Vegetation Identification in Hyperspectral Images Using Distance/Correlation Metrics

Distance/correlation metrics have emerged as a robust and simplified tool for assessing the spectral characteristics of hyperspectral image pixels effectively categorizing vegetation within specific study area. Correlation methods provide readily deployable computationally efficient approach, rendering them particularly advantageous applications in developing nations or regions with limited resources. This article presents comparative investigation correlation/distance identification imagery. The facilitates comprehensive evaluation five distance and/or correlation metrics, namely, direct correlation, cosine similarity, normalized Euclidean distance, Bray–Curtis Pearson correlation. Direct two that demonstrated highest accuracy pixel identification. Using selected methodologies, detection algorithm was implemented validated using Manga neighborhood Cartagena de Indias, Colombia. library facilitated processing, while mathematical calculation correlations performed numpy scipy libraries Python programming language. Both approach adopted this aim to serve point reference conducting studies on diverse material types imagery open-access platforms.