A New Framework for Hyperspectral Image Classification Using Multiple Semisupervised Collaborative Classification Algorithm

Hyperspectral images (HSIs) have evident advantages in image understanding because of enormous spectral bands, and rich spatial information. However, applying the limited labeled samples to obtain satisfactory classification results is a challenging task. Secondary screening algorithm and semisupervised learning are two promising methods to address this problem. Secondary screening algorithm exploits different query functions, which are on the basis of the evaluation of two criteria: uncertainty and diversity. The advantage of semisupervised learning is that with a small number of samples, classifiers could learn the structure of whole data sets without significant costs and efforts. Hence, combining secondary screening algorithm and semisupervised learning is a natural consideration. We firstly investigate nine secondary screening algorithms and compare their performance. Next, two novel frameworks are proposed in this paper. They are named the syncretic one-fold secondary screening algorithm and semisupervised learning framework (OFSS-SL) and syncretic multiple secondary screening algorithms and multiple-verification semisupervised learning framework (MSS-MVSL), respectively. We evaluate the performance of OFSS-SL and MSS-MVSL on three hyperspectral data sets and compare them with that of three state-of-the-art classification methods. In general, our results suggest that two proposed frameworks can apply limited labeled samples to achieve excellent classification results. And the computational costs of them are cheaper than previous methods.