1BAbstract This study implemented and applied a binary ensemble classifier for identification of grazed vegetation communities on Macquarie Island from very high resolution Quickbird imagery. Rabbit grazing has severely affected Macquarie’s unique sub-Antarctic vegetation communities. The aim of this study was to identify the grazed areas from Quickbird imagery to map their spatial extent. Seve...

The successful launch of the Chinese high spatial resolution hyperspectral satellite TianGong-1 (TG-1) opens up new possibilities for applications of remotely-sensed satellite imagery. One of the main goals of the TG-1 mission is to provide observations of surface attributes at local and landscape spatial scales to map urban land cover accurately using the hyperspectral technique. This study at...

Bernoulli HMMs (BHMMs) have been successfully applied to handwritten text recognition (HTR) tasks such as continuous and isolated handwritten words. BHMMs belong to the generative model family and, hence, are usually trained by (joint) maximum likelihood estimation (MLE) by means of the Baum-Welch algorithm. Despite the good properties of the MLE criterion, there are better training criteria su...

Classifying remote sensing imageries to obtain reliable and accurate LandUse/Cover (LUC) information still remains a challenge that depends on many factors suchas complexity of landscape especially in arid region. The aim of this paper is to extractreliable LUC information from Land sat imageries of the Pishkouh watershed of centralarid region, Iran. The classical Maximum Likelihood Classifier ...

This paper presents a novel framework, based on maximum likelihood, for training models to recognise simple spatial-motion events, such as those described by the verbs pick up, put down, push, pull, drop, and throw, and classifying novel observations into previously trained classes. The model that we employ does not presuppose prior recognition or tracking of 3D object pose, shape, or identity....

The problem of estimating the parameters for continuous-time partially observed systems is discussed. New exact lters for obtaining Maximum Likelihood (ML) parameter estimates via the Expectation Maximization algorithm are derived. The methodology exploits relations between incomplete and complete data likelihood and gradient of likelihood functions, which are derived using Girsanov's measure t...

In image matching applications such as tracking and stereo matching, it is common to use the sum-of-squareddi erences (SSD) measure to determine the best match for an image template. However, this measure is sensitive to outliers and is not robust to template variations. We describe a robust measure and eÆcient search strategy for template matching with a binary or greyscale template using a ma...

One popular class of unsupervised algorithms are competitive algorithms. In the traditional view of competition, only one competitor, the winner, adapts for any given case. I propose to view competitive adaptation as attempting to fit a blend of simple probability generators (such as gaussians) to a set of data-points. The maximum likelihood fit of a model of this type suggests a "softer" form ...

This module introduces the maximum likelihood estimator. We show how the MLE implements the likelihood principle. Methods for computing th MLE are covered. Properties of the MLE are discussed including asymptotic e ciency and invariance under reparameterization. The maximum likelihood estimator (MLE) is an alternative to the minimum variance unbiased estimator (MVUE). For many estimation proble...