26 March 1993 Multitemporal texture analysis of features computed from remotely sensed imagery
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Abstract
A concept is presented for analyzing the texture of changes in multi-temporal imagery. In more traditional change detection approaches, spectral signatures or textures from two or more spatially-coincident image sets are compared. Spatial cooccurrence has been used by various researchers to compute texture measures. These measures, representing the two dimensional x/y spatial variability in an image, are compared against two dimensional textures in other images. This paper introduces the concept of computing image texture using spatial cooccurrence matrices by searching, not just in the x/y space, but in the third dimension of time, or t space. An example problem is described in which changes in forest canopies are evaluated. A spectral mixture model for computing forest canopy closure from Landsat TM data is described. The canopy closure feature images from two spatially coincident, but time varying image sets are evaluated using three dimensional texture analysis. The technique lends itself to evaluation of systematic or localized forest changes; e.g. uniform thinning vs. localized damage.
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Mark B. Lazaroff, Mark B. Lazaroff, Mark W. Brennan, Mark W. Brennan, } "Multitemporal texture analysis of features computed from remotely sensed imagery", Proc. SPIE 1819, Digital Image Processing and Visual Communications Technologies in the Earth and Atmospheric Sciences II, (26 March 1993); doi: 10.1117/12.142197; https://doi.org/10.1117/12.142197
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