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Airborne high-resolution image motion target detection combined with hyperspectral features 5

Airborne high-resolution image motion target detection combi...

引用

2020 5th International conference on Intelligent Computing and signal processing, ICSP 2020

作者： Wang, Cailing Zhang, Yuchun Guo, Pu Xu, Jun Xi'an Shiyou University Xi'an710065 China Xi'an Aeronautical University Xi'an710089 China

Aiming at moving object detection in Airborne Hyperspectral remote sensing images under complex background, this paper proposes a background removal method based on Mixture Gauss Model. Firstly, in order to eliminate the influence of the dithering of airborne platform on remote sensing image, this paper uses affine transform-based image registration algorithm to achieve the image registration of 3D data. Then, this paper selects specific spectral images, establishes the Mixture Gauss background model, and subtracts the background to achieve the extraction of moving objects. The experimental results show that the method can extract moving objects in Airborne Hyperspectral remote sensing images effectively. © 2019 Published under licence by IOP Publishing Ltd.

关键词： image registration

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Segmentation for High Spatial Resolution remote sensing images by Combining Quadtree with Minimum Spanning Tree

Segmentation for High Spatial Resolution Remote Sensing Imag...

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International conference on Optical Instruments and Technology - Optoelectronic Imaging/Spectroscopy and signal processing Technology

作者： He, Yongqiang Jiang, Jie Beihang Univ Sch Instrumentat Sci & Optoelect Engn Beijing Peoples R China

ISBN: (纸本)9781510636552

This paper presents a high spatial resolution remote sensing image segmentation method by combining quadtree with minimum spanning tree. Firstly, the improved quadtree segmentation algorithm is used to divide the image iteratively into many over-segmented objects, which greatly facilitates the selection of initial segmentation parameters. Then the improved Morton coding is used to construct the spatial index of the generated over-segmented object and form the region adjacency relation. Combine spectral and texture features, the similarity between adjacent regions is calculated and the region merging criterion is constructed. Based on the idea of minimum spanning tree, the over-segmented objects are merged to generate multiple minimum spanning trees. During that process, the number of minimum spanning trees can be controlled to obtain ideal segmentation results. Compared with two other segmentation algorithms, the method proposed in this paper is more convenient to select segmentation parameters and has certain improvement in segmentation accuracy and object integrity of segmentation results.

关键词： remote sensing image segmentation quadtree objects merging

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Super-Resolution Based and Topological Structure for Narrow Road Extraction from remote sensing image 8th

Super-Resolution Based and Topological Structure for Narrow ...

引用

8th International conference on Communications, signal processing, and Systems, CSPS 2019

作者： Zhang, Guoying Wang, Guoqiang Wang, Zhonghong Key Lab of Electronic and Communication Engineering Heilongjiang University No. 74 Xuefu Road Harbin China

ISBN: (纸本)9789811394089

It is difficult to extract a narrow road with a width of only a few pixels from a remote sensing image. In order to solve this problem, it is proposed to process the remote sensing image with super-resolution. This paper extends the details of narrow roads by using the Deep Convolutional Neural Network (DCNN) method. Next, some noise points or roads of error extraction are processed by topological structure. To verify performance of the experimental method, experimental research on open remote sensing image data set is carried out. The experimental result is to compare the original image, super-resolution image, and topology filtering. Experimental results demonstrate that the new method has better effectiveness and superiority over the original remote sensing image. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Onboard signal processing system of HAYABUSA2 28

Onboard signal processing system of HAYABUSA2

引用

conference on Infrared remote sensing and Instrumentation XXVIII held at SPIE Optical Engineering + Applications conference

作者： Hihara, Hiroki Sano, Junpei Takada, Jun Masuda, Tetsuya Okada, Tatsuaki Ogawa, Naoko Ootake, Hisashi Tsuda, Yuichi NEC Space Technol Ltd 1-10 Nisshin Cho Fuchu Tokyo 1838551 Japan NEC Corp Ltd Nakahara Ku 1753 Shimonumabe Kawasaki Kanagawa 2118666 Japan NEC Corp Ltd 1-10 Nisshin Cho Fuchu Tokyo 1838501 Japan Japan Aerosp Explorat Agcy JAXA Chuo Ku 3-1-1 Yoshino Dai Sagamihara Kanagawa 2525210 Japan

ISBN: (纸本)9781510638112;9781510638105

HAYABUSA2 asteroid probe has completed its mission successfully in the vicinity of asteroid Ryugu on November 13, 2019. It is on its way to the Earth now. Digital Electronics and Optical Navigation Camera (DE-ONC) was developed for scientific observation and real-time image recognition for optical navigation. The development process and its high-speed wire rate signal processing architecture of onboard electronics are explained in this lecture. Highly efficient lossless and lossy image compression algorithm were developed to send observed images through within the limited capacity of communication channels between the asteroid Ryugu and the Earth for scientific purposes. Onboard sensitivity and distortion correction functions for image sensors were also developed to improve compression ratio of images. High level synthesis technology was employed to implement the image recognition functions for optical navigation functions into limited numbers of space grade field programmable gate arrays (FPGAs) and to achieve wire rate signal processing speed. It must also satisfy high reliability and safety requirements of HAYABUSA2 missions. Functional distribution mode, standby redundancy mode and hot redundancy mode were realized with the same device configuration. Model based design was performed to satisfy these requirements. The onboard image processing unit of DE-ONC adopts a unified language processing system and a distributed memory model with reference to a parallel inference machine developed for the Fifth Generation Computer Systems aiming at artificial intelligence technology development. Its image processing module integrates a radiation hardened micro-controller unit (MCU) and FPGAs with the unified language processing system and the distributed object model.

关键词： Infrared sensor data compression image recognition optical navigation artificial intelligence Internet of Thing (IoT) SpaceWire digital development

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End-to-End Spectral-Temporal Fusion Using Convolutional Neural Network 1

引用

4th Mediterranean conference on Pattern Recognition and Artificial Intelligence, MedPRAI 2020

作者： Benzenati, Tayeb Kallel, Abdelaziz Kessentini, Yousri Digital Research Center of Sfax Sfax3021 Tunisia Advanced Technologies for Image and Signal Processing University of Sfax Sfax Tunisia MIRACL Laboratory University of Sfax Sfax Tunisia

ISBN: (数字)9783030718046

ISBN: (纸本)9783030718039

In the last few years, Earth Observation sensors received a large development, offering, therefore, various types of data with different temporal, spatial, spectral, and radiometric resolutions. However, due to physical and budget limitations, the acquisition of images with the best characteristics is not feasible. image fusion becomes a valuable technique to deal with some specific applications. In particular, the vegetation area, which needs a high spectral resolution and frequent coverage. In this paper, we present a novel fusion technique based on Convolutional Neural Networks (CNN) to combine two kinds of remote sensing data with different but complement spectral and temporal characteristics, to produce one high spectral and temporal resolution product. To the best of our knowledge, this is the first attempt to deal with the spectral-temporal fusion problem. The feasibility of the proposed method is evaluated via Sentinel-2 data. The experimental results show that the proposed technique can achieve substantial gains in terms of fusion performance. © 2021, Springer Nature Switzerland AG.

关键词： image fusion

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remote sensing image Enhancement Technology of UAV Based on Improved GAN 6th

Remote Sensing Image Enhancement Technology of UAV Based on ...

引用

6th conference on signal and Information processing, Networking and Computers, ICSINC 2019

作者： Wu, Guoqiang Ma, Xiangsen Huang, Kun Guo, Haolun Beijing Aerospace Unmanned Vehicles System Engineering Research Institute Beijing China

ISBN: (纸本)9789811541629

In view of the problem that the remote sensing image of UAV is greatly affected by the weather, this paper proposes a UAV remote sensing image enhancement technology based on improved GAN. Firstly, the noise feature is extracted by the noise feature extraction network based on convolutional neural network (CNN). Secondly, using the noise features extracted by CNNs, the generation of GAN for image enhancement is constructed. For the problem of large storage overhead, low computational efficiency and pixel block size limiting the size of the perceptual region, this paper introduces a full CNN model (FCN) as a generation network. Finally, a data set for training is constructed using image enhancement techniques based on damaged sample learning. The experimental results show that the enhanced algorithm in this paper shows excellent performance in remote sensing images of UAV. © 2020, Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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Skip Attention GAN for remote sensing image Synthesis

Skip Attention GAN for Remote Sensing Image Synthesis

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IEEE International conference on Acoustics, Speech and signal processing

作者： Kai Deng Kun Zhang Ping Yao Siyuan Cheng Peng He University of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences

High-quality remote sensing images are difficult to obtain due to limited conditions and high cost for data acquisition. With the development of machine vision and deep learning, some image generation methods (e.g., GANs) are introduced into this field, but it's still hard to generate images with good texture details and structural dependencies. We establish Skip Attention Mechanism to deal with this problem, which learns dependencies between local points on low-resolution feature maps, and then upsample the attention map and combine it with high-resolution feature maps. With this method, long-range dependencies learned from low-resolution are used for generating remote sensing images with more structural details. We name this method as Skip Attention GAN, which is the first method applying cross-scale attention mechanism for unsupervised remote sensing image generation. Experiments show that our method outperforms previous methods under several metrics. Visual and ablation results of attention layers show that Skip Attention has learned long-distance structural dependencies between similar targets.

关键词： Measurement Deep learning Visualization image synthesis Machine vision Generative adversarial networks Sensors

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WHY TO MODEL remote sensing MEASUREMENTS IN 3D? RECENT ADVANCES IN DART: ATMOSPHERE, TOPOGRAPHY, LARGE LANDSCAPE, CHLOROPHYLL FLUORESCENCE AND SATELLITE image INVERSION 5

WHY TO MODEL REMOTE SENSING MEASUREMENTS IN 3D? RECENT ADVAN...

引用

5th International conference on Advanced Technologies for signal and image processing (ATSIP)

作者： Gastellu-Etchegorry, J. P. Wang, Y. Regaieg, O. Yin, T. Malenovsky, Z. Zhen, Z. Yang, X. Tao, Z. Landier, L. Al Bitar, A. Deschamps Lauret, N. Guilleux, J. Chavanon, E. Cao, B. Qi, J. Kallel, A. Mitraka, Z. Chrysoulakis, N. Cook, B. Morton, D. Univ Toulouse CNRS CNES CESBIO UPSIRD Toulouse France Univ Maryland Earth Syst Sci Interdisciplinary Ctr College Pk MD 20740 USA Univ Tasmania Sch Technol Environm & Design Hobart Tas Australia Jilin Univ Coll Geoexplorat Sci & Technol Changchun Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing Peoples R China East China Normal Univ Key Lab Geog Informat Sci Shanghai Peoples R China Beijing Forestry Univ Beijing Peoples R China CRNS ATMS Sfax Tunisia CNES 18 Av Belin Toulouse France Fdn Res & Technol Hellas FORTH Iraklion Greece NASA Goddard Space Flight Ctr Greenbelt MD 20771 USA

ISBN: (纸本)9781728175133

remote sensing (RS) dedicated to the study of land surfaces benefits from more and more advanced sensors. However, the interpretation of RS data is often is often inaccurate due to the complexity of the observed land surfaces. Therefore, RS models, in particular physical models, that simulate RS observations of the three-dimensional (3D) landscapes are critical to correctly interpret RS data. DART is one of the most comprehensive 3D models of Earth-atmosphere optical radiative transfer (RT), from ultraviolet (UV) to thermal infrared (TIR). It simulates the optical signal of proximal, aerial and satellite imaging spectrometers and laser scanners, the 3D RB and solar-induced chlorophyll fluorescence (SIF) signal, for any urban or natural landscape and any experimental or instrument configuration. It is freely available for research and teaching activities (***). After illustrating three significant sources of inaccuracy in RS interpretation, five recent DART advances are presented: RT in the atmosphere and topography, fast RS image simulation of large landscapes, SIF modelling, and satellite image inversion.

关键词： DART atmosphere topography SIF inversion modelling remote sensing

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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...

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IEEE conference on Aerospace

作者： M. Abo Elenean A.T. Hafez A.K. Helmy Fawzy ElTohamy Ahmed Azouz Aircraft Electric System Department Military Technical College Cairo Egypt National authoritie of remote sensing and space Sciences Cairo Egypt Military Technical College Cairo Egypt

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 classes. It 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 Cloude-Pottier classification method. The results demonstrate that the proposed algorithm accuracy reaches (88.6 %) with error (0.114), advances over the traditi

关键词： image segmentation Visualization Urban areas Clustering algorithms Time measurement Classification algorithms Matrix decomposition

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Spatial-Aware Probabilistic Collaborative Representation for Hyperspectral image Classification 26

Spatial-Aware Probabilistic Collaborative Representation for...

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conference on image and signal processing for remote sensing XXVI

作者： Shah, Chiranjibi Du, Qian Mississippi State Univ Dept Elect & Comp Engn Mississippi State MS 39762 USA

ISBN: (纸本)9781510638808

Collaborative representation classifier (CRC) is an efficient classifier for hyperspectral imagery. It represents a testing sample using labeled ones, and the testing sample is assigned to the class whose labeled samples yield the minimum representation error. The CRC allows all the samples to have equal chance to participate in the representation by imposing an L-2 norm minimization constraint. The solution has a closed form, offering computational convenience. Various techniques have been developed for further improvement of CRC-based classifiers, and probabilistic collaborative representation-based classifier (ProCRC) is one of techniques to enhance CRC by using maximum likelihood concept of testing sample that belongs to multiple classes. Taking into consideration for distance-weighted Tikhonov regularization, probabilistic collaborative representation-based classifier with Tikhonov regularization (ProCRT) can enhance the performance of the original ProCRC. In this paper, spatial regularization term is added in the objective function to incorporate spatial information, and the resulting spatial-aware ProCRC (SaProCRC) and spatial-aware ProCRT (SaProCRT) can offer even better classification accuracy with comparable computational cost.

关键词： hyperspectral imagery probabilistic collaborative representation spatial-aware probabilistic collaborative representation classification

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