| Summary: | 碩士 === 國立成功大學 === 測量工程學系 === 85 === Remote sensing images have some characteristics ，such as real
timeand automatic data extraction，which can provide an
important resourceof data collection and adapting for
geographical information system.If image segmentation
techniques are used to process remote sensing images,such as
image segmentation ,we can integrate the segmentation result
withpolygon data and corresponding attribute data from the
database ofgeographical information system ,and overlay analysis
is applied to helpthe recognition of land-cover, the
classification result of remote sensingimages can be improved
also.Regions in an image can be distinguished from the others
based on the differences of their textures. For example,
different land-cover categories(residential district, water body
,forest area)in remote sensing images havedifferent textures. We
use a asymmetric Daubechies orthogonal wavelet functionfor
wavelet decomposition to extract texture features from images by
means ofmultiresolution analysis. This method provides some
advantages, such assimple computational model, orientation
sensitive and multiresolutionanalysis of texture features.After
using wavelet decomposition to extract texture features from
images ,ISODATA clustering algorithm is used to accomplish image
segmentation ,whichcan be regarded as one of the multichannel
segmentation algorithms .Finally, the accuracy of segmentation
result is estimated by comparing with reference images.Thsee
kinds of images are selected for experiment. The first
experimentalimage comprises three kinds of artificial textures .
The overall accuracyof the segmentation is 93%,and the κindex
is 90%. The second experimentalimage comprises four kinds of
ground surface textures . The overall accuracyof the
segmentation is 94%,and the κindex is 92%. The third
experimentalimage is the multispectral remote sensing image of
experimental area . Theoverall accuracy of the segmentation is
82%,and the κindex is 72%.This research examines the
feasibility of using wavelet decomposition toaccomplish textured
image segmentation. We expect that the segmentationresult can be
improved by integrating region-based segmentation
techniquewhich is used in this research and edge-based
segmentation techniques.
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