The use of the Minnaert correction for land‐cover classification in mountainous terrain

Abstract

Land-cover classifications in mountainous terrain are often hampered by the topographic effect. Several strategies can be pursued to correct for this. A traditional approach is to use training areas for the same land-cover class for different topographic positions and later merge those into one class. Other solutions involve topographic corrections, such as a Minnaert correction. In this study the classification result of the traditional training-area approach was compared with the classification result of a Minnaert-corrected image. In order to derive the Minnaert constants, a SPOT XS scene of the Santa Monica Mountains, USA, was divided into three visually relatively homogeneous regions. Eighty per cent of the pixels were assigned the same land cover in both classifications. Differences in classification were mainly in the section of the image that had more diverse land cover than in the more homogeneous chaparral-covered eastern section. This supports previous findings that the Minnaert constant needs to be derived for individual land-cover classes. The findings also suggest that after the Minnaert correction the resulting classification is comparable to the classification obtained using a more traditional approach.

Topographic Correction of Landsat TM-5 and Landsat OLI-8 Imagery to Improve the Performance of Forest Classification in the Mountainous Terrain of Northeast Thailand

“…Vanonckelen et al [32] note that the impact of topographic correction methods on traditional per pixel image classification has not yet been studied, but summarize several studies that examine the effectiveness of topographic corrections on land cover classifications in mountainous terrain. Several authors have compared the supervised Maximum Likelihood (ML) classifier for the most successful topographic correction methods, and reported a 1%-10% improvement in overall accuracy [12,16,17,31,32,[34][35][36]. Additionally, Tan et al [14] and Vanonckelen et al [17] have applied a Support Vector Machine (SVM) classifier to topographically corrected imagery, achieving satisfactory accuracy.…”

C-correction of optical satellite data over alpine vegetation areas: A comparison of sampling strategies for determining the empirical c-parameter

“…In the literature, one of the few recommendations regarding an empirical parameter's sample can be found in Civco (1989) who stratified a sample by aspect with "large samples of an equal number of pixels (n = 1,390) falling on northern and southern slopes". In general, two categories of sampling methods for calculating semi-empirical parameters can be distinguished in the literature: 1) selection (often subjective) of a relatively small number of observations (generally n < 100) for a target vegetation type over a range of topographic conditions (e.g., Blesius and Weirich 2005;Ekstrand 1996;Smith et al 1980), or 2) random sampling of a varying quantity of observations from either a subset or an entire image. For example, Teillet et al (1982) sampled n = 1,265 and n = 1,038 in two Landsat images to calculate c over five forest types; Colby (1991) sampled all pixels and a subset from a forested Landsat Thematic Mapper (TM) image to calculate global and local k; and, Bishop and Colby (2002) compared k calculated globally, locally and specifically for three broad land-cover types using SPOT 3 images.…”

CIECAM02 and Perception of Colour in 3D Computer Generated Graphics

“…Senčenje Minnaert je nadgradnja Lambertovega senčenja, ki temelji na Minnaertovi funkciji, ki se uporablja predvsem za svetleče materiale in pri astronomskem opazovanju. Uporabno je za porozne in nesijajne materiale (kreda, brušena kovina, lunina površina) in ga imenujemo tudi lunino senčenje [45]. Toon omogoča nerealistično senčenje, vizualizacije so podobne ročno risani animaciji.…”