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.