Entity

Time filter

Source Type


Carrao H.,Portuguese Geographic Institute IGP | Carrao H.,New University of Lisbon | Araujo A.,Portuguese Geographic Institute IGP | Goncalves P.,University of Lyon | And 2 more authors.
International Journal of Remote Sensing | Year: 2010

Medium-spatial-resolution satellite images have already proved to be successful in automatic production of global land-cover maps. However, their operational use for land-covermapping at a national scale has not yet been well established.We find that the reasons for this are not data-source dependent, but are due to the landcover nomenclatures properties adopted, regional landscape specificities and the methodological approaches used. The aim of this paper is to evaluate the suitability for national applications of land-cover maps derived from automatic classification of medium-spatial-resolution satellite images. To tackle this issue, we produce a land-cover map of Continental Portugal from multitemporal MEdium Resolution Imaging Spectrometer (MERIS) full-resolution satellite images of 2005 and evaluate its accuracy. For the accuracy assessment of the final map, we compute unbiased estimates of overall, user and producer accuracies using an independent testing sample collected through a stratified random sampling design. The overall accuracy of the final map is 80%, with an absolute precision of 2% at the 95% confidence level. High independent accuracy assessment results demonstrate that medium-spatial-resolution satellite images can be used on an operational basis for annual production of land-cover maps suitable for national applications. © 2010 Taylor & Francis. Source


Goncalves L.M.S.,Polytechnic Institute of Leiria | Goncalves L.M.S.,Polytechnic Institute of Coimbra | Fonte C.C.,Polytechnic Institute of Coimbra | Fonte C.C.,University of Coimbra | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

The classification of remote sensing images performed with different classifiers usually produces different results. The aim of this paper is to investigate whether the outputs of different soft classifications may be combined to increase the classification accuracy, using the uncertainty information to choose the best class to assign to each pixel. If there is disagreement between the outputs obtained with the several classifiers, the proposed method selects the class to assign to the pixel choosing the one that presents less uncertainty. The proposed approach was applied to an IKONOS image, which was classified using two supervised soft classifiers, the Multi-layer Perceptron neural network classifier and a fuzzy classifier based on the underlying logic of the Minimum-Distance-to-Means. The overall accuracy of the classification obtained with the combination of both classifications with the proposed methodology was higher than the overall accuracy of the original classifications, which shows that the methodology is promising and may be used to increase classification accuracy. © 2010 Springer-Verlag Berlin Heidelberg. Source


Fonte C.C.,University of Coimbra | Fonte C.C.,INESC Coimbra | Dinis J.,Portuguese Geographic Institute IGP | Goncalves L.M.S.,INESC Coimbra | And 2 more authors.
European Space Agency, (Special Publication) ESA SP | Year: 2012

The aim of this work is to establish consistent training sets using available low resolution ancillary data from the study area, which may be land cover maps with low spatial resolution. An initial training set is formed by a set of points randomly chosen from the land cover classes existing in the ancillary data. In a first phase some initial improvement of the training sets is done using the Normalized Difference Vegetation Index (NDVI). Three approaches are then tested to improve the initial training set using respectively the Maximum Likelihood Classifier, a classifier based on Dempster-Shafer theory and another based on the Mahalanobis distance. The degrees of certainty and uncertainty obtained with the soft classifier associated to each class is used to eliminate from the training set pixels that are not representative of the classes or identify new more reliable training pixels. A subsequent classification of the entire scene is made with the reference datasets obtained with the different approaches and the accuracy of the results tested building a confusion matrix and computing the User's, Producer's and Overall Accuracy. The proposed methodology is applied to a case study. Source


Goncalves L.M.S.,Polytechnic Institute of Leiria | Fonte C.C.,University of Coimbra | Fonte C.C.,Polytechnic Institute of Coimbra | Julio E.N.B.S.,University of Coimbra | And 2 more authors.
International Journal of Remote Sensing | Year: 2010

The aim of this paper was to investigate the usefulness of non-specificity uncertainty measures to evaluate soft classifications of remote sensing images. In particular, we analysed whether these measures could be used to identify the difficulties found by the classifier and to estimate the classification accuracy. Two nonspecificity uncertainty measures were considered, the non-specificity measure (NSp) and the U-uncertainty measure, and their behaviour was analysed to evaluate which is the most appropriate for this application. To overcome the fact that these two measures have different ranges, a normalized version (Un) of the U-uncertainty measure was used. Both measures were applied to evaluate the uncertainty of a soft classification of a very high spatial resolution multispectral satellite image, performed with an object-oriented image analysis based on a fuzzy classification. The classification accuracy was evaluated using an error matrix and the user's and producer's accuracies were computed. Two uncertainty indexes are proposed for each measure, and the correlation between the information given by them and the user's and producer's accuracies was determined to assess the relationship and compatibility of both sources of information. The results show that there is a positive correlation between the information given by the uncertainty and accuracy indexes, but mainly between the uncertainty indexes and the user's accuracy, where the correlation achieved 77%. This study shows that uncertainty indexes may be used, along with the possibility distributions, as indicators of the classification performance, and may therefore be very useful tools. © 2010 Taylor & Francis. Source


Goncalves L.M.S.,Polytechnic Institute of Leiria | Goncalves L.M.S.,Polytechnic Institute of Coimbra | Fonte C.C.,Polytechnic Institute of Coimbra | Fonte C.C.,University of Coimbra | And 3 more authors.
International Journal of Remote Sensing | Year: 2012

The production of thematic maps from remotely sensed images requires the application of classification methods. A great variety of classifiers are available, producing frequently considerably different results. Therefore, the automatic extraction of thematic information requires the choice of the most appropriate classifier for each application. One of the main objectives of the research described in this article is to evaluate the performance of supervised classifiers using the information provided by the application of uncertainty measures to the testing sets, instead of statistical accuracy indices. The second main objective is to show that the information provided by the uncertainty measures for the training set may be used to assess and redefine the sample sites included in this set, in order to improve the classification results. To achieve the proposed objectives, two supervised classifiers, one probabilistic and another fuzzy, were applied to a very high spatial resolution (VHSR) image. The results show that similar conclusions on the classifiers' performance are obtained with the uncertainty measures and the traditional accuracy indices obtained from error matrices. It is also shown that the redefinition of the training set based on the information provided by the uncertainty measures may generate more accurate outputs. © 2012 Taylor and Francis Group, LLC. Source

Discover hidden collaborations