LAUSR

Tridimensional empirical mode decomposition (TEMD) has been successfully employed as a powerful tool for hyperspectral classification. It achieves good performance to analyze nonlinear/nonstationary data and capture abstract features of hyperspectral images (HSIs). However, the major challenge is how to adequately exploit and balance decomposed edge-wise (EW) and trend-wise (TW) features. To address this issue, we propose an accelerated adaptive feature balance technique (A2FBT) based on TEMD. Due to complex discriminant information, A2FBT initially decomposes the HSI into varying oscillations adequately, including several tridimensional intrinsic mode functions (TIMFs) and one residual (RES). They contain high-frequency EW and low-frequency TW features, respectively. To further accelerate the decomposition, a $k$ -D tree-based search strategy is proposed for filter size selection. To bridge the gaps between the RES and all TIMFs, a novel adaptive feature balance strategy is presented by assigning adaptive tradeoffs. We aim to balance multiple features adequately according to the intrinsic discriminant information. A combined metric-based strategy is provided for tradeoff calculation, measuring the mutual similarities between oscillations comprehensively. A2FBT pays more attention to these similarities, reinforcing the respective contributions of EW and TW features. Experiments on four typical hyperspectral datasets illustrate the effectiveness of A2FBT compared with several state-of-the-art techniques.