Clustering Algorithm Combined with Hill Climbing for Classification of Remote Sensing Image

Remote Sensing Image Classification

Nearest Clustering Algorithm for Satellite Image Classification in Remote Sensing Applications

Classification of satellite images plays a vital role in remote sensing applications. Numerous algorithms have been developed and tested to classify a satellite image. The main purpose of these algorithms is to lessen the human efforts and errors in minimum time. Classification is performed on satellite images for various purposes. This paper presents a framework to classify a satellite image b...

TEM Color Image Segmentation using Hill Climbing Algorithm

TEM image is rapidly gaining prominence in various fields. Since high resolution TEM image captures huge amount of information, it is important to understand image segmentation techniques on it. The goal of image segmentation is to cluster pixels into salient image regions, i.e., regions corresponding to individual surfaces, objects, or natural parts of objects. To have region based image retri...

A clustering genetic algorithm for software modularisation with a multiple hill climbing approach

Software clustering is a useful technique for software comprehension and re-engineering. In this thesis we examine Software Module Clustering by Hill Climbing (HC) and Genetic Algorithms (GA). Our work primarily addresses graph partitioning using HC and GA. The software modules are represented as directed graphs and clustered using novel HC and GA search techniques. We use a fitness criterion t...

Parallelized Dilate Algorithm for Remote Sensing Image

As an important algorithm, dilate algorithm can give us more connective view of a remote sensing image which has broken lines or objects. However, with the technological progress of satellite sensor, the resolution of remote sensing image has been increasing and its data quantities become very large. This would lead to the decrease of algorithm running speed or cannot obtain a result in limited...