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浏览/检索结果: 共10条，第1-10条 帮助

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Research on Desertification Monitoring and Vegetation Refinement Extraction Methods Based on the Synergy of Multisource Remote Sensing Imagery 期刊论文  OAI收割
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2025, 卷号: 63, 页码: 4404819
作者:  Song, Zhenqi;  Lu, Yuefeng;  Yuan, Jinhui;  Lu, Miao;  Qin, Yong
  |  收藏  |  浏览/下载：10/0  |  提交时间：2025/04/21
CLDRNet: A Difference Refinement Network Based on Category Context Learning for Remote Sensing Image Change Detection 期刊论文  OAI收割
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2024, 卷号: 17, 页码: 2133-2148
作者:  Wan, Ling;  Tian, Ye;  Kang, Wenchao;  Ma, Lei
  |  收藏  |  浏览/下载：19/0  |  提交时间：2024/02/20
A Priori Land Surface Reflectance Synergized With Multiscale Features Convolution Neural Network for MODIS Imagery Cloud Detection 期刊论文  OAI收割
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2023, 卷号: 16, 页码: 3294-3308
作者:  Ma, Nan;  Sun, Lin;  Zhou, Chenghu;  He, Yawen;  Dong, Chuanxiang
  |  收藏  |  浏览/下载：11/0  |  提交时间：2023/10/09
Multibranch Feature Difference Learning Network for Cross-Spectral Image Patch Matching 期刊论文  OAI收割
IEEE Transactions on Geoscience and Remote Sensing, 2022, 卷号: 60, 页码: 1-15
作者:  Yu C(余创);  Liu YP(刘云鹏);  Li CX(李晨曦);  Qi L(亓琳);  Xia X(夏鑫)
  |  收藏  |  浏览/下载：43/0  |  提交时间：2022/06/07
Context and Difference Enhancement Network for Change Detection 期刊论文  OAI收割
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2022, 卷号: 15
作者:  Song, Dawei;  Dong, Yongsheng;  Li, Xuelong
  |  收藏  |  浏览/下载：23/0  |  提交时间：2022/12/02
An underlying clock in the extreme flip-flop state transitions of the black hole transient Swift J1658.2-4242 期刊论文  OAI收割
Astronomy and Astrophysics, 2020, 卷号: 641, 页码: A101
作者:  HXMT
  |  收藏  |  浏览/下载：58/0  |  提交时间：2022/02/08
accretion  accretion disks  black hole physics  X-rays: binaries  time  Astrophysics - High Energy Astrophysical Phenomena  Abstract:
Aims: Flip-flops are top-hat-like X-ray flux variations, which have been observed in some transient accreting black hole binary systems, and feature simultaneous changes in the spectral hardness and the power density spectrum (PDS). They occur at a crucial time in the evolution of these systems, when the accretion disc emission starts to dominate over coronal emission. Flip-flops remain a poorly understood phenomenon, so we aim to thoroughly investigate them in a system featuring several such transitions.
Methods: Within the multitude of observations of Swift J1658.2-4242 during its outburst in early 2018, we detected 15 flip-flops, enabling a detailed analysis of their individual properties and the differences between them. We present observations by XMM-Newton, NuSTAR, Astrosat, Swift, Insight-HXMT, INTEGRAL, and ATCA. We analysed their light curves, searched for periodicities, computed their PDSs, and fitted their X-ray spectra, to investigate the source behaviour during flip-flop transitions and how the interval featuring flip-flops differs from the rest of the outburst.
Results: The flip-flops of Swift J1658.2-4242 are of an extreme variety, exhibiting flux differences of up to 77% within 100 s, which is much larger than what has been seen previously. We observed radical changes in the PDS simultaneous with the sharp flux variations, featuring transitions between the quasi-periodic oscillation types C and A, which have never been observed before. Changes in the PDS are delayed, but more rapid than changes in the light curve. Flip-flops occur in two intervals within the outburst, separated by about two weeks in which these phenomena were not seen. Transitions between the two flip-flop states occurred at random integer multiples of a fundamental period of 2.761 ks in the first interval and 2.61 ks in the second. Spectral analysis reveals the high and low flux flip-flop states to be very similar, but distinct from intervals lacking flip-flops. A change of the inner temperature of the accretion disc is responsible for most of the flux difference in the flip-flops. We also highlight the importance of correcting for the influence of the dust scattering halo on the X-ray spectra.  
An Automated Method for Extracting Rivers and Lakes from Landsat Imagery SCI/SSCI论文  OAI收割
2014
作者:  Feng M.
收藏  |  浏览/下载：33/0  |  提交时间：2014/12/24
Features extraction and matching of teeth image based on the SIFT algorithm (EI CONFERENCE) 会议论文  OAI收割
2012 2nd International Conference on Computer Application and System Modeling, ICCASM 2012, July 27, 2012 - July 29, 2012, Shenyang, China
作者:  Wang X.;  Wang X.;  Wang X.
收藏  |  浏览/下载：28/0  |  提交时间：2013/03/25
Using of SIFT algorithm in the image of teeth model  can detect the features of the teeth image effectively. In this approach  first  search over all scales and image locations by using a difference-of-Gaussian function to identify potential interest points that are invariant to scale and orientation. Second  select keypoints based on measures of their stability and a detailed model is fit to determine location and scale at each candidate location. Third  assign one or more orientations to each keypoint location based on local image gradient directions. Last  measure the local image gradients at the selected scale in the region around each keypoint. And then use the KNN algorithm to match the features. Through lots of experiments and comparing with other feature extraction methods  this method can detect the features of the teeth model effectively  and offer some available parameters for 3D reconstruction of the teeth model. the authors.  
Feature Coding via Vector Difference for Image Classification 会议论文  OAI收割
USA, 2012
作者:  Zhao, Xin;  Yu, Yinan;  Huang, Yongzhen;  Huang, Kaiqi;  Tan, Tieniu
  |  收藏  |  浏览/下载：13/0  |  提交时间：2016/12/30
Intelligent MRTD testing for thermal imaging system using ANN (EI CONFERENCE) 会议论文  OAI收割
ICO20: Remote Sensing and Infrared Devices and Systems, August 21, 2005 - August 26, 2005, Changchun, China
Sun J.; Ma D.
收藏  |  浏览/下载：26/0  |  提交时间：2013/03/25
The Minimum Resolvable Temperature Difference (MRTD) is the most widely accepted figure for describing the performance of a thermal imaging system. Many models have been proposed to predict it. The MRTD testing is a psychophysical task  for which biases are unavoidable. It requires laboratory conditions such as normal air condition and a constant temperature. It also needs expensive measuring equipments and takes a considerable period of time. Especially when measuring imagers of the same type  the test is time consuming. So an automated and intelligent measurement method should be discussed. This paper adopts the concept of automated MRTD testing using boundary contour system and fuzzy ARTMAP  but uses different methods. It describes an Automated MRTD Testing procedure basing on Back-Propagation Network. Firstly  we use frame grabber to capture the 4-bar target image data. Then according to image gray scale  we segment the image to get 4-bar place and extract feature vector representing the image characteristic and human detection ability. These feature sets  along with known target visibility  are used to train the ANN (Artificial Neural Networks). Actually it is a nonlinear classification (of input dimensions) of the image series using ANN. Our task is to justify if image is resolvable or uncertainty. Then the trained ANN will emulate observer performance in determining MRTD. This method can reduce the uncertainties between observers and long time dependent factors by standardization. This paper will introduce the feature extraction algorithm  demonstrate the feasibility of the whole process and give the accuracy of MRTD measurement.