scispace - formally typeset
Open AccessBook

Morphological Image Analysis: Principles and Applications

Pierre Soille
Reads0
Chats0
TLDR
This self-contained volume will be valuable to all engineers, scientists, and practitioners interested in the analysis and processing of digital images.
Abstract
From the Publisher: The purpose of this book is to provide readers with an in-depth presentation of the principles and applications of morphological image analysis. This is achieved through a step by step process starting from the basic morphological operators and extending to the most recent advances which have proven their practical usefulness. This self-contained volume will be valuable to all engineers, scientists, and practitioners interested in the analysis and processing of digital images.

read more

Citations
More filters
Journal ArticleDOI

Object-based cloud and cloud shadow detection in Landsat imagery

TL;DR: The goal is development of a cloud and cloud shadow detection algorithm suitable for routine usage with Landsat images and as high as 96.4%.
Journal ArticleDOI

Hyperspectral Remote Sensing Data Analysis and Future Challenges

TL;DR: A tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing.
Journal ArticleDOI

Classification of hyperspectral data from urban areas based on extended morphological profiles

TL;DR: A method based on mathematical morphology for preprocessing of the hyperspectral data is proposed, using opening and closing morphological transforms to isolate bright and dark structures in images, where bright/dark means brighter/darker than the surrounding features in the images.
Journal ArticleDOI

Advances in Spectral-Spatial Classification of Hyperspectral Images

TL;DR: Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper and several techniques are investigated for combining both spatial and spectral information.