-*- mode: org -*- * 2(<-481): Improving Hyperspectral Matching Method through Feature-Selection/Weighting Based on SVM In the present article, feature selection/weighting based on SVM was employed to improve the algorithm of choosing reference spectrum through a multi-objective optimization approach proposed in reference [18]. Based on the sensitive analysis, half of features having low weights in SVM classification model were eliminated iteratively. Two criteria, matching accuracy and classification confidence, were used to select the best-performing feature subset. Three scenarios were designed: (1) only feature subset selected by SVM was used; (2) both feature subset and global weights were used, in which global weights were the coefficients of selected features in the SVM classification model; (3) both feature subset and local weights, which changed with the distance of a sample point to the SVM separation plan, were used. Experiment executed on the popular Indiana AVIRIS data set indicate that under all the three scenarios, spectral matching accuracies were increased by 13%-17% compared to the situation without feature selection. The result obtained under scenario 3 is the most accurate and the most stable, which can be primarily attributed to the ability of local weights to accurately describe local distribution of spectra from the same class in feature space. Moreover, scenario 3 can be regarded as the extension of scenario 2 because when spectra far away from the separation plane are selected as reference spectrums for matching, the features' weights will not be considered. The results obtained under scenario 1 and 2 are very similar, indicating that considering global weights is not necessary. The research presented in this paper advanced the spectrum analysis using SVM to a higher level. 2009 * 5(<-462): Automatic Ground-Truth Validation With Genetic Algorithms for Multispectral Image Classification In this paper, we propose a novel method that aims at assisting the ground-truth expert through an automatic detection of potentially mislabeled learning samples. This method is based on viewing the mislabeled sample detection issue as an optimization problem where it is looked for the best subset of learning samples in terms of statistical separability between classes. This problem is formulated within a genetic optimization framework, where each chromosome represents a candidate solution for validating/invalidating the learning samples collected by the ground-truth expert. The genetic optimization process is guided by the joint optimization of two different criteria which are the maximization of a between-class statistical distance and the minimization of the number of invalidated samples. Experiments conducted on both simulated and real data sets show that the proposed ground-truth validation method succeeds in the following: 1) in detecting the mislabeled samples with a high accuracy, even when up to 30% of the learning samples are mislabeled, and 2) in strongly limiting the negative impact of the mislabeling issue on the accuracy of the classification process. 2009 * 7(<-474): Unsupervised Pixel Classification in Satellite Imagery Using Multiobjective Fuzzy Clustering Combined With SVM Classifier The problem of unsupervised classification of a satellite image in a number of homogeneous regions can be viewed as the task of clustering the pixels in the intensity space. This paper proposes a novel approach that combines a recently proposed multiobjective fuzzy clustering scheme with support vector machine (SVM) classifier to yield improved solutions. The multiobjective technique is first used to produce a set of nondominated solutions. The nondominated set is then used to find some high-confidence points using a fuzzy voting technique. The SVM classifier is thereafter trained by these high-confidence points. Finally, the remaining points are classified using the trained classifier. Results demonstrating the effectiveness of the proposed technique are provided for numeric remote sensing data described in terms of feature vectors. Moreover, two remotely sensed images of Bombay and Calcutta cities have been classified using the proposed technique to establish its utility. 2009 * 10(<- 76): Machine learning methods for sub-pixel land-cover classification in the spatially heterogeneous region of Flanders (Belgium): a multi-criteria comparison Until now, few research has addressed the use of machine learning methods for classification at the sub-pixel level. To close this knowledge gap, in this article, six machine learning methods were compared for the specific task of sub-pixel land-cover extraction in the spatially heterogeneous region of Flanders (Belgium). In addition to the classification accuracy at the pixel and the municipality level, three evaluation criteria reflecting the methods' ease-of-use were added to the comparison: the time needed for training, the number of meta-parameters, and the minimum training set size. Robustness to changing training data was also included as the sixth evaluation criterion. Based on their scores for these six criteria, the machine learning methods were ranked according to three multi-criteria ranking scenarios. These ranking scenarios correspond to different decision-making scenarios that differ in their weighting of the criteria. In general, no overall winner could be designated: no method performs best for all evaluation scenarios. However, when both time available for preprocessing and the magnitude of the training data set are unconstrained, Support Vector Machines (SVMs) clearly outperform the other methods. 2015