Support Vector Data Description Model to Map Specific Land Cover with Optimal Parameters Determined from a Window-Based Validation Set.

Jinshui Zhang,Xiufang Zhu,Guanyuan Shuai,Yaozhong Pan,Zhoumiqi Yuan

doi:10.3390/s17050960

Abstract

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 the 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.

Highlights

Land-cover thematic maps produced with remote sensing images have been used to record theEarth’s surface development processes and dramatic land use/cover changes impelled from natural and human factors [1,2,3]
The reason is that support vector data description (SVDD) does not need too many outlier classes to construct the hypersphere, whereas support vector machine (SVM) needs to create hyperplanes between the target class and each outlier class
This paper proposed a WVS-SVDD method that integrated a window-based validation set and an simulated annealing (SA)-based optimal C and s algorithm to map specific land cover

Summary

Introduction

Land-cover thematic maps produced with remote sensing images have been used to record theEarth’s surface development processes and dramatic land use/cover changes impelled from natural and human factors [1,2,3]. Land-cover thematic maps produced with remote sensing images have been used to record the. Exhaustively-labeled training data within images are required to map all land-cover types by supervised classification methods. Omission of any class would degrade classification performance because a pixel belonging to an untrained class would be erroneously allocated to one of the pre-defined classes in the training set [4]. Users are concerned about one specific class, such as the wetland or urban class. In such cases, conventional multi-class classification makes more of an effort to select training samples of non-target classes to meet the requirement of an exhaustive training set [5,6]. Much of the research has converted the solution from the traditional multi-class classification model to a one-class classification model which defines

Results

Discussion

Conclusion