多尺度分割原理与应用 PPT

Introduction
Advantages of hyperspectral remote sensor technology: ➢ The detailed spectral information increases the possibility of more accurately discriminating
robust to the spectral dimension of hyperspectral images. ➢ Iterative statistical classifier based on Markov random field (MRF) modeling. Note that recently
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Introduction
PART TWO
The Hseg Segmentation.
The Hseg Segmentation
The HSeg algorithm is a segmentation technique combining region growing, using the hierarchical stepwise optimization (HSWO) method , which produces spatially connected regions, with unsupervised classification, that groups together similar spatially disjoint regions. The algorithm can be summarized as follows.
多尺度分割原理与应用
CONTENTS
PART ONE
About the Introduction.
PART TWO
The Hseg Segmentation.
PART THREE
Application in ENVI.
PART ONE
About the Introduction.
Abstract
materials of interest. ➢ The fine spatial resolution of the sensors enables the analysis of small spatial structures in the
image. ➢ Many operational imaging systems are currently available providing a large amount of images for
various thematic applications.
But,it also brings some problem: the Curse of Dimensionality and the need for specific spectral– spatial classifiers.
PS: 维数灾难(Curse of Dimensionality):通常是指在涉及到向量的计算的问题中，随着维数的增加，计算量 呈指数倍增长的一种现象。维数灾难在很多学科中都可以碰到，比如动态规划，模式识别和影像识别等。
Introduction
The Curse of Dimensionality of hyperspectral remote sensor technoFra Baidu bibliotekogy :
➢ In high-dimensional spaces, normally distributed data have a tendency to concentrate in the tails, which seems to be contradictory with its bell-shaped density function.
➢ the rate of convergence of the statistical estimation decreases when the dimension grows while conjointly the number of parameters to estimate increases, making the estimation of the model parameters very difficult.
How to build accurate classifiers for hyperspectral images? ➢ SVMs perform a nonlinear pixel-wise classification based on the full spectral information which is
➢ with a limited training set, beyond a certain limit, the classification accuracy actually decreases as the number of features increases.
Introduction
Recent advances in spectral–spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Highlight the importance of spectral–spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.
adaptive MRF have been introduced in remote sensing. ➢ Use advanced morphological filters as an alternative way of performing joint classification.
PS: 鲁棒性(Robust):即系统的健壮性，是在异常和危险情况下系统生存的关键。