Using Texture to Classify Forests Separately from Vegetation

TL;DR

This paper proposes a novel texture-based algorithm to distinguish forest regions from other vegetation in satellite images, addressing a key challenge in land cover classification.

Contribution

It introduces a new static, algorithmic method utilizing texture features from edges and NDVI ratios for forest classification in high-resolution satellite imagery.

Findings

Initial results show promising accuracy in forest detection

Texture features effectively differentiate forest from other vegetation

Next steps include refining the algorithm for improved precision

Abstract

Identifying terrain within satellite image data is a key issue in geographical information sciences, with numerous environmental and safety implications. Many techniques exist to derive classifications from spectral data captured by satellites. However, the ability to reliably classify vegetation remains a challenge. In particular, no precise methods exist for classifying forest vs. non-forest vegetation in high-level satellite images. This paper provides an initial proposal for a static, algorithmic process to identify forest regions in satellite image data through texture features created from detected edges and the NDVI ratio captured by Sentinel-2 satellite images. With strong initial results, this paper also identifies the next steps to improve the accuracy of the classification and verification processes.