Land cover (LC) classification remains a challenging task due to the diversity of terrain and
topography, limited prior knowledge, and complex data sets. These variations can lead to …

Accurate mapping of selective logging (SL) serves as the foundation for additional research
on forest restoration and regeneration, species diversification and distribution, and …

Accuracy of land cover classification in remotely sensed images relies on the features
extracted and the classifier used. Texture features are significant in land cover classification …

Abstract Forests constitute 31%(about 4 billion ha) of the land area of the earth, and tropical
forests cover is about 2 billion ha. Tropical forests play a significant role in supporting the …

In the remote sensing domain, texture-based segmentation is by convention referred to as
texture-based classification and so in our case, it is referred to as texture based land cover …

This chapter explores texture, texture model, and properties of texture in image processing. It
exposes standard texture dataset to the reader which is vital for the validation of any new …

This chapter describes a wide range of basic statistical texture models like local binary
pattern (LBP), ternary pattern (TP), gray level co-occurrence matrix (GLCM), texture …