Object-oriented ensemble classification for polarimetric SAR Imagery using restricted Boltzmann machines

Abstract

A series of deep learning algorithms have recently shown excellent performances in many different fields. Deep models are usually generated by stacking similar modules, e.g., restricted Boltzmann machines (RBMs), and they are especially suitable for discriminating complex objects through the use of ‘big data’. However, object-oriented classification (OOC) for polarimetric synthetic aperture radar (PolSAR) imagery is based on homogeneous regions instead of pixels, which results in a degraded performance for the deep models, as the data volume is inadequate. To solve this problem, we adopt an RBM as the module, and use it to construct an adaptive boosting (AdaBoost) model instead of a stacked deep model, to carry out OOC for PolSAR imagery. The experimental results demonstrate that the proposed model is superior to the stacked RBM model and the other common methods for OOC.