Pub Date : 2021-01-01DOI: 10.11972/J.ISSN.1001-9014.2021.05.015
Tong Hao-Chen, Tang Shu-min, YE Shu-Ming, Duan Xiao-xiao, Li Xiao-nan, Xie Ji-Yang, Zhang Lu-Ran, Yang Jie, Qiu Feng, Wang Rong-Fei, Wen Xiao-ming, Yang Yu, Cui Hao-yang, W. Chong
{"title":"Effective enhancement of the photoluminescence from the Si + /Ni + ions co-implanted SOI by directly constructing the nanodisk photonic crystals","authors":"Tong Hao-Chen, Tang Shu-min, YE Shu-Ming, Duan Xiao-xiao, Li Xiao-nan, Xie Ji-Yang, Zhang Lu-Ran, Yang Jie, Qiu Feng, Wang Rong-Fei, Wen Xiao-ming, Yang Yu, Cui Hao-yang, W. Chong","doi":"10.11972/J.ISSN.1001-9014.2021.05.015","DOIUrl":"https://doi.org/10.11972/J.ISSN.1001-9014.2021.05.015","url":null,"abstract":"","PeriodicalId":50181,"journal":{"name":"红外与毫米波学报","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66395140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.11972/J.ISSN.1001-9014.2021.05.017
Zhu Wen-Qing, Tang Xin-yi, Zhang Rui, C. Xiao, Miao Zhuang
{"title":"Infrared and visible image fusion based on edge-preserving and attention generative adversarial network","authors":"Zhu Wen-Qing, Tang Xin-yi, Zhang Rui, C. Xiao, Miao Zhuang","doi":"10.11972/J.ISSN.1001-9014.2021.05.017","DOIUrl":"https://doi.org/10.11972/J.ISSN.1001-9014.2021.05.017","url":null,"abstract":"","PeriodicalId":50181,"journal":{"name":"红外与毫米波学报","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66395199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.11972/J.ISSN.1001-9014.2021.01.001
Ye Xin-Hui, X. Tian, Xia Hui, Chen Xi-Ren, Li Ju-Zhu, Zhang Shuai-Jun, Jiang Xin-Yang, Deng Wei-jie, Wang Wenjing, Li Yu-Ying, Liu Weiwei, Liu Fang, Li Tian-Xin
{"title":"Quantum well micropillar arrays with low filling factor for enhanced infrared absorption","authors":"Ye Xin-Hui, X. Tian, Xia Hui, Chen Xi-Ren, Li Ju-Zhu, Zhang Shuai-Jun, Jiang Xin-Yang, Deng Wei-jie, Wang Wenjing, Li Yu-Ying, Liu Weiwei, Liu Fang, Li Tian-Xin","doi":"10.11972/J.ISSN.1001-9014.2021.01.001","DOIUrl":"https://doi.org/10.11972/J.ISSN.1001-9014.2021.01.001","url":null,"abstract":"","PeriodicalId":50181,"journal":{"name":"红外与毫米波学报","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66394856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.11972/J.ISSN.1001-9014.2020.01.017
Tang Peng-fei, Miao Zelang, Lin Cong, Duan Pei-jun, Guo Shan-Chuan
Supervised classification is a vital approach to extract impervious surface areas(ISA)from satellite images,but the training samples need to be provided through heavy manual work. To address it,this study proposed an automatic method to generate training samples from high-resolution night light data,considering that nighttime lights generated by human activities is strongly correlated with impervious surface. First,positive and negative samples for ISA were located according to the distribu‐ tion of nighttime lights. Second,the feature sets were constructed by calculating the spectral and tex‐ ture feature from the OLI images. Third,an ensemble ELM classifier was selected for ISA classifica‐ tion and extraction. Four large cities were selected as study areas to examine the performance of the 文章编号:1001-9014(2020)01-0128-09 DOI:10. 11972/j. issn. 1001-9014. 2020. 01. 017 收稿日期:2019-08-16,修回日期:2019-12-16 Received date:2019-08-16,Revised date:2019-12-16 基金项目:国家自然科学基金重点项目(41631176) Foundation items:Supported by the National Natural Science Foundation of China(41631176) 作者简介(Biography):唐鹏飞(1997-),男,安徽合肥人,博士生,主要研究领域为遥感图像智能处理 . E-mail:Sgos_tpf@smail. nju. edu. cn *通讯作者(Corresponding author):zelang. miao@csu. edu. cn;dupjrs@126. com 1期 唐鹏飞 等:融合高分夜光和Landsat OLI影像的不透水面自动提取方法 proposed method in different environment. The results show that the proposed method can automatical‐ ly and accurately acquire ISA with an overall accuracy higher than 93% and Kappa coefficient higher than 0. 87. Furthermore,comparative experiments by biophysical composition index(BCI)and classi‐ fication by manual sample were conducted to evaluate its superiority. The results show that our method has better separability for ISA and soil than the BCI. In general,the proposed method is superior to manual methods,except Harbin mostly because some impervious surfaces with weak light intensity are selected as negative samples.
{"title":"An automatic method for impervious surface area extraction by fusing high-resolution night light and Landsat OLI images","authors":"Tang Peng-fei, Miao Zelang, Lin Cong, Duan Pei-jun, Guo Shan-Chuan","doi":"10.11972/J.ISSN.1001-9014.2020.01.017","DOIUrl":"https://doi.org/10.11972/J.ISSN.1001-9014.2020.01.017","url":null,"abstract":"Supervised classification is a vital approach to extract impervious surface areas(ISA)from satellite images,but the training samples need to be provided through heavy manual work. To address it,this study proposed an automatic method to generate training samples from high-resolution night light data,considering that nighttime lights generated by human activities is strongly correlated with impervious surface. First,positive and negative samples for ISA were located according to the distribu‐ tion of nighttime lights. Second,the feature sets were constructed by calculating the spectral and tex‐ ture feature from the OLI images. Third,an ensemble ELM classifier was selected for ISA classifica‐ tion and extraction. Four large cities were selected as study areas to examine the performance of the 文章编号:1001-9014(2020)01-0128-09 DOI:10. 11972/j. issn. 1001-9014. 2020. 01. 017 收稿日期:2019-08-16,修回日期:2019-12-16 Received date:2019-08-16,Revised date:2019-12-16 基金项目:国家自然科学基金重点项目(41631176) Foundation items:Supported by the National Natural Science Foundation of China(41631176) 作者简介(Biography):唐鹏飞(1997-),男,安徽合肥人,博士生,主要研究领域为遥感图像智能处理 . E-mail:Sgos_tpf@smail. nju. edu. cn *通讯作者(Corresponding author):zelang. miao@csu. edu. cn;dupjrs@126. com 1期 唐鹏飞 等:融合高分夜光和Landsat OLI影像的不透水面自动提取方法 proposed method in different environment. The results show that the proposed method can automatical‐ ly and accurately acquire ISA with an overall accuracy higher than 93% and Kappa coefficient higher than 0. 87. Furthermore,comparative experiments by biophysical composition index(BCI)and classi‐ fication by manual sample were conducted to evaluate its superiority. The results show that our method has better separability for ISA and soil than the BCI. In general,the proposed method is superior to manual methods,except Harbin mostly because some impervious surfaces with weak light intensity are selected as negative samples.","PeriodicalId":50181,"journal":{"name":"红外与毫米波学报","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66394683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.11972/J.ISSN.1001-9014.2020.03.018
Hu Shuo, Hu Yong, Gong Cai-lan, Zheng Fu-Qiang
Due to the limitations of infrared optical diffraction and infrared detectors,the noise of infra‐ red images is relatively large and the resolution is low. Super-resolution reconstruction of infrared im‐ ages improves image resolution,but at the same time enhances the noise of background. Aiming at this problem,a salience region super-resolution reconstruction algorithm for infrared images based on sparse coding is proposed. By combining the saliency detection and the super-segment reconstruction, it improves the target definition and reduces the background noise. Firstly,image feature is extracted by double-layer convolution,and image patches with large entropy are adaptively selected for training the joint dictionary. Sparse features are used to calculate the saliency to obtain salient regions,which reconstructs image patches in saliency region by the trained dictionary while the background region adopts Gaussian filtering. Experimental results show that the improved reconstruction algorithm is bet‐ ter than ScSR and SRCNN under the same conditions. The image signal-to-noise ratio is increased by 3-4 times.
{"title":"Salience region super-resolution reconstruction algorithm for infrared images based on sparse coding","authors":"Hu Shuo, Hu Yong, Gong Cai-lan, Zheng Fu-Qiang","doi":"10.11972/J.ISSN.1001-9014.2020.03.018","DOIUrl":"https://doi.org/10.11972/J.ISSN.1001-9014.2020.03.018","url":null,"abstract":"Due to the limitations of infrared optical diffraction and infrared detectors,the noise of infra‐ red images is relatively large and the resolution is low. Super-resolution reconstruction of infrared im‐ ages improves image resolution,but at the same time enhances the noise of background. Aiming at this problem,a salience region super-resolution reconstruction algorithm for infrared images based on sparse coding is proposed. By combining the saliency detection and the super-segment reconstruction, it improves the target definition and reduces the background noise. Firstly,image feature is extracted by double-layer convolution,and image patches with large entropy are adaptively selected for training the joint dictionary. Sparse features are used to calculate the saliency to obtain salient regions,which reconstructs image patches in saliency region by the trained dictionary while the background region adopts Gaussian filtering. Experimental results show that the improved reconstruction algorithm is bet‐ ter than ScSR and SRCNN under the same conditions. The image signal-to-noise ratio is increased by 3-4 times.","PeriodicalId":50181,"journal":{"name":"红外与毫米波学报","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66394542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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