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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 high-ordinary least square projection (HOLP) DF, was proposed in this article by introducing model-based HOLP feature screening (FS), random subspace propagation, and reduced error pruning techniques. To evaluate the performance of the proposed HOLP-DF, total eleven popular FS algorithms and total six advanced deep learning 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 and feature screening; 3) optimum sets of model depth, propaganda ratio and screening ratio parameters are 30, 40&#x0025;, and 40&#x0025;, respectively; 4) performance of HOLP-DF can be further boosted by extra usage of patch-based pooling and morphological profiling techniques.

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