ADAPTIVE SPATIAL SAMPLING WITH ACTIVE RANDOM FOREST FOR OBJECT-ORIENTED LANDSLIDE MAPPING

被引：3

作者：

Stumpf, A. ^{[1
,3
]}

Lachiche, N. ^{[2
,4
]}

Kerle, N. ^{[3
]}

Malet, Jean-Philippe ^{[2
]}

Puissant, A. ^{[1
]}

机构：

[1] Univ Strasbourg, Lab Image, CNRS ERL 7230, 3 Rue Argonne, F-67083 Strasbourg, France

[2] Univ Strasbourg EOST, CNRS UMR 7516, Inst Phys Globe, F-67084 Strasbourg, France

[3] Univ Twente, ITC Fac Geo Informat Sci & Earth Observ, Dept Earth Syst Anal, NL-7500 AA Enschede, AA, Netherlands

[4] Univ Strasbourg, CNRS UMR 7005, Comp Sci & Remote Sensing Lab, Image Sci, F-67412 Illkirch Graffenstaden, France

来源：

2012 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2012年

基金：

欧盟第七框架计划;

关键词：

active learning; spatial sampling landslide inventory mapping; object-oriented image analysis;

D O I：

10.1109/IGARSS.2012.6351630

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Active learning (AL) is a powerful framework to reduce labeling costs in supervised classification. However, spatial constraints on the sampling design have not yet received much attention and still pose problems for the application of AL on remote sensing data. In this study such issues are addressed in the context of landslide inventory mapping and it is shown that region-based query functions that focus the labeling efforts on compact spatial batches may provide several advantages over point-wise queries.

引用

页码：87 / 90

页数：4

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