Shape optimization

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UAV remote sensing image DEHAZING BASED ON SALIENCY GUIDED TWO-SCALE TRANSMISSION CORRECTION

UAV REMOTE SENSING IMAGE DEHAZING BASED ON SALIENCY GUIDED T...

引用

IEEE International conference on image processing (ICIP)

作者： Zhang, Kemeng Zheng, Ruohui Ma, Sijia Zhang, Libao Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

ISBN: (纸本)9781665441155

Current dehazing methods for unmanned aerial vehicle (UAV) remote sensing images often hold problems of texture detail loss in highlight regions and color distortions. This is mainly due to incorrect estimation of the transmission. In this paper, we propose a UAV dehazing method based on saliency guided two-scale transmission correction. Firstly, we propose a dehaze-driven frequency domain saliency model to detect highlight regions of hazy UAV images for better transmission correction. Secondly, we introduce a two-scale correction method to estimate the transmission map with more accurate texture details. We also introduce a suppression parameter to further suppress color distortions and energy over-reduction. Finally, the saliency map is taken as a weight of transmission correction to avoid texture detail loss and color distortions, especially in highlights. Compared with state-of-the-art methods, our method shows better visual effect and detail visibility, especially for UAV images with highlight regions.

关键词： Dehazing remote sensing unmanned aerial vehicle saliency detection transmission correction

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Mangrove Species Classification in Qi’ao Island Based on Gaofen-2 image and UAV LiDAR 1

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International conference on Communications, signal processing, and Systems, CSPS 2023

作者： Sun, Yuchao Wei, Zheng Gao, Yang Ren, Hongkai Chen, Qidong Dong, Di Hu, Ping South China Sea Institute of Planning and Environmental Research State Oceanic Administration Guangzhou510300 China Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural Resources Guangzhou510300 China Zhuhai519082 China

ISBN: (数字)9789819975051

ISBN: (纸本)9789819975396

Mangrove species classification is of great significance to the study of mangrove community structure and biodiversity. Most researches use foreign high-resolution remote sensing images or UAV images for mangrove species classification. In order to improve classification accuracy, LiDAR and hyper-spectral data are often used to assist classification. In this paper, based on the Gaofen-2 image and the CHM data obtained by the UAV Lidar, the mangroves in Qi'ao Island, Zhuhai are classified among species by using the random forest classification method. The classified species include 5 types of true mangroves, 3 types of semi mangroves, Phragmites australis and non-vegetation. The results show that the use of Gaofen-2 image can only effectively distinguish the Sonneratia apetala, Acrostichum aureum and non-vegetation, the accuracy of distinguishing other mangrove species is not ideal;After Gaofen-2 image fusion of CHM data, the classification accuracy of each mangrove species has been significantly improved, with the overall classification accuracy reaching 91.44%, which verifies the effectiveness of Gaofen-2 image fusion of external data in mangrove species classification research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image fusion

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End-to-end Multi-source image Phase Retrieval 3

End-to-end Multi-source Image Phase Retrieval

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3rd IEEE International conference on Computer Systems, ICCS 2023

作者： Zhao, Nan Song, Tianyang Guo, Yina Taiyuan University of Science and Technology Shanxi China

ISBN: (纸本)9798350313666

The phase information of multi-source hybrid images is prone to confusion during image propagation. Since traditional phase retrieval algorithms require a large number of assumptions that cannot be executed by obtaining amplitude, which makes phase retrieval of multi-source mixed images with phase distortion challenging. To address these phase retrieval problems, inspired by dual learning, we propose an end-to-end phase retrieval algorithm based on Multi-source image Phase Retrieval via dual Generative Adversarial Networks (MPRDGAN). By introducing the Least Square Error in the loss function of the network, the algorithm improves the performance of the model to prevent overfitting and ensures the phase retrieval of multi-source mixed images. Experimental results show that the MPRDGAN algorithm reduces the image restoration error, so that the correlation of restored images can reach more than 94.5%, which is widely used in various images such as face and remote sensing. © 2023 IEEE.

关键词： Generative adversarial networks

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