HOLP-DF: HOLP Based Screening Ultrahigh Dimensional Subfeatures in Deep Forest for Remote Sensing Image Classification

Abstract

To overcome the high intramodel dimensionality and low ensemble diversity issues, which limit the classification performance of original deep forest (DF), a new version of DF, the HOLP-DF, was proposed in this article by introducing model-based high-ordinary least square projection (HOLP) feature screening (FS), random subspace propagation, and reduced error pruning (REP) techniques. To evaluate the performance of the proposed HOLP-DF, total eleven popular FS algorithms and total six advanced deep learning (DL) methods are selected. Experimental results on three widely acknowledged hyperspectral and PolSAR image classification benchmarks showed that: 1) HOLP is an optimal choice for FS in contrast with other screeners in terms of high classification accuracy and execution efficiency; 2) HOLP-DF is capable of obtaining better results than the original DF, DF with confidence screening (DF-CS) and feature screening (DF-FS); 2) optimum sets of model depth, propaganda ratio and screening ratio parameters are 30, 40% and 40%, respectively; 3) performance of HOLP-DF can be further boosted by extra usage of patch-based pooling(PP) and morphological profiling(MP) techniques.