An Automatic Approach for Remote Sensing Classification Supported by Sample Transfer

doi:10.13474/j.cnki.11-2246.2018.0110

Abstract

Abstract:

This paper proposes a novel automatic classification approach to acquire land cover maps by classifying remote sensing images in the time series.Different from other sample collection methods,the proposed method tries to define a precise training set for target domain automatically by transfer learning.This method is proposed for Landsat TM images.This is done by change detection method and spectral curve shape vector.Firstly,the unchanged labels are located by change vector analysis.Then the prior class knowledge from source domain is transferred to the target images,taking advantage of the already available knowledge on the land cover products related to source images.Finally,the target image is classified by support vector machine.The result shows that the approach is effective in automatically obtaining land-cover classification maps.

Key words: transfer learning, land cover, change detection, spectral curve shape vector, automatic classification

CLC Number:

P237

LIN Cong, LI Erzhu, DU Peijun. An Automatic Approach for Remote Sensing Classification Supported by Sample Transfer[J]. 测绘通报, 2018, 0(4): 57-62.

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