Browsing by Author "Caetano, Mario"
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- Assessment of the state of conservation of buildings through roof mapping using very high spatial resolution imagesPublication . Gonçalves, Luísa M. S.; Fonte, Cidália C.; Júlio, Eduardo N. B. S.; Caetano, MarioThe assessment of the state of conservation of buildings is extremely important in urban rehabilitation. In the case of historical towns or city centres, the pathological characterization using traditional methods is a laborious and time consuming procedure. This study aims to show that Very High Spatial Resolution (VHSR) multispectral images can be used to obtain information regarding the state of conservation of roofs where, usually, building degradation starts. The study was performed with multispectral aerial images with a spatial resolution of 0.5 m. To extract the required information, a hybrid classification method was developed, that integrates pixel and object based classification methods, as well as information regarding the classification uncertainty. The proposed method was tested on the classification of the historical city centre of Coimbra, in Portugal, that includes over than 800 buildings. The results were then validated with the data obtained from a study conducted during 2 years by a nine element team from the University of Coimbra, using traditional methods. The study performed achieved a global classification accuracy of 78%, which proves that the state of conservation of roofs can be obtained from VHSR multispectralimages using the described methodology with a fairly good accuracy.
- Evaluation of soft possibilistic classifications with non-specificity uncertainty measuresPublication . Gonçalves, Luisa M. S.; Fonte, Cidália C.; Júlio, Eduardo N. B. S.; Caetano, MarioThe 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.
- A method to incorporate uncertainty in the classification of remote sensing imagesPublication . Gonçalves, Luísa M. S.; Fonte, Cidália C.; Júlio, Eduardo N. B. S.; Caetano, MarioThe aim of this paper is to investigate if the incorporation of the uncertainty associated with the classification of surface elements into the classification of landscape units (LUs) increases the results accuracy. To this end, a hybrid classification method is developed, including uncertainty information in the classification of very high spatial resolution multi-spectral satellite images, to obtain a map of LUs. The developed classification methodology includes the following steps: (1) a pixel-based hard classification with a probabilistic Bayesian classifier; (2) computation of the posterior probabilities and quantification of the classification uncertainty using an uncertainty measure; (3) image segmentation and (4) object classification based on decision rules. The classification of the resulting objects into LUs is performed considering a set of decision rules that incorporate the pixelbased classification uncertainty. The proposed methodology was tested on the classification of an IKONOS satellite image. The accuracy of the classification was computed using an error matrix. The comparison between the results obtained with the proposed approach and those obtained without considering the classification uncertainty revealed a 12% increase in the overall accuracy. This shows that the information about uncertainty can be valuable when making decisions and can actually increase the accuracy of the classification results.
- On the information provided by uncertainty measures in the classification of remote sensing imagesPublication . Gonçalves, Luisa; Fonte, Cidália C.; Júlio, Eduardo N.B.S.; Caetano, MarioThis paper investigates the potential information provided to the user by the uncertainty measures applied to the possibility distributions associated with the spatial units of an IKONOS satellite image, generated by two fuzzy classifiers, based, respectively, on the Nearest Neighbour Classifier and the Minimum Distance to Means Classifier. The deviation of the geographic unit characteristics from the prototype of the class to which the geographic unit is assigned is evaluated with the Un non-specificity uncertainty measures proposed by [1] and the exaggeration uncertainty measure proposed by [2]. The classifications were evaluated using accuracy and uncertainty indexes to determine their compatibility. Both classifications generated medium to high levels of uncertainty for almost all classes, and the global accuracy indexes computed were 70% for the Nearest Neighbour Classifier and 53% for the Minimum Distance to Means Classifier. The results show that similar conclusions can be obtained with accuracy and uncertainty indexes and the latter, along with the analysis of the possibility distributions, may be used as indicators of the classification performance and may therefore be very useful tools. Since the uncertainty indexes may be computed to all spatial units, the spatial distribution of the uncertainty was also analysed. It's visualization shows that regions where less reliability is expected present a great amount of detail that may be potentially useful to the user.
- The application of uncertainty measures in the training and evaluation of supervised classifiersPublication . Gonçalves, Luísa M. S.; Fonte, Cidália C.; Júlio, Eduardo N. B. S.; Caetano, MarioThe 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.
- Using Uncertainty Information to Combine Soft ClassificationsPublication . Gonçalves, Luisa M. S.; Fonte, Cidália C.; Caetano, MarioThe 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.