Journal ArticleDOI
Image segmentation and discriminant analysis for the identification of land cover units in ecology
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The segmentation algorithm, iterative mutually optimum region merging (IMORM), is presented and used to partition images into elements that are thereafter classified by linear canonical discriminant analysis and a maximum likelihood allocation rule.Abstract:
The textured nature of most natural land cover units as represented in remotely sensed imagery causes limited results of per-pixel classifications The segmentation algorithm, iterative mutually optimum region merging (IMORM), is presented and used to partition images into elements that are thereafter classified by linear canonical discriminant analysis and a maximum likelihood allocation rule This per-segment approach results in much higher accuracy than the conventional per-pixel approach Furthermore, separability matrices indicate that many land cover categories cannot be correctly defined by per-pixel statisticsread more
Citations
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Book
Remote sensing, models, and methods for image processing
TL;DR: The Nature of Remote Sensing: Introduction, Sensor Characteristics and Spectral Stastistics, and Spatial Transforms: Introduction.
Journal ArticleDOI
Estimating impervious surface distribution by spectral mixture analysis
Changshan Wu,Alan Murray +1 more
TL;DR: In this article, the authors estimate the distribution of impervious surface, a major component of the vegetation-impervious surface-soil (V-I-S) model, through a fully constrained linear spectral mixture model using Landsat Enhanced Thematic Mapper Plus (ETM+) data within the metropolitan area of Columbus, OH.
Journal ArticleDOI
Classification of Hyperspectral Images With Regularized Linear Discriminant Analysis
TL;DR: An efficient version of the RLDA recently presented by Ye to cope with critical ill-posed hyperspectral image classification problems is introduced in the remote sensing community and several LDA-based classifiers are compared theoretically and experimentally with the standard LDA and theRLDA.
Journal ArticleDOI
Dual-season mapping of wetland inundation and vegetation for the central Amazon basin
TL;DR: In this article, the authors used L-band synthetic aperture radar (SAR) imagery acquired by the Japanese Earth Resources Satellite-1 to map the central Amazon region and produce the first high-resolution wetlands map for the region.
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Forest change detection by statistical object-based method
TL;DR: High detection accuracy and overall Kappa were achieved by OB-Reflectance method in temperate forests using three SPOT-HRV images covering a 10-year period.
References
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Journal ArticleDOI
Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
OtherDOI
A land use and land cover classification system for use with remote sensor data
TL;DR: The framework of a national land use and land cover classification system is presented for use with remote sensor data and uses the features of existing widely used classification systems that are amenable to data derived from re-mote sensing sources.
Journal ArticleDOI
Spatial pattern and ecological analysis
TL;DR: In this article, the spatial heterogeneity of populations and communities plays a central role in many ecological theories, such as succession, adaptation, maintenance of species diversity, community stability, competition, predator-prey interactions, parasitism, epidemics and other natural catastrophes, ergoclines, and so on.
Journal ArticleDOI
Image Segmentation Techniques
TL;DR: There are several image segmentation techniques, some considered general purpose and some designed for specific classes of images as discussed by the authors, some of which can be classified as: measurement space guided spatial clustering, single linkage region growing schemes, hybrid link growing scheme, centroid region growing scheme and split-and-merge scheme.
A comparative study of texture measures for terrain classification.
J. S. Weszka,A. Rosenfeld +1 more
TL;DR: Three standard approaches to automatic texture classification make use of features based on the Fourier power spectrum, on second-order gray level statistics, and on first-order statistics of gray level differences, respectively; it was found that the Fouriers generally performed more poorly, while the other feature sets all performned comparably.