2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP)最新文献_第3页

High resolution remote sensing image change detection based on law of cosines with box-whisker plot 基于盒须图余弦定律的高分辨率遥感图像变化检测

2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP) Pub Date : 2017-05-01 DOI: 10.1109/RSIP.2017.7958805

Chunsen Zhang, Guojun Li, W. Cui

引用次数: 3

Low-rank matrix decomposition with a spectral-spatial regularization for change detection in hyperspectral imagery 基于光谱空间正则化的低秩矩阵分解高光谱图像变化检测

2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP) Pub Date : 2017-05-01 DOI: 10.1109/RSIP.2017.7958816

Zhao Chen, Muhammad Sohail, Bin Wang

引用次数: 2

A water extraction method based on airborne hyperspectral images in highly complex urban area 一种基于航空高光谱图像的高度复杂城区水体提取方法

2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP) Pub Date : 2017-05-01 DOI: 10.1109/RSIP.2017.7958812

Xin Luo, Huan Xie, X. Tong, Haiyan Pan

{"title":"A water extraction method based on airborne hyperspectral images in highly complex urban area","authors":"Xin Luo, Huan Xie, X. Tong, Haiyan Pan","doi":"10.1109/RSIP.2017.7958812","DOIUrl":"https://doi.org/10.1109/RSIP.2017.7958812","url":null,"abstract":"Water bodies are a fundamental element of urban ecosystems, and water mapping is critical for urban and landscape planning and management. Remote sensing has increasingly been used for water mapping in rural areas; especially, hyperspectral remote sensing image characterized with rich spectrum information provide greater potential for high-accuracy land cover classiflcation, however, the hundreds of bands contained in the image also poses a huge burden on data processing. In this study, aims for water extraction in the densely built urban area, we proposed a fast water extraction method based on spectral analysis of the hyperspectral images. The performance of the new method performs well especially for the extraction of water surface which casts many building shadows. In comparison with the normalized difference water index (NDWI) and K-means classifier, new method obtains significantly higher accuracy than that of NDWI and K-means. Therefore, new method can be used for extracting water with high accuracy, especially in urban areas where shadow caused by high buildings is an important source of classification error.","PeriodicalId":262222,"journal":{"name":"2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133736984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Multi-temporal PolSAR crops classification using polarimetric-feature-driven deep convolutional neural network 基于极化特征驱动的深度卷积神经网络的PolSAR作物多时相分类

2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP) Pub Date : 2017-05-01 DOI: 10.1109/RSIP.2017.7958818

Siwei Chen, Chensong Tao

引用次数: 12

Integrating H-A-α with fully convolutional networks for fully PolSAR classification 基于H-A-α与全卷积网络的全PolSAR分类

2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP) Pub Date : 2017-05-01 DOI: 10.1109/RSIP.2017.7958799

Yuanyuan Wang, Chao Wang, Hong Zhang

引用次数: 13

A novel target tracking method based on scale-invariant feature transform in imagery 一种基于图像尺度不变特征变换的目标跟踪新方法

2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP) Pub Date : 2017-05-01 DOI: 10.1109/RSIP.2017.7958801

Huang Qinglong, Yun Zhang, Ling Hongbo, Wangbin, Feng Tianjiao

引用次数: 0

Efficient solution of large-scale domestic hyperspectral data processing and geological application 国内大规模高光谱数据处理及地质应用的高效解决方案

2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP) Pub Date : 2017-05-01 DOI: 10.1109/RSIP.2017.7970774

Junchuan Yu, Bokun Yan

引用次数: 3

SAR image target recognition via deep Bayesian generative network 基于深度贝叶斯生成网络的SAR图像目标识别

2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP) Pub Date : 2017-05-01 DOI: 10.1109/RSIP.2017.7958814

D. Guo, Bo Chen

引用次数: 6

A novel multi-target track initiation method based on convolution neural network 一种基于卷积神经网络的多目标航迹起始方法

2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP) Pub Date : 2017-05-01 DOI: 10.1109/RSIP.2017.7958813

Yun Zhang, Shiyu Yang, Hongbo Li, Huilin Mu

{"title":"A novel multi-target track initiation method based on convolution neural network","authors":"Yun Zhang, Shiyu Yang, Hongbo Li, Huilin Mu","doi":"10.1109/RSIP.2017.7958813","DOIUrl":"https://doi.org/10.1109/RSIP.2017.7958813","url":null,"abstract":"This paper addresses the problem of track initiation for multi-target in different motion forms and in complicated clutter background. The method proposed combines the traditional logic-based method and convolution neural network. The logic-based method is used mainly to generate a set of track proposals, which is computed by the convolution neural network to extract features in data domain. In this paper, softmax at the end of the convolution neural network is substituted by a one-dimensional two-class classifier for the output layer of the convolution neural network is designed to output a one-dimensional value. There are two key insights in this method: (1) the classification problem has been transformed into target tracking problem on the condition that the set of track proposals is found. (2) the convolution neural network is firstly used in data domain to mine and augment high-level features that make classification more easily. The simulation experiments have shown that this method performs much better than modified Hough transform which is used to initialize tracks traditionally, especially when the targets are maneuver. In the experiments based on real data, this method is proved to be adaptive enough to initialize tracks whose data comes from different radars.","PeriodicalId":262222,"journal":{"name":"2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125702049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7