paper: fixed DOIs

Author: egemose

paper/paper.bib

@@ -19,22 +19,25 @@ @article{pareview2020
 pages = {107148},
 year = {2020},
 issn = {1389-1286},
-doi = {https://doi.org/10.1016/j.comnet.2020.107148},
+doi = {10.1016/j.comnet.2020.107148},
 url = {https://www.sciencedirect.com/science/article/pii/S138912862030116X},
 author = {Panagiotis Radoglou-Grammatikis and Panagiotis Sarigiannidis and Thomas Lagkas and Ioannis Moscholios},
 keywords = {Precision agriculture (PA), Remote sensing (RS), Unmanned aerial vehicle (UAV)},
 abstract = {Climate change has introduced significant challenges that can affect multiple sectors, including the agricultural one. In particular, according to the Food and Agriculture Organization of the United Nations (FAO) and the International Telecommunication Union (ITU), the world population has to find new solutions to increase the food production by 70% by 2050. The answer to this crucial challenge is the suitable adoption and utilisation of the Information and Communications Technology (ICT) services, offering capabilities that can increase the productivity of the agrochemical products, such as pesticides and fertilisers and at the same time, they should minimise the functional cost. More detailed, the advent of the Internet of Things (IoT) and specifically, the rapid evolution of the Unmanned Aerial Vehicles (UAVs) and Wireless Sensor Networks (WSNs) can lead to valuable and at the same time economic Precision Agriculture (PA) applications, such as aerial crop monitoring and smart spraying tasks. In this paper, we provide a survey regarding the potential use of UAVs in PA, focusing on 20 relevant applications. More specifically, first, we provide a detailed overview of PA, by describing its various aspects and technologies, such as soil mapping and production mapping as well as the role of the Global Positioning Systems (GPS) and Geographical Information Systems (GIS). Then, we discriminate and analyse the various types of UAVs based on their technical characteristics and payload. Finally, we investigate in detail 20 UAV applications that are devoted to either aerial crop monitoring processes or spraying tasks. For each application, we examine the methodology adopted, the proposed UAV architecture, the UAV type, as well as the UAV technical characteristics and payload.}
 }
 
 @article{xue2017,
-  title={Significant remote sensing vegetation indices: A review of developments and applications},
-  author={Xue, Jinru and Su, Baofeng},
-  journal={Journal of sensors},
-  volume={2017},
-  number={1},
-  pages={1353691},
-  year={2017},
-  publisher={Wiley Online Library}
+author = {Xue, Jinru and Su, Baofeng},
+title = {Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications},
+journal = {Journal of Sensors},
+volume = {2017},
+number = {1},
+pages = {1353691},
+doi = {10.1155/2017/1353691},
+url = {https://onlinelibrary.wiley.com/doi/abs/10.1155/2017/1353691},
+eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1155/2017/1353691},
+abstract = {Vegetation Indices (VIs) obtained from remote sensing based canopies are quite simple and effective algorithms for quantitative and qualitative evaluations of vegetation cover, vigor, and growth dynamics, among other applications. These indices have been widely implemented within RS applications using different airborne and satellite platforms with recent advances using Unmanned Aerial Vehicles (UAV). Up to date, there is no unified mathematical expression that defines all VIs due to the complexity of different light spectra combinations, instrumentation, platforms, and resolutions used. Therefore, customized algorithms have been developed and tested against a variety of applications according to specific mathematical expressions that combine visible light radiation, mainly green spectra region, from vegetation, and nonvisible spectra to obtain proxy quantifications of the vegetation surface. In the real-world applications, optimization VIs are usually tailored to the specific application requirements coupled with appropriate validation tools and methodologies in the ground. The present study introduces the spectral characteristics of vegetation and summarizes the development of VIs and the advantages and disadvantages from different indices developed. This paper reviews more than 100 VIs, discussing their specific applicability and representativeness according to the vegetation of interest, environment, and implementation precision. Predictably, research, and development of VIs, which are based on hyperspectral and UAV platforms, would have a wide applicability in different areas.},
+year = {2017}
 }
 
 @article{PANG2020,
@@ -81,7 +84,8 @@ @InProceedings{Ronneberger2015Unet
 address="Cham",
 pages="234--241",
 abstract="There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.",
-isbn="978-3-319-24574-4"
+isbn="978-3-319-24574-4",
+doi="10.1007/978-3-319-24574-4_28"
 }
 
 @article{Woebbecke1995,
@@ -120,7 +124,8 @@ @article{scikit-learn
   journal={Journal of Machine Learning Research},
   volume={12},
   pages={2825--2830},
-  year={2011}
+  year={2011},
+  doi={10.5555/1953048.2078195}
 }
 
 @misc{ MahalanobisDistance,