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The advent of modern remote sensors alongside the development of advanced parallel computing has significantly transformed both the theoretical and real implementation aspects of remote sensing. Several algorithms for detecting objects of interest in remote sensing images and subsequent classification have been devised, and these include template matching based methods, machine learning and knowledge-based methods. Knowledge-driven approaches have received much attention from the remote sensing fraternity. They do, however, face challenges in terms of sensory gap, duality of expression, vagueness and ambiguity, geographic concepts expressed in multiple modes, and semantic gap. This paper aims to review and provide an up-to-date survey on machine learning and knowledge driven approaches towards remote sensing forest image analysis. It is envisaged that this work will assist researchers in coming up with efficient models that accurately detect and classify forest images. There is a mismatch between what domain experts expect from remote sensing data and what remote sensing science produces. Such a mismatch or disparity can be reduced or alleviated by adopting an ontology paradigm methodology. Ontologies should be used to support the future of remote sensing in forest object classification. The paper is presented in five parts: (1) a review of methods used for forest image detection and classification; (2) challenges faced by object detection methods; (3) analysis of segmentation techniques employed; (4) feature extraction and classification; and (5) performance of the state-of-the-art methods employed in forest image detection and classification.

Machine Learning Methods for Forest Image Analysis and Classification: A Survey of the State of the Art