For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Improved Contextual Classifiers of Multispectral Image Data
Takashi WATANABE Hitoshi SUZUKI Sumio TANBA Ryuzo YOKOYAMA
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 1994/09/25
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section of Papers Selected from the 8th Digital Signal Processing Symposium)
Category: Image Processing
contextual classification, multispectral image, remote sensing, probabilistic relaxation,
Full Text: PDF>>
Contextual classification of multispectral image data in remote sensing is discussed and concretely two improved contextual classifiers are proposed. The first is the extended adaptive classifier which partitions an image successively into homogeneously distributed square regions and applies a collective classification decision to each region. The second is the accelerated probabilistic relaxation which updates a classification result fast by adopting a pixelwise stopping rule. The evaluation experiment with a pseudo LANDSAT multispectral image shows that the proposed methods give higher classification accuracies than the compound decision method known as a standard contextual classifier.