Remote Sensing Image Classification Using Attribute Filters Defined Over the Tree of Shapes

Recent Developments from Attribute Profiles for Remote Sensing Image Classification

Morphological attribute profiles (APs) are among the most effective methods to model the spatial and contextual information for the analysis of remote sensing images, especially for classification task. Since their first introduction to this field in early 2010’s, many research studies have been contributed not only to exploit and adapt their use to different applications, but also to extend an...

Remote Sensing Image Classification

Remote Sensing Image Classification using Back Propogation

The resolution of remote sensing images increase every day . Most of the existing methods is used the same method for years. The existing method does not provide satisfactory result. The aim is to develop an artificial neural network based on classification method consists of segmentation and classification . Segmentation followed by K-Means method and then classification performed with back pr...

Image Analysis by Means of Attribute Trees — Remote Sensing Applications

In remote sensing, many target objects have highly irregular appearance. Especially natural objects — such as clouds, isotherms and curves of terrain elevation — are typically irregular in both shape and structure. This paper explains how irregular natural objects can be modelled and processed efficiently as attribute trees. First, we present a technique for extracting image topology and shape ...

Remote Sensing Image Classification Using Fuzzy- PSO Hybrid Approach

Pixel classification among overlapping land cover regions in remote sensing imagery is a challenging task. Detection of uncertainty and vagueness are always key features for classifying mixed pixels. This chapter proposes an approach for pixel classification using hybrid approach of Fuzzy C-Means and Particle Swarm Optimization methods. This new unsupervised algorithm is able to identify cluste...