2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP)最新文献

PolSAR image classification using discriminative clustering 基于判别聚类的PolSAR图像分类

2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP) Pub Date : 2017-05-18 DOI: 10.1109/RSIP.2017.7958798

Haixia Bi, Jian Sun, Zongben Xu

{"title":"PolSAR image classification using discriminative clustering","authors":"Haixia Bi, Jian Sun, Zongben Xu","doi":"10.1109/RSIP.2017.7958798","DOIUrl":"https://doi.org/10.1109/RSIP.2017.7958798","url":null,"abstract":"This paper presents a novel unsupervised image classification method for polarimetric synthetic aperture radar (PolSAR) data. The proposed method is based on a discriminative clustering framework that explicitly relies on a discriminative supervised classification technique to perform unsupervised clustering. To implement this idea, we design an energy function for unsupervised PolSAR image classification by combining a supervised softmax regression model with a Markov Random Field (MRF) smoothness constraint. In this model, both the pixel-wise class labels and classifiers are taken as unknown variables to be optimized. Starting from the initialized class labels generated by Cloude-Pottier decomposition and K-Wishart distribution hypothesis, we iteratively optimize the classifiers and class labels by alternately minimizing the energy function w.r.t. them. Finally, the optimized class labels are taken as the classification result, and the classifiers for different classes are also derived as a side effect. We apply this approach to real PolSAR benchmark data. Extensive experiments justify that our approach can effectively classify the PolSAR image in an unsupervised way, and produce higher accuracies than the compared state-of-the-art methods.","PeriodicalId":262222,"journal":{"name":"2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122855545","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: 5

An enhanced deep convolutional neural network for densely packed objects detection in remote sensing images 基于深度卷积神经网络的遥感图像密集目标检测

2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP) Pub Date : 2017-05-18 DOI: 10.1109/RSIP.2017.7958800

Zhipeng Deng, Lin Lei, Hao Sun, H. Zou, Shilin Zhou, Juanping Zhao

引用次数: 24

Airborne Ka-band digital beamforming SAR system and flight test 机载ka波段数字波束形成SAR系统及飞行试验

2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP) Pub Date : 2017-05-18 DOI: 10.1109/RSIP.2017.7958791

Hui Wang, Shoulun Dai, Shichao Zheng

引用次数: 3

Fast vehicle detection in UAV images 无人机图像中的快速车辆检测

2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP) Pub Date : 2017-05-18 DOI: 10.1109/RSIP.2017.7958795

Tianyu Tang, Zhipeng Deng, Shilin Zhou, Lin Lei, H. Zou

{"title":"Fast vehicle detection in UAV images","authors":"Tianyu Tang, Zhipeng Deng, Shilin Zhou, Lin Lei, H. Zou","doi":"10.1109/RSIP.2017.7958795","DOIUrl":"https://doi.org/10.1109/RSIP.2017.7958795","url":null,"abstract":"Fast and accurate vehicle detection in unmanned aerial vehicle (UAV) images remains a challenge, due to its very high spatial resolution and very few annotations. Although numerous vehicle detection methods exist, most of them cannot achieve real-time detection for different scenes. Recently, deep learning algorithms has achieved fantastic detection performance in computer vision, especially regression based convolutional neural networks YOLOv2. It's good both at accuracy and speed, outperforming other state-of-the-art detection methods. This paper for the first time aims to investigate the use of YOLOv2 for vehicle detection in UAV images, as well as to explore the new method for data annotation. Our method starts with image annotation and data augmentation. CSK tracking method is used to help annotate vehicles in images captured from simple scenes. Subsequently, a regression based single convolutional neural network YOLOv2 is used to detect vehicles in UAV images. To evaluate our method, UAV video images were taken over several urban areas, and experiments were conducted on this dataset and Stanford Drone dataset. The experimental results have proven that our data preparation strategy is useful, and YOLOv2 is effective for real-time vehicle detection of UAV video images.","PeriodicalId":262222,"journal":{"name":"2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129443809","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: 55

A modified faster R-CNN based on CFAR algorithm for SAR ship detection 基于CFAR算法的改进更快R-CNN SAR舰船检测

2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP) Pub Date : 2017-05-18 DOI: 10.1109/RSIP.2017.7958815

Miao Kang, Xiangguang Leng, Zhao Lin, K. Ji

引用次数: 168

Monitoring ghost cities at prefecture level from multi-source remote sensing data 地级鬼城多源遥感监测

2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP) Pub Date : 2017-05-18 DOI: 10.1109/RSIP.2017.7958810

Xiaolong Ma, Zhaoting Ma, X. Tong, Sicong Liu

{"title":"Monitoring ghost cities at prefecture level from multi-source remote sensing data","authors":"Xiaolong Ma, Zhaoting Ma, X. Tong, Sicong Liu","doi":"10.1109/RSIP.2017.7958810","DOIUrl":"https://doi.org/10.1109/RSIP.2017.7958810","url":null,"abstract":"Monitoring urban spatial information is important to hold the process of urbanization for keeping balance between the human activity and the environment. To promote the application extent of the remote sensing technology in the topic of ghost cities, an effective method was proposed to monitor and evaluate “ghost city” phenomenon in the prefecture level city of China by taking advantage of multi-source remote sensing datasets, namely the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime light data and other auxiliary data such as Landsat images and the Land-cover/use datasets. Based on several indexes related urban expansion and landscape pattern, experiments were conducted by using the proposed approach in Weihai, as classified by statistics and Landsat images. Compared with the Optimized-Sample-Selection (OSS) method, the proposed method achieved better performance with respect to relative less errors and better visual display of the spatial dynamics of urban expansion in Weihai during the year of 2000–2010, so as to reveal the specific characteristics of urban expansion patterns in those periods.","PeriodicalId":262222,"journal":{"name":"2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126692229","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

The development of deep learning in synthetic aperture radar imagery 合成孔径雷达图像中深度学习的发展

2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP) Pub Date : 2017-05-18 DOI: 10.1109/RSIP.2017.7958802

C. Schwegmann, W. Kleynhans, B. P. Salmon

引用次数: 6

Change detection of SAR images based on supervised contractive autoencoders and fuzzy clustering 基于监督压缩自编码器和模糊聚类的SAR图像变化检测

2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP) Pub Date : 2017-05-18 DOI: 10.1109/RSIP.2017.7958819

Jie Geng, Hongyu Wang, Jianchao Fan, Xiaorui Ma

引用次数: 20

3-D imaging of high-speed moving space target 高速运动空间目标的三维成像

2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP) Pub Date : 2017-05-18 DOI: 10.1109/RSIP.2017.7958797

Yu-Xue Sun, Ying Luo, Qun Zhang, Song Zhang

引用次数: 0

Hyperspectral image classification based on spectral-spatial feature extraction 基于光谱空间特征提取的高光谱图像分类

2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP) Pub Date : 2017-05-18 DOI: 10.1109/RSIP.2017.7958808

Zhen Ye, Li-ling Tan, Lin Bai

引用次数: 8