Forests play a pivotal role in mitigating climate change as well as contributing to the socio-economic activities of many countries. Therefore, it is of paramount importance to monitor forest cover. Traditional machine learning classifiers for segmenting images lack the ability to extract features such as the spatial relationship between pixels and texture, resulting in subpar segmentation results when used alone. To address this limitation, this study proposed a novel hybrid approach that combines deep neural networks and machine learning algorithms to segment an aerial satellite image into forest and non-forest regions. Aerial satellite forest image features were first extracted by two deep neural network models, namely, VGG16 and ResNet50. The resulting features are subsequently used by five machine learning classifiers including Random Forest (RF), Linear Support Vector Machines (LSVM), k-nearest neighbor (kNN), Linear Discriminant Analysis (LDA), and Gaussian Naive Bayes (GNB) to perform the final segmentation. The aerial satellite forest images were obtained from a deep globe challenge dataset. The performance of the proposed model was evaluated using metrics such as Accuracy, Jaccard score index, and Root Mean Square Error (RMSE). The experimental results revealed that the RF model achieved the best segmentation results with accuracy, Jaccard score, and RMSE of 94%, 0.913 and 0.245, respectively; followed by LSVM with accuracy, Jaccard score and RMSE of 89%, 0.876, 0.332, respectively. The LDA took the third position with accuracy, Jaccard score, and RMSE of 88%, 0.834, and 0.351, respectively, followed by GNB with accuracy, Jaccard score, and RMSE of 88%, 0.837, and 0.353, respectively. The kNN occupied the last position with accuracy, Jaccard score, and RMSE of 83%, 0.790, and 0.408, respectively. The experimental results also revealed that the proposed model has significantly improved the performance of the RF, LSVM, LDA, GNB and kNN models, compared to their performance when used to segment the images alone. Furthermore, the results showed that the proposed model outperformed other models from related studies, thereby, attesting its superior segmentation capability.

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.

Most existing approaches to image classification neglect the concept of semantics, resulting in two major shortcomings. Firstly, categories are treated as independent even when they have a strong semantic overlap. Secondly, the features used to classify images into different categories can be the same. It has been demonstrated that the integration of ontologies and semantic relationships greatly improves image classification accuracy. In this study, a hybrid ontological bagging algorithm and an ensemble technique of convolutional neural network (CNN) models have been developed to improve forest image classification accuracy. The ontological bagging approach learns discriminative weak attributes over multiple learning instances, and the bagging concept is adopted to minimize the error propagation of the classifiers. An ensemble of ResNet50, VGG16, and Xception models is used to generate a set of features for the classifiers trained through an ontology to perform the image classification process. To the authors’ best knowledge, there are no publicly available datasets for forest-type images; hence, the images used in this study were obtained from the internet. Obtained images were put into eight categories, namely: orchards, bare land, grassland, woodland, sea, buildings, shrubs, and logged forest. Each category comprised 100 images for training and 19 images for testing; thus, in total, the dataset contained 800 images for training and 152 images for testing. Our ensemble deep learning approach with an ontology model was successfully used to classify forest images into their respective categories. The classification was based on the semantic relationship between image categories. The experimental results show that our proposed model with ontology outperformed other baseline classifiers without ontology with 96% accuracy and the lowest root-mean-square error (RMSE) of 0.532 compared to 88.8%, 86.2%, 81.6%, 64.5%, and 63.8% accuracy and 1.048, 1.094, 1.530, 1.678, and 2.090 RMSE for support-vector machines, random forest, k-nearest neighbours, Gaussian naive Bayes, and decision trees, respectively.