Super-resolution mapping is used to produces thematic maps at a scale finer than the source images. This paper presents
a new super-resolution mapping approach that exploits the typically fine temporal resolution of coarse spatial resolution
images as it input and an adoption of an active threshold surface using Hopfield neural network as a means to map land
cover at a sub-pixel scale. The results demonstrated that the proposed technique is slightly more accurate than the
existence technique in terms of site specific accuracy and produce better visualization on individual land cover map.
This paper describes the application of the geostastistical method to quantify noise from a compact airborne spectrograhic imager (CASI) data set. Estimation of noise contained within a remote sensing image is essential in order to quanitfy the effects of noise contamination. Noise was estimated from CASI imagery by calculation the noise as the square root of the nugget variance, a parameter of a fitte semivariogram model. The signal-to-noise ratio (SNR) can then be estimated by dividing the mean vaue by the square root of the nugget variance. Three wavebands 0.46-049μm (blue), 0-63-0.64μm (red) and 0.70-071μm (near-infrared) were used in the analysis. A total of five land covers were selected, each representing a common land cover type in the area which are i)bracken ii)conifer woodland iii)grassland iv)heathland and v)deciduous woodland. The results shows that the noise varies in different land cover types and wavelengths.
SVM classification has great potential in remote sensing. The nature of SVM classification also provides opportunities for accurate classification from relatively small training sets, especially if interest is focused on a single class. Five approaches to reducing training set size from that suggested by conventional heuristics are discussed: intelligent selection of the most informative training samples, selective class exclusion, acceptance of imprecise descriptions for spectrally distinct classes, the adoption of a one-class classifier and a focus on boundary regions. All five approaches were able to reduce the training set size required considerably below that suggested by conventional widely used heuristics without significant impact on the accuracy with which the class of interest was classified. For example, reductions in training set size of ~90% from that suggested by a conventional heuristic are reported with the accuracy of the class of interest remaining nearly constant at ~95% and ~97% from the user's and producer's perspectives respectively.
Metrics are defined which quantify the ‘value’ of hyperspectral imagery in the context of military tasks. A design trade-off process for sensor concept evaluation is described which takes account of constraints, such as system cost and technology limitations, on the permitted values of design parameters. The particular issue of band selection is addressed in some detail. Techniques for evaluating sensor design trade-offs are then described, based principally on simulating sensor configurations using measured image data as input. The initial use of these techniques against a limited data set is reported.
Image classification used in mapping land cover form remotely sensed data are frequently described as being 'hard' of 'soft' yet in reality such a simple distinction is not observed and a continuum of classification softness can be defined. Using airborne sensors or imagery of two test sites in South Wales, classifications at different points along this continuum with a feedforward neural network are illustrated. It is shown that soft classification can provide a better and more accurate representation of both discrete and continuous land cover classes, resolving in particular problems associated with mixed pixels. Classifications produced at different positions along the continuum of classification softness, however, differed markedly in the representation of land cover distribution and accuracy, highlighting the need to recognize the existence of the continuum and its implications for land cover mapping from remotely sensed data. The results also highlight that the use of a soft or fuzzy classifier is only a partial solution to the mixed pixel problem; a full solution requires refinement of the training and testing stages and methods for this are discussed. Despite an ability to accommodate for the effects of mixed pixels on each of the three stages of supervised image classifications, other factors can degraded classification quality. One important issue is the presence of untrained classes. It is hon, however, that the effect of untrained classes can be reduced with the use of additional information on the typicality of class membership that can be derived form some soft classifications.
Conference Committee Involvement (15)
Image and Signal Processing for Remote Sensing
11 September 2017 | Warsaw, Poland
Image and Signal Processing for Remote Sensing
26 September 2016 | Edinburgh, United Kingdom
Image and Signal Processing for Remote Sensing
21 September 2015 | Toulouse, France
Image and Signal Processing for Remote Sensing
22 September 2014 | Amsterdam, Netherlands
Image and Signal Processing for Remote Sensing XIX
23 September 2013 | Dresden, Germany
Image and Signal Processing for Remote Sensing
24 September 2012 | Edinburgh, United Kingdom
Image and Signal Processing for Remote Sensing
19 September 2011 | Prague, Czech Republic
Image and Signal Processing for Remote Sensing
20 September 2010 | Toulouse, France
Image and Signal Processing for Remote Sensing
31 August 2009 | Berlin, Germany
Image and Signal Processing for Remote Sensing
15 September 2008 | Cardiff, Wales, United Kingdom
Image and Signal Processing for Remote Sensing
18 September 2007 | Florence, Italy
Image and Signal Processing for Remote Sensing XII
13 September 2006 | Stockholm, Sweden
Image and Signal Processing for Remote Sensing XI
20 September 2005 | Bruges, Belgium
Image and Signal Processing for Remote Sensing X
13 September 2004 | Maspalomas, Canary Islands, Spain
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