Abstract
Abstract. Topic modeling has been an increasingly mature method to bridge the semantic gap between the low-level features and high-level semantic information. However, with more and more high spatial resolution (HSR) images to deal with, conventional probabilistic topic model (PTM) usually presents the images with a dense semantic representation. This consumes more time and requires more storage space. In addition, due to the complex spectral and spatial information, a combination of multiple complementary features is proved to be an effective strategy to improve the performance for HSR image scene classification. But it should be noticed that how the distinct features are fused to fully describe the challenging HSR images, which is a critical factor for scene classification. In this paper, a semantic-feature fusion fully sparse topic model (SFF-FSTM) is proposed for HSR imagery scene classification. In SFF-FSTM, three heterogeneous features – the mean and standard deviation based spectral feature, wavelet based texture feature, and dense scale-invariant feature transform (SIFT) based structural feature are effectively fused at the latent semantic level. The combination of multiple semantic-feature fusion strategy and sparse based FSTM is able to provide adequate feature representations, and can achieve comparable performance with limited training samples. Experimental results on the UC Merced dataset and Google dataset of SIRI-WHU demonstrate that the proposed method can improve the performance of scene classification compared with other scene classification methods for HSR imagery.
Highlights
The rapid development of earth observation and remote sensing techniques has led to large amount of high spatial resolution (HSR) images with abundant spatial and structural information
Inspired by the aforementioned work, we present a semanticfeature fusion fully sparse topic model (SFF-Fully sparse topic model (FSTM)) for HSR image scene classification
The combination of multiple semantic-feature fusion strategy and sparse representation based FSTM is able to trade off sparsity and the quality of sparse inferred semantic information as well as inferring time, and presents a comparable performance with the existed relevant method
Summary
The rapid development of earth observation and remote sensing techniques has led to large amount of high spatial resolution (HSR) images with abundant spatial and structural information. The same type of scenes may consist of different types of simple objects These methods which are based on the low-level features are unable to capture the complex semantic concepts of different scene images. Based on the similarity of documents and images, FSTM is able to remove the redundant information and infer sparse semantic representations with shorter inference time In this way, to acquire sparse latent topics, we intended to use a limited number of images as training sample which is more in line with the practical application. The combination of multiple semantic-feature fusion strategy and sparse representation based FSTM is able to trade off sparsity and the quality of sparse inferred semantic information as well as inferring time, and presents a comparable performance with the existed relevant method.
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