A critical survey of GEOBIA methods for forest image detection and classification

Modern earth observation sensors have revolutionized the remote sensing community by improving remote sensing image quality. However, Pixel-based image analysis methods have challenges in handling very high-resolution (VHR) imagery. Geographic Based Image Analysis (GEOBIA) yielded promising results, but it is not inflexible in capturing domain experts' expressions, therefore geographic information system professionals shifted to ontologies for remote sensing science. This paper advocates for the adoption of knowledge representation using ontologies in remote sensing. To this end, a survey of GEOBIA studies for image analysis and classification is presented, and the limitations of existing methods in reaching the remote sensing expert-level expectation are clarified. New GEOBIA development techniques as well as opportunities for improving GEOBIA models have been looked into. Recent studies that adopted ontologies in forest image classification are analyzed and recommendations for the remote sensing science community are provided, to highlight the advantages of ontologies in interpreting satellite images.

Automated summary

Modern earth observation sensors have improved remote sensing image quality, but pixel-based image analysis methods face challenges with very high-resolution imagery. Geographic Based Image Analysis (GEOBIA) has shown promise, but lacks flexibility in capturing expert expressions, leading to the adoption of ontologies in remote sensing science. This paper advocates for using ontologies for knowledge representation in remote sensing, presenting a survey of GEOBIA studies and analyzing recent ontologies-based forest image classification studies.