Effect of hyperspectral image denoising with PCA and total variation on tree species mapping using Apex Data

In this paper, the impact of image denoising on feature selection and tree species mapping accuracy is assessed. We apply a novel algorithm for hyperspectral (HS) image denoising using principal component analysis (PCA) and total variation (TV). The method is embedded in an object‐based classification framework and tested for complex forests with closed canopies and scarce reference data. Results show that, under the given conditions, HS image denoising is beneficial yielding stable mapping results with acceptable accuracy levels. Denoising also affected feature selection processing time with a time gain of 41.6%