Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images
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- 作者：Puzhao Zhang ; Maoguo Gong ; gong@ieee.org" class="auth_mail" title="E-mail the corresponding author ; Linzhi Su ; Jia Liu ; Zhizhou Li
- 关键词：Change detection ; Spatial-resolution ; Denoising autoencoder ; Stacked denoising autoencoder ; Deep neural networks ; Feature transformation
- 刊名：ISPRS Journal of Photogrammetry and Remote Sensing
- 出版年：2016
- 出版时间：June 2016
- 年：2016
- 卷：116
- 期：Complete
- 页码：24-41
- 全文大小：9153 K
文摘
Multi-spatial-resolution change detection is a newly proposed issue and it is of great significance in remote sensing, environmental and land use monitoring, etc. Though multi-spatial-resolution image-pair are two kinds of representations of the same reality, they are often incommensurable superficially due to their different modalities and properties. In this paper, we present a novel multi-spatial-resolution change detection framework, which incorporates deep-architecture-based unsupervised feature learning and mapping-based feature change analysis. Firstly, we transform multi-resolution image-pair into the same pixel-resolution through co-registration, followed by details recovery, which is designed to remedy the spatial details lost in the registration. Secondly, the denoising autoencoder is stacked to learn local and high-level representation/feature from the local neighborhood of the given pixel, in an unsupervised fashion. Thirdly, motivated by the fact that multi-resolution image-pair share the same reality in the unchanged regions, we try to explore the inner relationships between them by building a mapping neural network. And it can be used to learn a mapping function based on the most-unlikely-changed feature-pairs, which are selected from all the feature-pairs via a coarse initial change map generated in advance. The learned mapping function can bridge the different representations and highlight changes. Finally, we can build a robust and contractive change map through feature similarity analysis, and the change detection result is obtained through the segmentation of the final change map. Experiments are carried out on four real datasets, and the results confirmed the effectiveness and superiority of the proposed method.