Object Ased Image Analysis And Pixel Based Image Analysis

This is the first book to explain and demonstrate the tremendous ability of Pulse-Coupled Neural Networks (PCNNs) when applied to the field of image processing. PCNNs and their derivatives are biologically inspired models that are powerful tools for extracting texture, segments, and edges from images. As these attributes form the foundations of most image processing tasks, the use of PCNNs facilitates traditional tasks such as recognition, foveation, and image fusion. PCNN technology has also paved the way for new image processing techniques such as object isolation, spiral image fusion, image signatures, and content-based image searches. This volume contains examples of several image processing applications, as well as a review of hardware implementations.

The availability of new high resolution satellite data brings with it the need for new image analysis methods. Traditional pixel-oriented algorithms do not give credit to the spatial coherence of high resolution imagery. In particular, for detection and classification of man-made structures, object-based procedures are much more appropriate. The use of object features such as scale, compactness, orientation and texture, in addition to spectral characteristics, extends the possibilities of remote sensing satellite image analysis considerably.

Pixel- and Object-based High-Resolution satellite Image classification in ENVI software (FAST TRACK)

This book covers the latest advancements in the areas of machine learning, computer vision, pattern recognition, computational learning theory, big data analytics, network intelligence, signal processing, and their applications in real world. The topics covered in machine learning involve feature extraction, variants of support vector machine (SVM), extreme learning machine (ELM), artificial neural network (ANN), and other areas in machine learning.