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Diofantos G. Hadjimitsis

Bio: Diofantos G. Hadjimitsis is an academic researcher from Cyprus University of Technology. The author has contributed to research in topics: Cultural heritage & Remote sensing (archaeology). The author has an hindex of 34, co-authored 312 publications receiving 3953 citations. Previous affiliations of Diofantos G. Hadjimitsis include Foundation for Research & Technology – Hellas & Frederick Institute of Technology.


Papers
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TL;DR: In this article, a study of atmospheric effects and their correction, using multi-spectral satellite remote sensing data for an area to the west of London that includes eight large water reservoirs and a major international airport (Heathrow) is presented.
Abstract: Although satellite remote sensing techniques have been widely implemented for a variety of applications, using either single or time-series images, few studies have explicitly considered atmospheric effects, and how they can most effectively be minimized Despite the considerable number of available atmospheric correction algorithms, there is little literature concerning their relative merits Over water bodies, atmospheric effects account for the majority of the at-satellite measured radiance in the visible bands, and therefore targets of this type provide an opportunity for assessing the effectiveness of the different methods available This paper reports a study of atmospheric effects and their correction, using multi-spectral satellite remote sensing data for an area to the west of London that includes eight large water reservoirs and a major international airport (Heathrow) Through comparisons of reflectance within a time series of 12 Landsat-5 Thematic Mapper (TM) images, the overall impact of atmo

210 citations

Journal ArticleDOI
TL;DR: In this article, the authors highlight the importance of considering atmospheric effects when vegetation indices, such as DVI, NDVI, SAVI, MSAVI and SARVI, are used and present the results obtained by applying the darkest-pixel atmospheric correction method on ten Landsat TM/ETM+ images of Cyprus acquired from July to December 2008.
Abstract: . Solar radiation reflected by the Earth's surface to satellite sensors is modified by its interaction with the atmosphere. The objective of applying an atmospheric correction is to determine true surface reflectance values and to retrieve physical parameters of the Earth's surface, including surface reflectance, by removing atmospheric effects from satellite images. Atmospheric correction is arguably the most important part of the pre-processing of satellite remotely sensed data. Such a correction is especially important in cases where multi-temporal images are to be compared and analyzed. For agricultural applications, in which several vegetation indices are applied for monitoring purposes, multi-temporal images are used. The integration of vegetation indices from remotely sensed images with other hydro-meteorological data is widely used for monitoring natural hazards such as droughts. Indeed, the most important task is to retrieve the true values of the vegetation status from the satellite-remotely sensed data. Any omission of considering the effects of the atmosphere when vegetation indices from satellite images are used, may lead to major discrepancies in the final outcomes. This paper highlights the importance of considering atmospheric effects when vegetation indices, such as DVI, NDVI, SAVI, MSAVI and SARVI, are used (or considered) and presents the results obtained by applying the darkest-pixel atmospheric correction method on ten Landsat TM/ETM+ images of Cyprus acquired from July to December 2008. Finally, in this analysis, an attempt is made to determine evapotranspiration and to examine its dependence on the consideration of atmospheric effects when multi-temporal image data are used. It was found that, without applying any atmospheric correction, the real daily evapotranspiration was less than the one found after applying the darkest pixel atmospheric correction method.

204 citations

Journal ArticleDOI
TL;DR: In this article, the authors developed an overall methodology for estimating erosion rate in a catchment area in Cyprus with the integrated use of satellite remote sensing (RS), Geographical Information Systems (GIS) and precipitation data.

173 citations

Journal ArticleDOI
TL;DR: A multidisciplinary approach, based on remote sensing techniques and Geographical Information System (GIS) analysis, is presented, in order to assess the overall risk in the Paphos district (Cyprus), which has a great deal of archaeological sites and isolated monuments.

117 citations

Journal ArticleDOI
TL;DR: Several not widely used vegetation indices are suggested and evaluated using both Landsat TM and EO-1 Hyperion images, and the use of more than one vegetation index is suggested in order to enhance the final results.
Abstract: Several studies in the past have examined the spectral capability of multispectral and hyperspectral imagery for the identification of crop marks, while recent studies have applied different vegetation indices in order to support remote sensing archaeological applications. However, the use of vegetation indices for the detection of crop marks lacks in accuracy assessment and critical evaluation. In this study, 71 vegetation indices were indexed, from the relevant bibliography, and evaluated for their potential to detect such crop marks. During this study, several ground spectroradiometric campaigns took place, in a controlled archaeological environment in Cyprus, cultivated with barley crops, during a complete phenological cycle (2011-2012). All vegetation indices, both broadband and narrowband, were evaluated for their separability performance, and the results were presented through tables and diagrams. In the end, the use of more than one vegetation index is suggested in order to enhance the final results. In fact, several not widely used vegetation indices are suggested and evaluated using both Landsat TM and EO-1 Hyperion images.

115 citations


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01 Jan 2016
TL;DR: The using multivariate statistics is universally compatible with any devices to read, allowing you to get the most less latency time to download any of the authors' books like this one.
Abstract: Thank you for downloading using multivariate statistics. As you may know, people have look hundreds times for their favorite novels like this using multivariate statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop. using multivariate statistics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the using multivariate statistics is universally compatible with any devices to read.

14,604 citations

Journal Article
TL;DR: This research examines the interaction between demand and socioeconomic attributes through Mixed Logit models and the state of art in the field of automatic transport systems in the CityMobil project.
Abstract: 2 1 The innovative transport systems and the CityMobil project 10 1.1 The research questions 10 2 The state of art in the field of automatic transport systems 12 2.1 Case studies and demand studies for innovative transport systems 12 3 The design and implementation of surveys 14 3.1 Definition of experimental design 14 3.2 Questionnaire design and delivery 16 3.3 First analyses on the collected sample 18 4 Calibration of Logit Multionomial demand models 21 4.1 Methodology 21 4.2 Calibration of the “full” model. 22 4.3 Calibration of the “final” model 24 4.4 The demand analysis through the final Multinomial Logit model 25 5 The analysis of interaction between the demand and socioeconomic attributes 31 5.1 Methodology 31 5.2 Application of Mixed Logit models to the demand 31 5.3 Analysis of the interactions between demand and socioeconomic attributes through Mixed Logit models 32 5.4 Mixed Logit model and interaction between age and the demand for the CTS 38 5.5 Demand analysis with Mixed Logit model 39 6 Final analyses and conclusions 45 6.1 Comparison between the results of the analyses 45 6.2 Conclusions 48 6.3 Answers to the research questions and future developments 52

4,784 citations

Journal ArticleDOI
TL;DR: It is suggested that designing a suitable image‐processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map and the selection of a suitable classification method is especially significant for improving classification accuracy.
Abstract: Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image-processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non-parametric classifiers such as neural network, decision tree classifier, and knowledge-based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image-processing chain to improve classification accuracy.

2,741 citations

01 Jan 2016
TL;DR: The remote sensing and image interpretation is universally compatible with any devices to read and is available in the digital library an online access to it is set as public so you can get it instantly.
Abstract: Thank you very much for downloading remote sensing and image interpretation. As you may know, people have look hundreds times for their favorite novels like this remote sensing and image interpretation, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some malicious virus inside their computer. remote sensing and image interpretation is available in our digital library an online access to it is set as public so you can get it instantly. Our book servers spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the remote sensing and image interpretation is universally compatible with any devices to read.

1,802 citations