Hyperspectral Image Classification Using Semi-supervised Random Forest

In this paper, a hyperspectral image classification technique is proposed using semi-supervised random forest (SSRF). Robust node splitting in the random forest requires enormous training data, which is scarce in remote sensing applications. In order to overcome this drawback, we propose utilizing unlabeled data in conjunction with labeled data to assist the splitting process. Moreover, in order to tackle the curse of dimensionality associated with a hyperspectral image, we explore nonnegative matrix factorization (NMF) to remove redundant information. Experimental results confirm the efficacy of the proposed method.