Hierarchical Scale-space Representational Measure for Estimating Land Cover


Shape-filling curves such as planar lines and rectilinear segments are the Minimum Mapping Unit (MMU) for an object-oriented image analysis operation. When the scale is changed, the space-filling curves do not change the feature object representation, thus representing spatial and aspatial features with finer or coarser granularity. The increased collinearity can be explained by the arrangement of topological objects (neighbors/objects) in the aggregated feature space, which results in image areal objects. Instead of performing a single operation on the imagery objects by scanline rows, we can compute on custom built algorithms applied to distinguishing objects. This operation produces super. relationships, taking advantage of multi-scale object-oriented analysis procedures A continuous hierarchical scale space filtering operation is adapted for segmentation purposes for information retrieval. In fact, MMU variations will generate instances of image objects that retain the spatial scale at a given optimizing parameter. This article focuses on object-oriented analysis and fuzzy inference analysis of the imagery scene. By denoting image analysis procedures based on image objects at the characteristic scale, imagery semantics at the low and how-level spatial context can be delineated. With object oriented scale space hierarchical theory and varying intra and inter scale parameters, such a method becomes feasible. Fuzzy modeling of mixed pixels is used to extract reliability without incorporating edges while using image objects to calculate multi-variate statistics (Entropy measure, heterogeneity measure, local mean vs. local variance measure, and mean vs. covariance measure). The Region Labeling Operator will use in-class variance measures to resolve homogeneous areas of mixed pixels. In between classes Variances can be used to calculate the distance between scale intervals that the scale object can resolve. This results in a hierarchical network that delineates the final object’s features further. In the Region Growing and Region Merging procedures, the Scale Operator (SO) is defined as the varying optimizer selection. Individual objects with similar sub-class variance and texture characteristics will be fused to create a segmented super object during the region abstraction process. The Scale Operator diffuses the super-objects as a result of the increased heterogeneity, and thus more objects are merged and created within the class intervals.

Author (S) Details

C. Rajabhushanam
Computer Science Engineering, BIST Bharath Institute of Higher Education and Research, Chennai, India.

View Book :- https://stm.bookpi.org/CTMCS-V2/article/view/1783

Leave A Comment