An efficient unsupervised index based approach for mapping urban vegetation from IKONOS imagery

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• 作者：Julius Y Anchang ; Erick O. Ananga ; Ruiliang Pu ; ^{rpu@usf.edu" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Unsupervised classification ; Vegetation index ; Jenks natural breaks ; Hierarchical clustering ; Support vector machine ; IKONOS imagery
• 刊名：International Journal of Applied Earth Observation and Geoinformation
• 出版年：2016
• 出版时间：August 2016
• 年：2016
• 卷：50
• 期：Complete
• 页码：211-220
• 全文大小：2876 K

文摘

<dl class="listitem" id="list_lis0005"><dt class="label">•dt><dd>

d="par0005">There is a potential for a completely unsupervised mapping of urban vegetation cover from an IKONOS-derived vegetation Index.

dd><dt class="label">•dt><dd>

d="par0010">Two unsupervised techniques are investigated for extraction of urban trees/shrubs and ground vegetation cover types.

dd><dt class="label">•dt><dd>

d="par0015">Techniques investigated include the Jenks natural breaks classification and an innovative two-step clustering technique.

dd><dt class="label">•dt><dd>

d="par0020">The unsupervised techniques are simple, efficient and can be easily automated for use in low resource areas.

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