An Automatic Sample Collection Method for Object-oriented Classification of Remotely Sensed Imageries Based on Transfer Learning

doi:10.13485/j.cnki.11-2089.2014.0163

Abstract

Abstract:

For the large-scale remote sensing applications, the automatic classification of remotely sensed imageries is still a challenge. For example, the artificial sample collection scheme cannot meet the needs of automatic information extraction from the remotely sensed imageries. In order to establish a prior knowledge-based and fully automatic classification method, an automatic sample collection method for object-oriented classification, with the introduction of data mining to the process of information extraction, is proposed. Firstly, the unchanged landmarks are located. Then the prior class knowledge from old interpreted thematic images is transferred to the new target images. And the above knowledge is then used to rebuild the relationship between landmark classes and their spatial-spectral features. The results show that, with the assist of preliminary thematic data, the approach can automatically obtain reliable object samples for object-oriented classification. The accuracy of the classified land-cover types and the efficiency of object-oriented classification are both improved.

Key words: automation, land-cover, object-oriented classification, sample collection, change detection, transfer learning

CLC Number:

TP753

WU Tianjun LUO Jiancheng XIA Liegang YANG Haiping SHEN Zhanfeng HU Xiaodong. An Automatic Sample Collection Method for Object-oriented Classification of Remotely Sensed Imageries Based on Transfer Learning[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(9): 908-916.

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