## 2 Materials and Methods
### Conceptual Framework  
While VHSR sensors enable the detection of a single seal (or, under some circumstances, a penguin) hauled out on pack ice, it remains very difficult to intelligently transform very high resolution image pixels to predefined semantic labels. Automated processes for interpreting VHSR imagery are increasingly critical because recent policy easements allow the release of 0.31 m resolution satellite images (e.g., WorldView 3) for civilian use, and the number of commercial satellites supplying EO imagery continues to grow. While there are some developments in fully-automated VHSR image interpretation, the intriguing question of \"how to meaningfully link RS quantifications and user defined class labels\" remains largely unanswered and voices the argument of considering this issue as a technological challenge rather a broader knowledge management problem [59] in VHRS image processing. To address the \"big data\" challenge in EO of Antarctic wildlife, we propose a conceptual outline (Figure 2), which resonates with the framework of [59], for automated segmentation and classification of guano in VHSR imagery. In this framework, scene complexity is reduced through multi-scale segmentation, which incorporates multisource RS/GIS/thematic data and creates a hierarchical object structure based on the premise that real world features are found in an image at different scales of analysis [36]. Image object candidates extracted at different scales are adaptively streamed into semantic categories through knowledge-based classification (KBC), which interfaces low-level image object features and expert knowledge by means of fuzzy membership functions. The classified image objects can then be integrated into ecological models; for example, areas of guano staining can be used to estimate the abundance of breeding penguins [22].  
In a cyclic manner, classified image objects and model outputs are further integrated into the segmentation and rule-set tuning process. Operator- and scene-dependency of rulesets can impair the repeatability and transferability of KBC workflows across scenes and sensors for the same land cover of interest. We propose to organize rulesets into target- and sensor-specific libraries [59; 60] and systematically store a set of classified image objects as \"bag-of-image-objects\" (BIO) for refining and adapting KBC workflows.  
Figure 2: Object-based penguin guano detection from VIRR images.  
It is important to emphasize that our approach is not a fully-developed system architecture but instead represents a road map for an \"image-to-assessment pipeline\" that links integral components like class explanation, remote sensing expert knowledge, domain ontology [61; 62], multiscale image content modeling, and ecological model integration. This work is crucial to decision support software that links image interpretation with statistical models of occupancy (presence and absence of populations) and abundance and, at a subsequent step, model exploration applications that allow stakeholders to perform geographic searches for populations, aggregate abundance across areas of interest (e.g., proposed Marine Protected Areas), and visualize predicted dynamics for future years. In this exploratory study, we develop those elements critical to the image interpretation components of this pipeline, specifically image segmentation, object-based classification, and ruleset transferability.