Computing geostatistical image texture for remotely sensed data classification

Abstract

Most classical mathematical algorithms for image classi®cation do not usually consider the spectral dependence existing between a pixel and its neighbours, i.e., spatial autocorrelation. Thus, it would be advisable for discrimination of landcover classes to add to the radiometric bands of the sensor complementary information related to the textural features of an image, which can be analysed from the autocorrelation spatial structure of the digital numbers. In this way, the results obtained from pixel-by-pixel classi®ers simultaneously taking into account both radiometric and texture information could be improved. This improvement would arise from the hypothesis that a pixel is not independent of its neighbours and, furthermore, that its dependence can be quanti®ed and incorporated into the classi®er. In this paper we present a methodology based on computing a set of univariate and multivariate textural measures of spatial variability based on several variogram estimators. Madogram and direct variogram for the univariate case, and cross and pseudo-cross variograms for the multivariate one, have been proposed. These measures are calculated for a speci®c lag of distance in a neighbourhood using a moving window on the two most representative principal components of the radiometric bands, enabling us to quantify the spatial variability of radiometric data at a local level. A computer program has been written to create a multiband image texture as output ®le that can be used within the classi®cation process as additional information. An application of this methodology to lithological discrimination is presented using a Landsat-5 TM image. 7

Mapping Spatial Distribution of Larch Plantations from Multi-Seasonal Landsat-8 OLI Imagery and Multi-Scale Textures Using Random Forests

“…Because different lag distances have a limited effect on classification results [17,32], one lag (h = 1) were selected for the three window sizes. More detailed descriptions about variogram functions and definitions of parameters can be found in previous studies [36][37][38]. All the prediction variables derived from geostatistical texture were showed in Figure 2.…”

“…The geostatistics approach is a textural analysis tool used to measure spatial variation (e.g., spatial autocorrelation) in remotely sensed data [36]. Implementation of variograms is one of the most promising techniques of geostatistics and it has been widely used in image texture characterization [37].…”

“…The direction of variogram computation and the lag distance h are the two important parameters affecting texture values. In this study, the four main directions (N-S, E-W, NE-SW and NW-SE) were averaged to produce an omnidirectional variogram texture [36,37]. Because different lag distances have a limited effect on classification results [17,32], one lag (h = 1) were selected for the three window sizes.…”

A multiscale texture analysis procedure for improved forest stand classification

“…One of the most promising techniques is the implementation of variograms to remotely sensed data as a means of classifying image texture (Curran 1988, Jupp et al 1988, Woodcock et al 1988a, b, Lark 1996, Chica-Olmo and Abarca-Hernandez 2000. While several studies have shown that semivariance is useful for quantifying texture, few studies have assessed semivariance as a tool for mapping image texture (Schachter et al 1978, Miranda et al 1992.…”

Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data

“…Different surface objects have different surface morphology, which yields different textural features. Textural features have been widely applied in several studies, such as sea ice mapping, crop species composition classification, vegetation structure analysis, lithological discrimination, and land-cover mapping [20,[30][31][32][33][34][35][36]. However, these studies focused more on the improvement or design of texture-derived methods, and texture features were calculated from a single remote sensing image.…”