Topic Modle On Image Classification And Their Applications In High Spatial Resolution Remote Sensing Image

The development of sensor technology makes us obtain more and more high spatial resolution remote sensing images. But one of the most important problems that face us is how to extract low-level features and classify huge amount of remote sensing images. Recently, object-based method has been widely used in remote image processing and analysis instead of pixel-based method. Then the low-level visual feature is directly used for object classification. However, one challenge is how to bridge the semantic gap between low-level visual features and high-level semantic features when using object-based method. In order to apply the semantic content for better classification performance, the dissertation follows the routine of"bag-of-word (BOW), topic model and object classification", where we research the image objects from the low-level visual feature to BOW representation to middle-level semantic topics.Two main steps are used to achieve visual semantic representation and image classification. Firstly, the work organizes the local feature to obtain visual words and construct the BOW representation. Secondly, the work uses topic model to reveal underlying topics, which are used to classify images as middle-level semantic information. The concrete contributions are listed as follows:1. The multi-scale visual vocabulary is proposed. The bag-of-word method is introduced to high resolution remote sensing image. To reduce visual word ambiguity, the"virtual word"and multi-scale words are proposed. The"virtual word"is generated to expand visual vocabulary, where all uncertain image features would be mapped to virtual word. And the multi-scale visual words based on image scale pyramid are used not only to enrich representative ability of visual words, but also to associate image regions within a local region. Experimental results show that the classification performance with multi-scale BOW representation is better than the classification performance with low-level visual feature and traditional BOW representation.2. The hierarchical topic model based on multi-scale BOW representation is proposed to organize the object hierarchy. The scale information in multi-scale BOW is added into hierarchical Latent Dirichlet Allocation model as constraints in model initialization. Then, the proposed model assumes that visual words at coarse scale outline the image contents, and visual words at fine scale describe the detailed contents, which simulates the coarse-to-fine image understanding process. Comparison to the traditional topic models and SVM classifier, our model obtains the higher classification performance and even constructs the hierarchy consistent to the results by human recognition.3. The semi-supervised topic model is proposed to learn both the labeled and unlabeled training data. The proposed model unifies the supervised and unsupervised LDA models into the same generative process, and weights the contribution of these two topic models to create a semi-supervised LDA model. Thus, the semi-supervised model not only improves the classification performance with few labeled samples and many unlabeled samples, but also extends a new application.