OBJECT-BASED FOREST CHANGE DETECTION USING HIGH RESOLUTION SATELLITE IMAGES

被引：0

作者：

Chehata, Nesrine ^{[1
]}

Orny, Camille ^{[1
,2
]}

Boukir, Samia ^{[1
]}

Guyon, Dominique ^{[2
]}

机构：

[1] Bordeaux Univ, G&E Lab, ENSEGID, F-33607 Pessac, France

[2] INRA, EPHYSE Lab, F-33140 Villenave Dornon, France

来源：

PIA11: PHOTOGRAMMETRIC IMAGE ANALYSIS, 2011 | 2011年 / 38-3卷 / W22期

关键词：

multitemporal classification; segmentation; feature selection; change detection; forest damage; TEXTURE; FEATURES;

D O I：

暂无

中图分类号：

P9 [自然地理学];

学科分类号：

0705 ; 070501 ;

摘要：

An object-based approach for forest disaster change detection using High Resolution (HR) satellite images is proposed. An automatic feature selection process is used to optimize image segmentation via an original calibration-like procedure. A multitemporal classification then enables the separation of wind-fall from intact areas based on a new descriptor that depends on the level of fragmentation of the detected regions. The mean shift algorithm was used in both the segmentation and the classification processes. The method was tested on a high resolution Formosat-2 multispectral satellite image pair acquired before and after the Klaus storm. The obtained results are encouraging and the contribution of high resolution images for forest disaster mapping is discussed.

引用

页码：49 / 54

页数：6

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