2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)最新文献_第5页

Extracting Rural Residential Areas from High-Resolution Remote Sensing Images in the Coastal Area of Shandong, China Based on Fast Acquisition of Training Samples and Fully Convoluted Network 基于快速获取训练样本和全卷积网络的山东沿海高分辨率遥感影像农村居民点提取

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486368

Chen-Gui Lu, Xiaomei Yang, Zhihua Wang, Yueming Liu

{"title":"Extracting Rural Residential Areas from High-Resolution Remote Sensing Images in the Coastal Area of Shandong, China Based on Fast Acquisition of Training Samples and Fully Convoluted Network","authors":"Chen-Gui Lu, Xiaomei Yang, Zhihua Wang, Yueming Liu","doi":"10.1109/PRRS.2018.8486368","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486368","url":null,"abstract":"Automatic extraction of rural residential areas from high-resolution remote sensing images in large regions is a challenging task, because all kinds of background features, such as roads, green houses, and urban areas, must be excluded effectively by an extraction method. For the unsupervised methods of rural residential areas extraction, it is difficult to manually design features which are only sensitive to residential areas. At the same time, the supervised methods utilize training samples to obtain the discrimination between rural residential areas and the background features. However, manual labeling in large regions is tedious and time-consuming. The drawbacks of the existing methods for extracting rural residential areas limit their application in large regions. Therefore, we proposed a novel methodology for extracting rural residential areas in large regions based on fast acquisition of training samples and the fully convoluted network (FCN). A block-based method was proposed to extract rural residential areas rapidly and acquire training samples. Then, the large amount of training samples were used to train the FCN for rural residential area extraction. Finally, all ZY-3 satellite images in in the coastal area of Shandong, China were feed into the FCN, and the extraction result were obtained.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"1 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":"129951871","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: 2

Extraction of Altered Mineral from Remote Sensing Data in Gold Exploration Based on the Nonlinear Analysis Technology 基于非线性分析技术的金矿遥感蚀变矿物提取

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486248

Han Hai-hui, Wang Yilin, Zhang Zhuan, Ren Guang-li, Yang Min

引用次数: 1

Preliminary Investigation on Single Remote Sensing Image Inpainting Through a Modified GAN 基于改进GAN的单幅遥感图像涂装初探

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486163

S. Lou, Q. Fan, Feng Chen, Cheng Wang, Jonathan Li

{"title":"Preliminary Investigation on Single Remote Sensing Image Inpainting Through a Modified GAN","authors":"S. Lou, Q. Fan, Feng Chen, Cheng Wang, Jonathan Li","doi":"10.1109/PRRS.2018.8486163","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486163","url":null,"abstract":"Because of impacts resulted from sensor malfunction and clouds, there is usually a great deal of missing regions (pixels) in remotely sensed imagery. To make full use of the remotely sensed imagery affected, different algorithms for remote sensing images inpainting have been proposed. In this paper, an unsupervised convolutional neural network (CNN) context generate model was modified to recover the affected (or un-recorded) pixels in a single image without auxiliary information. Unlike existing nonparametric algorithms in which pixels located in surrounding region are used to estimate the unrecorded pixel, the proposed method directly generates content based on a neural network. To ensure recovered results with high quality, a modified reconstruction loss was used in training the model, which included structural similarity index (SSIM) loss and Ll loss. Comparison of the proposed model with bilinear interpolation was indicated through relative error. The performances of two methods in scenes with different complexity were discussed further. Results show that the proposed model performed better in simple scenes (i.e., with relative homogeneity), compared to the traditional method. Meanwhile, the corrupted images of channel blue were recovered more accurately, compared to the corrupted images of other channels (i.e., channel green and channel red). The relationship between scene complexities and channels shows that same scene has different complexities in different channels. The scene complexity presents significant correlation with recovered results, high complexity images are always accompanied by poor recovered results. It suggests that the recovering accuracy depends on scene complexity.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"48 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":"131138816","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: 11

2D-DFrFT Based Deep Network for Ship Classification in Remote Sensing Imagery 基于2D-DFrFT的遥感影像船舶分类深度网络

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486413

Qiaoqiao Shi, Wei Li, R. Tao

引用次数: 9

A Novel Ship Segmentation Method Based on Kurtosis Test in Complex-Valued SAR Imagery 基于峰度检验的复杂值SAR图像舰船分割新方法

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486227

Xiangguang Leng, K. Ji, Shilin Zhou

引用次数: 4

Modified Extinction Profiles for Hyperspectral Image Classification 用于高光谱图像分类的改进消光轮廓

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486259

Wei Li, Zhongjian Wang, Lu Li, Q. Du

引用次数: 2

Urban Local Climate Zone Classification with a Residual Convolutional Neural Network and Multi-Seasonal Sentinel-2 Images 基于残差卷积神经网络和多季节Sentinel-2图像的城市局地气候带分类

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486155

C. Qiu, M. Schmitt, Lichao Mou, Xiaoxiang Zhu

引用次数: 7

Fusion of Panchromatic and Multispectral Images via Morphological Operator and Improved PCNN in Mixed Multiscale Domain 基于形态学算子和改进PCNN的混合多尺度全色与多光谱图像融合

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486292

Jiao Jiao, Wu Lingda

{"title":"Fusion of Panchromatic and Multispectral Images via Morphological Operator and Improved PCNN in Mixed Multiscale Domain","authors":"Jiao Jiao, Wu Lingda","doi":"10.1109/PRRS.2018.8486292","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486292","url":null,"abstract":"In order to effectively combine the spectral information of the multispectral (MS) image with the spatial details of the panchromatic (PAN) image and improve the fusion quality, a fusion method based on morphological operator and improved pulse coupled neural network (PCNN) in mixed multi-scale (MM) domain is proposed. Firstly, the MS and PAN images are decomposed by nonsubsampled shearlet transform (NSST) to low- and high-frequency coefficients, respectively; secondly, morphological filter-based intensity modulation (MFIM) technology and stationary wavelet transform (SWT) are applied to the fusion of the low-frequency coefficients; an improved PCNN model is employed to the fusion of the high-frequency coefficients; thirdly, the final coefficients are reconstructed with inverse NSST. The experimental results on QuickBird satellite demonstrate that the proposed method is superior to five other kinds of traditional and popular methods: HIS, PCA, SWT, NSCT-PCNN and NSST-PCNN. The proposed method can improve the spatial resolution effectively while maintaining the spectral information well. The experimental results show that the proposed method outperforms the other methods in visual effect and objective evaluations.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"1 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":"129196916","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

Feature Fusion Through Multitask CNN for Large-scale Remote Sensing Image Segmentation 基于多任务CNN特征融合的大尺度遥感图像分割

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-07-24 DOI: 10.1109/PRRS.2018.8486170

Shihao Sun, Lei Yang, Wenjie Liu, Ruirui Li

引用次数: 14