Iterative satellite image segmentation by fuzzy hit-or-miss and homogeneity index

A fuzzy morphological hit-or-miss transform for grey-level images: A new approach

“…No properties were analyzed but it was indicated how to choose the structuring elements. Later in 2006, Intajat et al in the work [25] make use of a homogeneity index to measure the former by dividing an input image with a segmentation algorithm based on the SFHMT operator in order to segment satellite images. Here the selected λ-function was λ n (x) = 1 1+x 2 − x 2 with n ≥ 1.584.…”

Fuzzy Image Processing and Recognition

“…For the same purpose, Krishnapuram et al (65) proposed another algorithm that is claimed to be less time consuming than that of Dave. Soft decision making has been used to develop many other segmentation algorithms for various applications such as document image processing, ultrasound image processing, satellite image analysis, MR image analysis, and remote sensing (70)(71)(72)(73)(74)(75)(76)(77)(78). Algorithms for applications such as classification of MR brain images (79) and microcalcification detection (80) have been succesfully implemented.using fuzzy techniques.…”

Automatic cloud segmentation from INSAT‐3D satellite image via IKM and IFCM clustering

“…The qualitative and quantitative segmentation accuracies of the algorithm are improved as compared with existing algorithms. Intajag et al [10] partitioned the satellite images into the homogeneous regions by using an automatic iterative segmentation algorithm. A fuzzy hit‐or‐miss operator is used for segmentation purpose, and an index is measured for homogeneity measurement.…”

“…The algorithms in [3, 6] segment the cloudy regions into different types. However, the algorithms used in [3, 6, 9, 10] divide the cloudy satellite images by taking the initial centroids randomly. As the centroids are not automatic, different choice of centroids will result in different segmentation output.…”