Soil image segmentation and texture analysis: A computer vision approach

Sofou, A; Evangelopoulos, G; Maragos, P

dc.contributor.author	Sofou, A	en
dc.contributor.author	Evangelopoulos, G	en
dc.contributor.author	Maragos, P	en
dc.date.accessioned	2014-03-01T01:23:05Z
dc.date.available	2014-03-01T01:23:05Z
dc.date.issued	2005	en
dc.identifier.issn	1545-598X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16805
dc.subject	Computer vision	en
dc.subject	Image segmentation	en
dc.subject	Remote sensing	en
dc.subject	Soil analysis	en
dc.subject	Texture analysis	en
dc.subject.classification	Geochemistry & Geophysics	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.classification	Remote Sensing	en
dc.subject.other	Feature measurements	en
dc.subject.other	Soil analysis	en
dc.subject.other	Soil morphology	en
dc.subject.other	Texture analysis	en
dc.subject.other	Computation theory	en
dc.subject.other	Computer vision	en
dc.subject.other	Ecology	en
dc.subject.other	Image processing	en
dc.subject.other	Image segmentation	en
dc.subject.other	Information analysis	en
dc.subject.other	Morphology	en
dc.subject.other	Soil mechanics	en
dc.title	Soil image segmentation and texture analysis: A computer vision approach	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/LGRS.2005.851752	en
heal.identifier.secondary	http://dx.doi.org/10.1109/LGRS.2005.851752	en
heal.language	English	en
heal.publicationDate	2005	en
heal.abstract	Automated processing of digitized soilsection images reveals elements of soil structure and draws primary estimates of bioecological importance, like ground fertility and changes in terrestrial ecosystems. We examine a sophisticated integration of some modern methods from computer vision for image feature extraction, texture analysis, and segmentation into homogeneous regions, relevant to soil micromorphology. First, we propose the use of a morphological partial differential equation-based segmentation scheme based on seeded region-growing and level curve evolution with speed depending on image contrast. Second, we analyze surface texture information by modeling image variations as local modulation components and using multifrequency filtering and instantaneous nonlinear energy-tracking operators to estimate spatial modulation energy. By separately exploiting contrast and texture information, through multiscale image smoothing, we propose a joint image segmentation method for further interpretation of soil images and feature measurements. Our experimental results in images digitized under different specifications and scales demonstrate the efficacy of our proposed computational methods for soil structure analysis. We also briefly demonstrate their applicability to remote sensing images. © 2005 IEEE.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Geoscience and Remote Sensing Letters	en
dc.identifier.doi	10.1109/LGRS.2005.851752	en
dc.identifier.isi	ISI:000232897100005	en
dc.identifier.volume	2	en
dc.identifier.issue	4	en
dc.identifier.spage	394	en
dc.identifier.epage	398	en