Contextual land-cover classification: incorporating spatial dependence in land-cover classification models using random forests and the Getis statistic

Abstract

Land-cover characterization of large heterogeneous landscapes is challenging because of the confusion caused by high intra-class variability and heterogeneous landscape artefacts. Neighbourhood context can be used to supplement spectral information, and a novel way of incorporating spatial dependence in a heterogeneous region is tested here using an ensemble learning technique called random forests and a measure of local spatial dependence called the Getis statistic. The overall Kappa accuracy of the random forest classifier that used a combination of spectral and local spatial (Getis) variables at three different neighbourhood sizes (3 Â 3, 7 Â 7, and 11 Â 11) ranged from 0.85 to 0.92. This accuracy was higher than that of a non-spatial random forest classifier having an overall Kappa accuracy of 0.78, which was run using the spectral variables only. This study demonstrated that the use of the Getis statistic with different neighbourhood sizes leads to substantial increase in per class classification accuracy of heterogeneous land-cover categories.

Machine Learning Comparison between WorldView-2 and QuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification

“…Four different classifiers were applied on each data set, namely: (1) Decision Tree by the C4.5 method [4,30]; (2) Random Forest [25,26]; (3) Support Vector Machines [31]; and (4) Regression Tree Classifiers [32,33]. Decision Trees create easy to interpret classification models by hierarchically splitting the data set.…”

“…The feature selection algorithms used in our analysis were: (1) InfoGain [22], which computes the information gain (based on the entropy measure) to evaluate the worth of a feature with respect to the classes; (2) Relief-F [23], which evaluates the value of a feature by repeatedly sampling instances and considering the value of the given feature for the nearest instance(s) of the same and different class(es); (3) Fast Correlation-Based Filter (FCBF) [24], which is based on a correlation measure and relevance/redundancy analysis (this algorithm should be used in conjunction with an attribute set evaluator, in this case, the Symmetrical Uncertainty [24] measure was used); and (4) the Random Forest algorithm [25,26], which is a classification algorithm that also provides a ranking of variable relevance by comparing classification accuracies obtained with, and then without, each of the features. When using the Relief-F (RF) algorithm, the ten nearest neighbors were considered, weighted by their distances to the randomly selected samples in order to calculate class distances.…”

Mapping Mountain Pine Beetle Mortality through Growth Trend Analysis of Time-Series Landsat Data

“…MLC relies on assumptions about the data distribution (e.g., normally distributed), whereas the ensemble learning techniques used by RF do not [53]. Meanwhile, by grouping many individual classifiers, RF combined the strengths and minimized the weaknesses of each [54]. (2) MLC is less affected by the selection of input layers than RF.…”

Mapping Robinia Pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial and Textureal Information Extracted From Ikonos Imagery

“…Mean decrease in accuracy (%) Predictive variables Ghimire et al (2010) found that making use of the Gi statistic with different distance values led to substantial increase in per class classification accuracy of heterogeneous land-cover categories. The Kappa values of the RF classifications that used a combination of spectral and Gi variables at three different distance values (1, 3, and 5 pixels) ranged from 0.85 to 0.92 (vs. 0.78 using only spectral bands).…”

“…For example, in stressed/diseased forest stands, the understory plants (e.g., regeneration forest, shrubs, and grasses) presenting in gaps and open areas may have a similar NIR response to a closed forest canopy. Such a classification challenge may be overcome by using grey-level co-occurrence matrix (GLCM) (Franklin et al, 2001) or Getis statistic (Gi) (Wulder and Boots, 2001;Myint et al, 2007;Ghimire et al, 2010).…”