## 4 Discussion  
This is the first attempt of which we are aware to use of geographic object-based image analysis paradigm in penguin guano stain detection as an alternative for time- and labor-intensive human-augmented image interpretation. In this exploratory study, we tasked object-based methods to classify chinstrap and Adelie guano stains from very high spatial resolution satellite imagery and closely examined the transferability of knowledge-based GEOBIA rules across different study sites focusing on the same semantic class. A master ruleset was developed based on a QuickBird image scene encompassing the Hope Bay area and it was re-tasked \"without adaptation\" and \"with adaptation\" on candidate image scenes (QuickBird and WorldView-2) comprising guano stains from  
Figure 4: Zoomed-in areas showing: the original image (**left**); classification based on the reference ruleset (**middle**); and classification based on the adapted ruleset (**right**), from the reference study site and the candidate sites. A section of the reference ruleset programmed in Cognitive Network Language (CNL) is also provided at top right. Classified guano is shown in red. Imagery copyright DigitalGlobe, Inc.  
six different study sites. The modular design of our experiment aimed to systematically gauge the segmentation quality, classification accuracy, and the robustness of fuzzy rulesets. Image segmentation serves as the precursor step in the GEOBIA. Based on the highlighted success rate, in this study, we used eCognition developer's multiresolution segmentation (MRS) algorithm and complemented it with the multi-threshold segmentation (MTS) algorithm, which can be manipulated in a self-adaptive manner. The underlying idea of coupling MTS and MRS algorithms is to initially reduce the scene complexity at multiscale settings and later feed those \"pointer segments\" (semantically ill-defined and mixed objects) to the MRS algorithm for more meaningful fine-scale segmentation that creates segments resembling real-world target objects. This hybrid procedure consumes notably less time for scene-wise segmentation than tasking the complex MRS algorithm alone over the entire scene. Segmentation algorithms are inherently scene dependent, which impairs the transferability of a particular segmentation algorithm's parameter settings (e.g., scale parameter of the MRS algorithm) from one scene to another. Thus, we fine-tuned the segmentation process on a trial-and-error basis at each study site until an acceptable segmentation quality was achieved before tasking either master or adapted rulesets. In general, optimal segmentation results, an equilibrium stage of under- and over-segmentation, are favored for achieving a better classification. From that respect, we purposely employed an empirical segmentation quality measure- Euclidean Distance 2 (ED2), which operates on arithmetic and geometric agreement between reference objects and the image object candidates. Compared to other sites, HOPE reported equally high geometric and arithmetic fit yielding the best overall segmentation quality (lowest ED2). The marked spectral contrast between the guano stains and the dark background materials has led to a better delineation of object boundaries in the segmentation process. In contrast, ZAVO showed the highest ED2 metric value, which was mainly due to the weak arithmetic congruency (high NSR score) at the expense of high geometrical fit. A similar trend can be observed in the remaining study sites as well, for example, BIRD, PAUL, and SPIN. This \"one-to-many\" relationship between the reference objects and the image object candidates arises primarily due to two reasons: (1) the occurrence of target objects at multiple scales, which impairs the proper isolation of object boundaries at a single scale; (2) when producing reference objects, human interpreters tend to generalize object boundaries [75] based on their experience and semantic meanings of objects than low-level image features like color, texture, or shape.