Proceedings of the Symposium on Global and Environmental Monitoring: Proceedings of the Symposium on Global and Environmental Monitoring

Bibliographic data

Multivolume work

Persistent identifier:: 856665355

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts ; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856665355

Language:: English

Document type:: Multivolume work

Volume

Persistent identifier:: 856669164

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Scope:: XIV, 912 Seiten

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856669164

Illustration:: Illustrationen, Diagramme, Karten

Signature of the source:: ZS 312(28,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [WP-1 ADVANCED COMPUTING FOR INTERPRETATION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: COMPARISON OF SOME TEXTURE CLASSIFIERS. Einari Kilpela and Jan Heikkila

Document type:: Multivolume work

Structure type:: Chapter

COMPARISON OF SOME TEXTURE CLASSIFIERS Einari Kilpela and Jan Heikkila Helsinki University of Technology Institute of Photogrammetry and Remote Sensing 02150 Espoo 15, Finland ABSTRACT A performance analysis between different textural feature descriptors in land-use classi fication is presented. Both satellite and aerial images are used. The texture descriptors used are first order statistics, second order (cooccurrence) statistics, Fourier spectrum, amplitude varying rate statistics and fractal descriptors. The technical implementation of each of these descriptors in the context of classifica tion is also addressed. Each data set is classified using the spectral features, each of the texture descriptors and some variations of them, and using a combination of spect ral and textural features. The classifiers used are the maximum likelihood classifier assuming multinormal density functions, the k-NN classifier and the average learning subspace (ALSM) classifier. The performance analysis, which is based on independent test sites, shows that the ALSM- classifier and the k-NN classifier work equally well, but the crude assumption of normal densities in the context of maximum likelihood classifier produces biased results. No clear distinction between the behavior of the different texture descriptors was found. The full usage of the cooccurrence statistics works well. However, its computational load is quite heavy. The more simple texture descriptors, like the simple fractal di mension in combination with spectral features, works often equally well in the context of satellite images. In case of larger scale images, the more complex texture descrip tors are called for. 1. INTRODUCTION Texture is an important cue for understand ing and discriminating in natural images. However, it is surprisingly seldom utilized in the context of terrain classification. In many applications (e.g. land-use clas sification) texture features could bring more discriminatory information. This is especially true when larger scale imagery is used. When computing textural feature vectors for each pixel according to some local neighborhood, a little bit of the rude assumption of spatial independence can be broken down. This does not mean that one should give up from the attempts to more properly model the sampling pro cess, e.g. with the Markov random field models (see /GemGem84/) and further devel op their computational characteristics especially for multi-dimensional spaces. This should be the final goal. In this paper we are anyhow concerned with more conservative and practical approaches. The problem of texture analysis and mo delling is a widely discussed problem in the areas of Pattern Recognition, Image Analysis, Computer Vision and even in Com puter Graphics. Texture is a commonly used criteria in the early processing of visual information. Paradoxically however, because of its loose definition, a huge amount of methods, both ad hoc and formal, have been developed (for surveys see /Ha- rali79/, /GoDeOo85/ and /Harali86/). The methods fall into two main categories, namely statistical and structural. The naming convention is slightly misleading, because usually quite a lot of statistics is involved in the structural approaches, too. Images taken over natural terrain contain both spatially and spectrally quite irregularly distributed, usually microscop ic, texture elements. The smaller the ima ging scale, the less structure it has. In many circumstances, just a simple measure of the roughness of the texture can bring enough discriminatory power to the feature space. Anyhow, the larger the scale, the more structure is visible in the texture. Excluding manmade objects, the spatial distribution of the (maybe invisible) tex tural structure elements is usually quite irregular also in large scale (aerial) images. Due to these facts, statistical methods are preferred when analyzing textu res in natural images. So is the case also in the underlying project. Because of the variability of the texture measures, a practitioner faces the problem of choosing the most suitable descriptor for his application. Reviewing the litera ture does not help much, because no tho rough comparison exists. There are so many factors which influence the performance of a texture classifier (the data, the texture descriptor, the number of features, the type of classifier, the number of training samples, resolution level, prepro cessing steps etc. ) that a complete compa rison would be a huge task. There are some texture measures, which have been quite successful in single comparative studies and which have become quite popular. One of the most popular texture descriptors is the second order statistics (Cooccur rence Statistics), originally suggested by Haralick, Shanmugam and Dinstein in 1973 (/HaShDi73/). Another, widely used descriptor is the Fourier power spectrum. These two methods are compared in many

Access restriction

Copyright

fullscreen: Proceedings of the Symposium on Global and Environmental Monitoring (Part 1)

Multivolume work

Volume

Chapter

Chapter

Table of contents

Cite and reuse

Volume

Chapter

Image

Image fragment

Citation links

Citation recommendation