PROBABILISTIC LAND COVER CLASSIFICATION APPROACH TOWARD KNOWLEDGE-BASED SATELLITE DATA INTERPRETATIONS

被引：2

作者：

Hashimoto, Shutaro ^{[1
]}

Tadono, Takeo

Onosato, Masahiko ^{[1
]}

Hori, Masahiro

Moriyama, Takashi

机构：

[1] Hokkaido Univ, Grad Sch Informat Sci & Technol, Sapporo, Hokkaido 0600814, Japan

来源：

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

关键词：

land cover classification; machine learning; probabilistic inference; generative model; knowledge-based system; REMOTE-SENSING DATA; INFORMATION;

D O I：

10.1109/IGARSS.2012.6351247

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：

The recognition of concepts that we human beings are able to locate within satellite imagery requires analysis based on the particular context using knowledge. In this paper, we present a supervised pixel-based classification approach toward utilization of the classification results in knowledge-based satellite data interpretation system. The proposed approach is based upon a generative model, which is able to output the classification results with their probabilities and subsequently utilize them in detailed analysis. The experiment of classification was performed to demonstrate characteristics of the approach.

引用

页码：1513 / 1516

页数：4

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