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A multiscale texture analysis procedure for improved forest stand classification
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DOI：10.1080/0143116042000192367.png)
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En 中文
Image texture is a complex visual perception. With the ever-increasing spatial resolution of remotely sensed data, the role of image texture in image classification has increased. Current approaches to image texture analysis rely on a single band of spatial information to characterize texture. This paper presents a multiscale approach to image texture where first and second-order statistical measures were derived from different sizes of processing windows and were used as additional information in a supervised classification. By using several bands of textural information processed with different window sizes (from 565 to 15615) the main forest stands in the image were improved up to a maximum of 40%. A geostatistical analysis indicated that there was no single window size that would adequately characterize the range of textural conditions present in this image. A number of different statistical texture measures were compared for this image. While all of the different texture measures provided a degree of improvement (from 4 to 13% overall), the multiscale approach achieved a higher degree of classification accuracy regardless of which statistical procedure was used. When compared with single band texture measures, the level of overall improvement varied between 4 and 8%. The results indicate that this multiscale approach is an improvement over the current single band approach to analysing image texture.
Keyword:
LAND-USE CLASSIFICATION
REMOTELY-SENSED IMAGES
SPOT HRV
CONTEXTUAL CLASSIFICATION
SPATIAL-RESOLUTION
DISCRIMINATION
SEGMENTATION
MODELS
VARIOGRAM
REDUCTION
期刊
IF:
2.6
论文数:
1.2W
被引数:
2.7W
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