A novel information transferring approach for the classification of remote sensing images

Fengchen Huang,Jie Shen,Lizhong Xu,Jianqiang Gao,Feng Xu

doi:10.1186/s13634-015-0223-0

Fengchen Huang, Jie Shen + Show 3 more

Open Access

https://doi.org/10.1186/s13634-015-0223-0

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Abstract

Traditional remote sensing images classification methods focused on using a large amount of labeled target data to train an efficient classification model. However, these approaches were generally based on the target data without considering a host of auxiliary data or the additional information of auxiliary data. If the valuable information from auxiliary data could be successfully transferred to the target data, the performance of the classification model would be improved. In addition, from the perspective of practical application, these valuable information from auxiliary data should be fully used. Therefore, in this paper, based on the transfer learning idea, we proposed a novel information transferring approach to improve the remote sensing images classification performance. The main rationale of this approach is that first, the information of the same areas associated with each pixel is modeled as the intra-class set, and the information of different areas associated with each pixel is modeled as the inter-class set, and then the obtained texture feature information of each area from auxiliary is transferred to the target data set such that the inter-class set is separated and intra-class set is gathered as far as possible. Experiments show that the proposed approach is effective and feasible.

Highlights

Remote sensing images classification is a complex process that may be affected by many factors, such as the availability of high-quality images, proper classification method, and the analytical ability of scientists
In [1], the authors built textural information model that use spatial information, and proposed a wavelet-based multi-scale strategy to characterize local texture, taking the physical nature of the data into account, the extracted textural information was used as new feature to build a texture kernel and the final kernel was the weighted sum of a kernel made with the spectral information and the texture kernel
Fauvel et al [3] proposed a spatial-spectral kernel-based approach with the spatial and spectral information were jointly used for the classification

Summary

Introduction

Remote sensing images classification is a complex process that may be affected by many factors, such as the availability of high-quality images, proper classification method, and the analytical ability of scientists. In [6], Dos Santos J.A. et al proposed a method for interactive classification of remote sensing images considering multiscale segmentation. Their aim is to improve the selection of training samples using the features from the most appropriate scales of representation. They use a boosting-based active learning strategy to select regions at various scales for user’s relevance feed back. These approaches may ignore the auxiliary data of the remote sensing images. We aim to transfer the texture feature information from the auxiliary data to

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