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Remote sensing and image interpretation
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TLDR
In this article, the authors present a textbook for introductory courses in remote sensing, which includes concepts and foundations of remote sensing; elements of photographic systems; introduction to airphoto interpretation; air photo interpretation for terrain evaluation; photogrammetry; radiometric characteristics of aerial photographs; aerial thermography; multispectral scanning and spectral pattern recognition; microwave sensing; and remote sensing from space.Abstract:
A textbook prepared primarily for use in introductory courses in remote sensing is presented. Topics covered include concepts and foundations of remote sensing; elements of photographic systems; introduction to airphoto interpretation; airphoto interpretation for terrain evaluation; photogrammetry; radiometric characteristics of aerial photographs; aerial thermography; multispectral scanning and spectral pattern recognition; microwave sensing; and remote sensing from space.read more
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Journal ArticleDOI
Hyperspectral image data for mapping wetland vegetation
TL;DR: In this paper, a vegetation map was tested for classification accuracy with a pre-existing detailed GIS wetland vegetation database compiled by manual interpretation of 1∶40,000-scale color infrared (CIR) aerial photographs.
Journal ArticleDOI
Automatic kernel clustering with a Multi-Elitist Particle Swarm Optimization Algorithm
TL;DR: A scheme for clustering complex and linearly non-separable datasets, without any prior knowledge of the number of naturally occurring groups in the data is introduced, based on a modified version of classical Particle Swarm Optimization algorithm, known as the Multi-Elitist PSO (MEPSO) model, which employs a kernel-induced similarity measure instead of the conventional sum-of-squares distance.
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Integrating remote sensing in Natura 2000 habitat monitoring: Prospects on the way forward
Jeroen Vanden Borre,Desiré Paelinckx,Caspar A. Mücher,Lammert Kooistra,Birgen Haest,Geert De Blust,Anne M. Schmidt +6 more
TL;DR: It is argued that the integration of remote sensing into Natura 2000 habitat monitoring could benefit from harmonising and standardising approaches, focusing on data at hand to develop readily useful products, and an enhanced sharing and exchange of ideas and results between the different research communities involved.
Journal ArticleDOI
Tropical mangrove species discrimination using hyperspectral data: A laboratory study
TL;DR: In this paper, the authors used one-way ANOVA to test whether spectra of crown canopy leaves of various tropical mangrove species measured under laboratory conditions contain sufficient spectral information for discriminating mangroves at the species level.
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An Objective Analysis of Support Vector Machine Based Classification for Remote Sensing
Thomas Oommen,Debasmita Misra,Navin K. C. Twarakavi,Anupma Prakash,Bhaskar Sahoo,Sukumar Bandopadhyay +5 more
TL;DR: A comparative analysis of SVC with the Maximum Likelihood Classification (MLC) method, which is the most popular conventional supervised classification technique, illustrated that SVC improved the classification accuracy, was robust and did not suffer from dimensionality issues such as the Hughes Effect.