Spectral texture for improved class discrimination in complex terrain
作者:
STEVENE. FRANKLIN,
DEREKR. PEDDLE,
期刊:
International Journal of Remote Sensing
(Taylor Available online 1989)
卷期:
Volume 10,
issue 8
页码: 1437-1443
ISSN:0143-1161
年代: 1989
DOI:10.1080/01431168908903979
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
A spatial co-occurrence algorithm has been used to derive image texture from Landsat Multispectral Scanner (MSS) data to increase classification accuracy in a moderate relief, boreal environment in eastern Canada. The aim was to investigate ‘data-driven improvements’, including those available through digital elevation modelling. Overall classification accuracy using MSS data alone was 59·1 per cent when compared to a biophysical inventory of the area compiled primarily by aerial photointerpretation. This increased to 66·2 per cent with MSS plus texture and to 89·8 per cent when MSS data were analysed with geomorphometry extracted from a digital elevation model (DEM). The introduction of MSS texture resulted in statistically significant increases in individual class accuracies in classes that were also well defined using the geomorphometric and integrated data sets. This suggested that some of the additional information provided by geomorphometry was also contained in spectral texture. It was also noted that individual texture orientations resulted in higher class accuracies than average texture measures; this is probably related to structural (slope/aspect) characteristics of specific vegetation communities.
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