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ISPRS journal of photogrammetry and remote sensing... 13건

  1. [해외논문]   inside front cover (Editorial Board)  


    ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) v.124 ,pp. IFC , 2017 , 0924-2716 ,

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    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  2. [해외논문]   inside front cover (Editorial Board)   SCIE


    ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) v.124 ,pp. IFC - IFC , 2017 , 0924-2716 ,

    초록

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    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  3. [해외논문]   Sparse graph regularization for robust crop mapping using hyperspectral remotely sensed imagery with very few in situ data   SCIE

    Xue, Zhaohui (School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China ) , Du, Peijun (Key Laboratory for Satellite Mapping Technology and Applications of National Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing 210023, China ) , Li, Jun (Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China ) , Su, Hongjun (School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China)
    ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) v.124 ,pp. 1 - 15 , 2017 , 0924-2716 ,

    초록

    Abstract The generally limited availability of training data relative to the usually high data dimension pose a great challenge to accurate classification of hyperspectral imagery, especially for identifying crops characterized with highly correlated spectra. However, traditional parametric classification models are problematic due to the need of non-singular class-specific covariance matrices. In this research, a novel sparse graph regularization (SGR) method is presented, aiming at robust crop mapping using hyperspectral imagery with very few in situ data. The core of SGR lies in propagating labels from known data to unknown, which is triggered by: (1) the fraction matrix generated for the large unknown data by using an effective sparse representation algorithm with respect to the few training data serving as the dictionary; (2) the prediction function estimated for the few training data by formulating a regularization model based on sparse graph. Then, the labels of large unknown data can be obtained by maximizing the posterior probability distribution based on the two ingredients. SGR is more discriminative, data-adaptive, robust to noise, and efficient, which is unique with regard to previously proposed approaches and has high potentials in discriminating crops, especially when facing insufficient training data and high-dimensional spectral space. The study area is located at Zhangye basin in the middle reaches of Heihe watershed, Gansu, China, where eight crop types were mapped with Compact Airborne Spectrographic Imager (CASI) and Shortwave Infrared Airborne Spectrogrpahic Imager (SASI) hyperspectral data. Experimental results demonstrate that the proposed method significantly outperforms other traditional and state-of-the-art methods.

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    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  4. [해외논문]   Ground-based hyperspectral analysis of the urban nightscape   SCIE

    Alamú (Institut Cartogràfic i Geològic de Catalunya (ICGC), Parc de Montjuïc s/n, 08038 Barcelona, Catalunya, Spain ) , s, Ramon (Área de Óptica, Departamento de Física Aplicada, Universidade de Santiago de Compostela, Santiago de Compostela, Galicia, Spain ) , Bará (Institut Cartogràfic i Geològic de Catalunya (ICGC), Parc de Montjuïc s/n, 08038 Barcelona, Catalunya, Spain ) , , Salvador (Departament d'Òptica i Optometria, Universitat Politècnica de Catalunya, Terrassa, Catalunya, Spain ) , Corbera, Jordi (Institut Cartogràfic i Geològic de Catalunya (ICGC), Parc de Montjuïc s/n, 08038 Barcelona, Catalunya, Spain ) , Escofet, Jaume (Institut Cartogràfic i Geològic de Catalunya (ICGC), Parc de Montjuïc s/n, 08038 Barcelona, Catalunya, Spain ) , Palà (Institut Cartogràfic i Geològic de Catalunya (ICGC), Parc de Montjuïc s/n, 08038 Barcelona, Catalunya, Spain) , , Vicenç , , Pipia, Luca , Tardà , , Anna
    ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) v.124 ,pp. 16 - 26 , 2017 , 0924-2716 ,

    초록

    Abstract Airborne hyperspectral cameras provide the basic information to estimate the energy wasted skywards by outdoor lighting systems, as well as to locate and identify their sources. However, a complete characterization of the urban light pollution levels also requires evaluating these effects from the city dwellers standpoint, e.g. the energy waste associated to the excessive illuminance on walls and pavements, light trespass, or the luminance distributions causing potential glare, to mention but a few. On the other hand, the spectral irradiance at the entrance of the human eye is the primary input to evaluate the possible health effects associated with the exposure to artificial light at night, according to the more recent models available in the literature. In this work we demonstrate the possibility of using a hyperspectral imager (routinely used in airborne campaigns) to measure the ground-level spectral radiance of the urban nightscape and to retrieve several magnitudes of interest for light pollution studies. We also present the preliminary results from a field campaign carried out in the downtown of Barcelona.

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    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  5. [해외논문]   Multi-source remotely sensed data fusion for improving land cover classification   SCIE

    Chen, Bin (State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China ) , Huang, Bo (Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong ) , Xu, Bing (State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China)
    ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) v.124 ,pp. 27 - 39 , 2017 , 0924-2716 ,

    초록

    Abstract Although many advances have been made in past decades, land cover classification of fine-resolution remotely sensed (RS) data integrating multiple temporal, angular, and spectral features remains limited, and the contribution of different RS features to land cover classification accuracy remains uncertain. We proposed to improve land cover classification accuracy by integrating multi-source RS features through data fusion. We further investigated the effect of different RS features on classification performance. The results of fusing Landsat-8 Operational Land Imager (OLI) data with Moderate Resolution Imaging Spectroradiometer (MODIS), China Environment 1A series (HJ-1A), and Advanced Spaceborne Thermal Emission and Reflection (ASTER) digital elevation model (DEM) data, showed that the fused data integrating temporal, spectral, angular, and topographic features achieved better land cover classification accuracy than the original RS data. Compared with the topographic feature, the temporal and angular features extracted from the fused data played more important roles in classification performance, especially those temporal features containing abundant vegetation growth information, which markedly increased the overall classification accuracy. In addition, the multispectral and hyperspectral fusion successfully discriminated detailed forest types. Our study provides a straightforward strategy for hierarchical land cover classification by making full use of available RS data. All of these methods and findings could be useful for land cover classification at both regional and global scales.

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    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  6. [해외논문]   A survey of landmine detection using hyperspectral imaging   SCIE

    Makki, Ihab (Lebanese University, Faculty of Engineering, Beirut, Lebanon ) , Younes, Rafic (Lebanese University, Faculty of Engineering, Beirut, Lebanon ) , Francis, Clovis (Lebanese University, Faculty of Engineering, Beirut, Lebanon ) , Bianchi, Tiziano (Politecnico di Torino, Torino, Italy ) , Zucchetti, Massimo (Politecnico di Torino, Torino, Italy)
    ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) v.124 ,pp. 40 - 53 , 2017 , 0924-2716 ,

    초록

    Abstract Hyperspectral imaging is a trending technique in remote sensing that finds its application in many different areas, such as agriculture, mapping, target detection, food quality monitoring, etc. This technique gives the ability to remotely identify the composition of each pixel of the image. Therefore, it is a natural candidate for the purpose of landmine detection, thanks to its inherent safety and fast response time. In this paper, we will present the results of several studies that employed hyperspectral imaging for the purpose of landmine detection, discussing the different signal processing techniques used in this framework for hyperspectral image processing and target detection. Our purpose is to highlight the progresses attained in the detection of landmines using hyperspectral imaging and to identify possible perspectives for future work, in order to achieve a better detection in real-time operation mode.

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    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  7. [해외논문]   Multi-objective based spectral unmixing for hyperspectral images   SCIE

    Xu, Xia (Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, PR China ) , Shi, Zhenwei (Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, PR China)
    ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) v.124 ,pp. 54 - 69 , 2017 , 0924-2716 ,

    초록

    Abstract Sparse hyperspectral unmixing assumes that each observed pixel can be expressed by a linear combination of several pure spectra in a priori library. Sparse unmixing is challenging, since it is usually transformed to a NP-hard l 0 norm based optimization problem. Existing methods usually utilize a relaxation to the original l 0 norm. However, the relaxation may bring in sensitive weighted parameters and additional calculation error. In this paper, we propose a novel multi-objective based algorithm to solve the sparse unmixing problem without any relaxation. We transform sparse unmixing to a multi-objective optimization problem, which contains two correlative objectives: minimizing the reconstruction error and controlling the endmember sparsity. To improve the efficiency of multi-objective optimization, a population-based randomly flipping strategy is designed. Moreover, we theoretically prove that the proposed method is able to recover a guaranteed approximate solution from the spectral library within limited iterations. The proposed method can directly deal with l 0 norm via binary coding for the spectral signatures in the library. Experiments on both synthetic and real hyperspectral datasets demonstrate the effectiveness of the proposed method.

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    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  8. [해외논문]   A cloud detection algorithm-generating method for remote sensing data at visible to short-wave infrared wavelengths   SCIE

    Sun, Lin (Geomatics College, Shandong University of Science and Technology, Qingdao, Shandong 266590, China ) , Mi, Xueting (Geomatics College, Shandong University of Science and Technology, Qingdao, Shandong 266590, China ) , Wei, Jing (Geomatics College, Shandong University of Science and Technology, Qingdao, Shandong 266590, China ) , Wang, Jian (School of Geography, Beijing Normal University, Beijing 100875, China ) , Tian, Xinpeng (Geomatics College, Shandong University of Science and Technology, Qingdao, Shandong 266590, China ) , Yu, Huiyong (Geomatics College, Shandong University of Science and Technology, Qingdao, Shandong 266590, China ) , Gan, Ping (Geomatics College, Shandong University of Science and Technology, Qingdao, Shandong 266590, China)
    ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) v.124 ,pp. 70 - 88 , 2017 , 0924-2716 ,

    초록

    Abstract To realize highly precise and automatic cloud detection from multi-sensors, this paper proposes a cloud detection algorithm-generating (CDAG) method for remote sensing data from visible to short-wave infrared (SWIR) bands. Hyperspectral remote sensing data with high spatial resolution were collected and used as a pixel dataset of cloudy and clear skies. In this paper, multi-temporal AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) data with 224 bands at visible to SWIR wavelengths and a 20m spatial resolution were used for the dataset. Based on the pixel dataset, pixels of different types of clouds and land cover were distinguished artificially and used for the simulation of multispectral sensors. Cloud detection algorithms for the multispectral remote sensing sensors were then generated based on the spectral differences between the cloudy and clear-sky pixels distinguished previously. The possibility of assigning a pixel as cloudy was calculated based on the reliability of each method. Landsat 8 OLI (Operational Land Imager), MODIS (Moderate Resolution Imaging Spectroradiometer) Terra and Suomi NPP VIIRS (Visible/Infrared Imaging Radiometer) were used for the cloud detection test with the CDAG method, and the results from each sensor were compared with the corresponding artificial results, demonstrating an accurate detection rate of more than 85%.

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    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  9. [해외논문]   Quantifying annual changes in built-up area in complex urban-rural landscapes from analyses of PALSAR and Landsat images   SCIE

    Qin, Yuanwei (Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA ) , Xiao, Xiangming (Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA ) , Dong, Jinwei (Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA ) , Chen, Bangqian (Ministry of Education Key Laboratory of Biodiversity Science and Ecological Engineering, Institute of Biodiversity Science, Fudan University, Shanghai 200433, China ) , Liu, Fang (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China ) , Zhang, Geli (Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA ) , Zhang, Yao (Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA ) , Wang, Jie (Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, N) , Wu, Xiaocui
    ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) v.124 ,pp. 89 - 105 , 2017 , 0924-2716 ,

    초록

    Abstract Built-up area supports human settlements and activities, and its spatial distribution and temporal dynamics have significant impacts on ecosystem services and global environment change. To date, most of urban remote sensing has generated the maps of impervious surfaces, and limited effort has been made to explicitly identify the area, location and density of built-up in the complex and fragmented landscapes based on the freely available datasets. In this study, we took the lower Yangtze River Delta (Landsat Path/Row: 118/038), China, where extensive urbanization and industrialization have occurred, as a case study site. We analyzed the structure and optical features of typical land cover types from (1) the HH and HV gamma-naught imagery from the Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), and (2) time series Landsat imagery. We proposed a pixel- and rule-based decision tree approach to identify and map built-up area at 30-m resolution from 2007 to 2010, using PALSAR HH gamma-naught and Landsat annual maximum Normalized Difference Vegetation Index (NDVI max ). The accuracy assessment showed that the resultant annual maps of built-up had relatively high user (87–93%) and producer accuracies (91–95%) from 2007 to 2010. The built-up area was 2805km 2 in 2010, about 16% of the total land area of the study site. The annual maps of built-up in 2007–2010 show relatively small changes in the urban core regions, but large outward expansion along the peri-urban regions. The average annual increase of built-up area s was about 80km 2 per year from 2007 to 2010. Our annual maps of built-up in the lower Yangtze River Delta clearly complement the existing maps of impervious surfaces in the region. This study provides a promising new approach to identify and map built-up area, which is critical to investigate the interactions between human activities and ecosystem services in urban-rural systems.

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    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  10. [해외논문]   EXhype: A tool for mineral classification using hyperspectral data   SCIE

    Adep, Ramesh Nityanand (Corresponding author.) , shetty, Amba , Ramesh, H.
    ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) v.124 ,pp. 106 - 118 , 2017 , 0924-2716 ,

    초록

    Abstract Various supervised classification algorithms have been developed to classify earth surface features using hyperspectral data. Each algorithm is modelled based on different human expertises. However, the performance of conventional algorithms is not satisfactory to map especially the minerals in view of their typical spectral responses. This study introduces a new expert system named ‘EXhype (Expert system for hyperspectral data classification)’ to map minerals. The system incorporates human expertise at several stages of it’s implementation: (i) to deal with intra-class variation; (ii) to identify absorption features; (iii) to discriminate spectra by considering absorption features, non-absorption features and by full spectra comparison; and (iv) finally takes a decision based on learning and by emphasizing most important features. It is developed using a knowledge base consisting of an Optimal Spectral Library, Segmented Upper Hull method, Spectral Angle Mapper (SAM) and Artificial Neural Network. The performance of the EXhype is compared with a traditional, most commonly used SAM algorithm using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data acquired over Cuprite, Nevada, USA. A virtual verification method is used to collect samples information for accuracy assessment. Further, a modified accuracy assessment method is used to get a real users accuracies in cases where only limited or desired classes are considered for classification. With the modified accuracy assessment method, SAM and EXhype yields an overall accuracy of 60.35% and 90.75% and the kappa coefficient of 0.51 and 0.89 respectively. It was also found that the virtual verification method allows to use most desired stratified random sampling method and eliminates all the difficulties associated with it. The experimental results show that EXhype is not only producing better accuracy compared to traditional SAM but, can also rightly classify the minerals. It is proficient in avoiding misclassification between target classes when applied on minerals. Highlights New expert system (EXhype) has introduced to classify hyperspectral data. This study came out with novel way, to give input to the neural network. EXhype has learning ability as it incorporates neural network. EXhype discriminates similar spectra and avoids misclassification between targets.

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