Segmentation of remotely sensed images with fuzzy thresholding, and quantitative evaluation

Pal, Sankar K.; Ghosh, Ashish; Shankar, B. Uma

doi:10.1080/01431160050029567

Cited by 160 publications

(82 citation statements)

References 40 publications

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“…Here, 80% of the design set is used for training. Table V provides the classwise and overall values of an index, called [7], which is the ratio of the total variation and within class variation. The higher this value is, the better the performance of the classifier.…”

Section: Pixel Classification Of Spot Imagementioning

confidence: 99%

Pixel classification using variable string genetic algorithms with chromosome differentiation

Bandyopadhyay

Pal

2001

IEEE Trans. Geosci. Remote Sensing

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Abstract-The concept of chromosome differentiation, commonly witnessed in nature as male and female sexes, is incorporated in genetic algorithms with variable length strings for designing a nonparametric classification methodology. Its significance in partitioning different landcover regions from satellite images, having complex/overlapping class boundaries, is demonstrated. The classifier is able to evolve automatically the appropriate number of hyperplanes efficiently for modeling any kind of class boundaries optimally. Merits of the system over the related ones are established through the use of several quantitative measure.

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Section: Pixel Classification Of Spot Imagementioning

confidence: 99%

Pixel classification using variable string genetic algorithms with chromosome differentiation

Bandyopadhyay

Pal

2001

IEEE Trans. Geosci. Remote Sensing

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“…Rijsbergen (1979) gives such a good indicator of accuracy, F measure in information query. Without the benchmark data, Pal et al (2000) gives Index b as an evaluation standard to the new data to be clustered.…”

Section: Assessmentmentioning

confidence: 99%

An information-fusion method to identify pattern of spatial heterogeneity for improving the accuracy of estimation

Wang

Cao

et al. 2007

Stoch Environ Res Risk Assess

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While spatial autocorrelation is used in spatial sampling survey to improve the precision of the feature's estimate of a certain population at area units, spatial heterogeneity as the stratification frame in survey also often have a considerable effect upon the precision. Under the context of increasingly enriched spatiotemporal data, this paper suggests an information-fusion method to identify pattern of spatial heterogeneity, which can be used as an informative stratification for improving the estimation accuracy. Data mining is major analysis components in our method: multivariate statistics, association analysis, decision tree and rough set are used in data filter, identification of contributing factors, and examination of relationship; classification and clustering are used to identify pattern of spatial heterogeneity using the auxiliary variables relevant to the goal and thus to stratify the samples. These methods are illustrated and examined in the case study of the cultivable land survey in Shandong Province in China. Different from many stratification schemes which just uses the goal variable to stratify which is too simplified, information from multiple sources can be fused to identify pattern of spatial heterogeneity, thus stratifying samples at geographical units as an informative polygon map, and thereby to increase the precision of estimates in sampling survey, as demonstrated in our case research.

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“…The 2D S-type membership function reported in [15] assigns a composite membership value to a pair of adjacent pixels as follows. For a particular threshold b,…”

Section: D S-type Membership Functionmentioning

confidence: 99%

Segmentation of brain MR images using fuzzy sets and modified co-occurrence matrix

Maji¹,

Kundu²,

Chanda³

2006

IET International Conference on Visual Information Engineering (VIE 2006)

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Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of magnetic resonance (MR) images. A robust segmentation technique based on fuzzy set theory for brain MR images is proposed in this paper. The histogram of the given image is thresholded according to the similarity between gray levels. The similarity is assessed through second order fuzzy correlation. To calculate the second order fuzzy correlation, a modified co-occurrence matrix is used to extract the local information more accurately. Two parameters -ambiguity and the strength of ambiguity, are introduced to determine the thresholds of the given histogram. The effectiveness of the proposed algorithm, along with a comparison with other methods, has been demonstrated on a set of brain MR images.

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Segmentation of remotely sensed images with fuzzy thresholding, and quantitative evaluation

Cited by 160 publications

References 40 publications

Pixel classification using variable string genetic algorithms with chromosome differentiation

Pixel classification using variable string genetic algorithms with chromosome differentiation

An information-fusion method to identify pattern of spatial heterogeneity for improving the accuracy of estimation

Segmentation of brain MR images using fuzzy sets and modified co-occurrence matrix

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