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.

翻译：识别卫星图像数据中的地形是地理信息科学中的一个关键问题，具有广泛的环境和安全影响。目前已有多种技术可以从卫星获取的光谱数据中推导出分类结果。然而，可靠地分类植被仍然是一项挑战。特别是，在高级卫星图像中，尚无精确的方法用于区分森林与非森林植被。本文提出了一种初始的静态算法流程，通过利用Sentinel-2卫星图像捕获的边缘检测产生的纹理特征以及NDVI比值，来识别卫星图像数据中的森林区域。基于初步的强劲结果，本文还确定了提升分类与验证过程准确性的后续步骤。