Support Vector Data Description Model to Map Specific Land Cover with Optimal Parameters Determined from a Window-Based Validation Set
This paper developed an approach, the window-based validation set for support vector data description (WVS-SVDD), to determine optimal parameters for support vector data description (SVDD) model to map specific land cover by integrating training and window-based validation sets. Compared to the conventional approach where the validation set included target and outlier pixels selected visually and randomly, the validation set derived from WVS-SVDD constructed a tightened hypersphere because of
... e compact constraint by the outlier pixels which were located neighboring to the target class in the spectral feature space. The overall accuracies for wheat and bare land achieved were as high as 89.25% and 83.65%, respectively. However, target class was underestimated because the validation set covers only a small fraction of the heterogeneous spectra of the target class. The different window sizes were then tested to acquire more wheat pixels for validation set. The results showed that classification accuracy increased with the increasing window size and the overall accuracies were higher than 88% at all window size scales. Moreover, WVS-SVDD showed much less sensitivity to the untrained classes than the multi-class support vector machine (SVM) method. Therefore, the developed method showed its merits using the optimal parameters, tradeoff coefficient (C) and kernel width (s), in mapping homogeneous specific land cover. Sensors 2017, 17, 960 2 of 15 the user-interested class as a target, and other classes as outliers. The one-class classifier is designed to extract the interested land-cover class using a small training set including only the target class, thus it can efficiently reduce the hard and redundant work to collect all classes of ground training data for multi-class classification     . The support vector data description (SVDD) method, a boundary method developed by Tax and Duin, creates a hypersphere which is the decision boundary in a high-dimensional feature space such that it encloses most target objects and rejects outliers  . This method shows excellent ability in mapping specific land-cover distribution    . Foody and Mathur  demonstrated that the SVDD could achieve satisfying land-cover accuracy, which had little difference in terms of accuracy compared with multi-class support vector machine (SVM) classifications. Sanchez-Hernandez et al.  introduced SVDD classification to map fenland, which outperformed the conventional multiclass maximum-likelihood classification algorithm. also analyzed and compared the applicability of different one-classifiers, and concluded that the SVDD classifier yielded the best crop classification with respect to other one-classifiers when applied to multi-spectral, hyperspectral and SAR (Synthetic Aperture Radar) data. Niazmardi et al.  combined Fuzzy C-means with SVDD for unsupervised hyperspectral data classification, which obtained acceptable results with high dimensional data. Uslu et al.  presented ensemble methods for improving classification performance of SVDD in the remotely sensed hyperspectral imagery data. Based on the SVDD's principle, the tradeoff coefficient C and kernel width s, are two critical parameters that affect the shape of hypersphere  . The C is defined as the ratio of target objects to outlier objects in a training sample set and the kernel width s is to control the compactness of hypersphere. When the s value is fixed, the reduction of C causes a shrinking hypersphere and more target objects would be rejected as outliers. When C is set as constant, smaller s contributes to an over-tight boundary around the training sample set whereas a very loose one would be derived with higher s value. Previous research demonstrated that the accuracy of one-class classification is very sensitive to these parameters. In particular, when the spectral mixture between the target class and outliers is significant, classification errors may be relatively high with inappropriate parameters. However, little attention was drawn to determining the appropriate parameters, C and s, for the SVDD model to ensure land-cover mapping performance. In this paper, an innovative approach was developed to improve SVDD performance for specific land cover by optimizing classification parameters using a window-based validation set. The simulated annealing (SA) search algorithm was employed to determine optimal parameters. Hereafter, the method of window-based validation set for SVDD is abbreviated as WVS-SVDD. The remainder of this paper is organized as follows. The modules of the proposed method are introduced in Section 2. Then, experiments are conducted to test the performance of WVS-SVDD on different spatial resolution remote sensing images in Section 3. Finally, the conclusions are drawn in Section 4. WVS-SVDD: Window-Based Validation Set for SVDD The proposed method includes four modules ( Figure 1 ): (1) training set selection; (2) validation set selection; (3) optimized parameter determination using simulated annealing (SA) algorithm; and (4) SVDD-based specific land-cover classification. Each module is described in the following sections.