2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)最新文献_第4页

Research on Automatic Generation and Data Organization Method of Control Points 控制点自动生成与数据组织方法研究

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486252

Lai Guangling, Z. Yongsheng, Tong Xiaochong, Li Kai, Ding Lu

{"title":"Research on Automatic Generation and Data Organization Method of Control Points","authors":"Lai Guangling, Z. Yongsheng, Tong Xiaochong, Li Kai, Ding Lu","doi":"10.1109/PRRS.2018.8486252","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486252","url":null,"abstract":"High precision control points are indispensable for the improvement of geometric positioning accuracy of aerial and space images. At present, most control points need to be installed manually, and which obtained in this way are fixed to a specific area and have high installation and maintenance cost. Satellites can only correct their orbit and attitude in real time when they pass through the area with control points. Therefore, setting up control points by this way has poor flexibility and is not conducive to the improvement of satellite positioning accuracy. In order to solve this problem, an automatic control point generation algorithm based on natural ground object automatic recognition and detection is proposed. First, typical ground objects such as playground and road intersection are automatically identified by YOLO algorithm, and feature extraction is carried out by classic SIFT feature extraction operator on the basis of recognition. Then, the feature extraction results, along with the target attribute, location and other information are stored in the agreed format. Finally, the data of control points are organized by the multi-scale integer coding method based on quadruplication to improve the efficiency of data storage and access. This method can make full use of high precision surveying and mapping satellite image data and set up control points around the world. Satellites can correct their orbit and attitude at any time according to their needs, and can greatly improve the positioning accuracy of images.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133833920","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: 1

A Method of Building Extraction Using Object Based Analysis of High Resolution Remote Sensing Images 基于目标分析的高分辨率遥感影像建筑物提取方法

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486404

Wang Yan, Tao Zui, Lyu Fenghua

引用次数: 0

An Elegant End-to-End Fully Convolutional Network (E3FCN) for Green Tide Detection Using MODIS Data 基于MODIS数据的优雅端到端全卷积网络(E3FCN)绿潮检测

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486160

Haoyu Yin, Yingjian Liu, Qiang Chen

{"title":"An Elegant End-to-End Fully Convolutional Network (E3FCN) for Green Tide Detection Using MODIS Data","authors":"Haoyu Yin, Yingjian Liu, Qiang Chen","doi":"10.1109/PRRS.2018.8486160","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486160","url":null,"abstract":"Using remote sensing (RS) data to monitor the onset, proliferation and decline of green tide (GT) has great significance for disaster warning, trend prediction and decision-making support. However, remote sensing images vary under different observing conditions, which bring big challenges to detection missions. This paper proposes an accurate green tide detection method based on an Elegant End-to-End Fully Convolutional Network (E3FCN) using Moderate Resolution Imaging Spectroradiometer (MODIS) data. In preprocessing, RS images are firstly separated into subimages by a sliding window. To detect GT pixels more efficiently, the original Fully Convolutional Neural Network (FCN) architecture is modified into E3FCN, which can be trained end-to-end. The E3FCN model can be divided into two parts, contracting path and expanding path. The contracting path aims to extract high-level features and the expanding path aims to provide a pixel-level prediction by using a skip technique. The prediction result of whole image is generated by merging the prediction results of subimages, which can also improve the final performance. Experiment results show that the average precision of E3FCN on the whole data sets is 98.06%, compared to 73.27% of Support Vector Regression (SVR), 71.75% of Normalized Difference Vegetation Index (NDVI), and 64.41% of Enhanced Vegetation Index (EVI).","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133490425","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: 3

Developing Process Detection of Red Tide Based on Multi-Temporal GOCI Images 基于多时相GOCI图像的赤潮过程检测研究

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486244

Zhang Feng, Yang Xuying, Sun Xiaoxiao, Du Zhenhong, L. Renyi

引用次数: 3

An Automatic Image Enhancement Method Based on the Improved HCTLS 一种基于改进hctl的图像自动增强方法

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486212

Junyu Chen, Jiahang Liu, Chenghu Zhou, F. Zhu, Tieqiao Chen, Hang Zhang

{"title":"An Automatic Image Enhancement Method Based on the Improved HCTLS","authors":"Junyu Chen, Jiahang Liu, Chenghu Zhou, F. Zhu, Tieqiao Chen, Hang Zhang","doi":"10.1109/PRRS.2018.8486212","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486212","url":null,"abstract":"Remote sensing images often suffer low contrast, and the efficiency and robustness of contrast enhancement for remote sensing images is still a challenge. To meet with the requirements of applications, Liu et al recently proposed a self-adaptive contrast enhancement method (HCTLS) based on the histogram compacting transform (HCT). In this method, some gray levels on which the frequency is smaller than a certain reference, will merged into their adjacent levels for a compact level distribution. However, if the merged levels whose corresponding pixels in some connected regions, local contrast of these connected regions will decrease, even disappear. In this paper, an improved enhancement method (DPHCT) for remote sensing image based on the HCTLS is presented for preserving more the local detail and contrast. Firstly, extracting the connected regions from the enhanced result by HCT where the local contrast is decreased or disappeared. These connected regions are decomposed into the inner regions and the boundary regions adaptively. Then, construct pixel values by using the unified brightness function to maintain the contrast for the connected regions inside. At the same time eliminate stitching lines by using a weighted fusion spliced algorithm to eliminate the problem of borders outstanding in result of intensity roughness. Finally, the image is normalized into [0, 255] by linear stretch. Experimental results indicate that the proposed algorithm not only can enhance the global contrast but also can preserve local contrast and details.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122099348","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: 1

A Method of Interactively Extracting Region Objects from High-Resolution Remote Sensing Image Based on Full Connection CRF 基于全连通CRF的高分辨率遥感图像区域目标交互式提取方法

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486175

Zhang Chun-sen, Yu Zhen, Hu Yan

引用次数: 2

Multi-Branch Regression Network For Building Classification Using Remote Sensing Images 基于多分支回归网络的遥感影像建筑分类

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486177

Yuanyuan Gui, Xiang Li, Wei Li, Anzhi Yue

引用次数: 2

High-precision Centroid Extraction and PSF Calculation on Remote Sensing Image of Point Source Array 点源阵列遥感图像高精度质心提取与PSF计算

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486240

Li Kai, Z. Yongsheng, Z. Zhenchao, Xu Lin

{"title":"High-precision Centroid Extraction and PSF Calculation on Remote Sensing Image of Point Source Array","authors":"Li Kai, Z. Yongsheng, Z. Zhenchao, Xu Lin","doi":"10.1109/PRRS.2018.8486240","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486240","url":null,"abstract":"The high-precision measurement of remote sensing image geometric and radiometric information is an important basis for remote sensing image geometric and radiometric processing. Based on the theory of image degradation, this paper describes the method of obtaining simulated degradation image of point source array using prior information. Then the shortcomings of traditional Point Spread Function (PSF) parameter solving methods are analyzed, and a new algorithm for PSF parameter solving is proposed on this basis. Experimental results show that the accuracy of geometric center of point source and full-width half-maximum width (FWHM) of PSF obtained by the proposed method from simulated degradation image are better than the traditional algorithms. When the SNR is 40dB, the RMSE of the geometrical position of the point source obtained by proposed algorithm is only 0.01 pixels; the RMSE of FWHM of PSF is only 0.03 pixels. Experimental results further show that the use of the multiphase point source arrays can effectively improve the accuracy of PSF parameter. This paper demonstrates that point source can provide both high precision geometry and radiation information for remote sensing images, and will potentially be an ideal tool for joint geometric and radiometric calibration.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131916184","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: 0

Ship Detection by Modified RetinaNet 基于改进retanet的船舶检测

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486308

Yingying Wang, Wei Li, Xiang Li, Xu Sun

{"title":"Ship Detection by Modified RetinaNet","authors":"Yingying Wang, Wei Li, Xiang Li, Xu Sun","doi":"10.1109/PRRS.2018.8486308","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486308","url":null,"abstract":"Ship detection in optical remote sensing imagery has been a hot topic in recent years and achieved promising performance. However, there are still several problems in detecting ships with various sizes. The key objective of all scales precise positioning is to obtain a high resolution feature map while having a high semantic characteristic information. Based on this idea, a modified RetinaNet (M-RetinaNet) is proposed to build dense connections between shallow and deep feature maps, which aims at solving problems resulting from different sizes of ships. It consists of a baseline residual network and a modified multi-scale network. The modified multi-scale network includes a top-down pathway and a bottom-up pathway, both of which build on the multi-scale base network. The benefits of this model are two folds: first, it can generate feature maps with high semantic information at each layer by introducing dense lateral connections from deep to shallow; second, it maintains high spatial resolution in deep layers. Comprehensive evaluations on a ship dataset and comparison with several state-of-the-art approaches demonstrate the effectiveness of the proposed network.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126147406","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

SAR Image Matching Area Selection Based on Actual Flight Real-Time Image 基于实际飞行实时图像的SAR图像匹配区域选择

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486416

Wang Jianmei, Wang Zhong, Zhang Shaoming, F. Tiantian, Dong Jihui

引用次数: 1