## Materials and Methods
### Supervised Classification Workflow  
Segmentation was performed using segment shape, color, and texture as components of the segmentation. Importance of small spectral differences was emphasized (spectral detail = 20). The same settings were used for both images except for minimum segment size which was 15 pixels for WV imagery and 10 for RE imagery due to the increased size of RE pixels. Training samples were created by manually drawing polygons representing each habitat class using the UAS mosaics as a reference (Figure 3). These UAS-derived training polygons were overlaid on segments from the segmentation process and the segment with the greatest area of overlap with each UAS-derived training polygon was identified. These identified segments were then used as training inputs for each classification  
Figure 2: Image processing included calibration, creation of additional image layers to test impact on final accuracy, and thresholding to eliminate complex water pixels. The classification workflow included creation of training samples using unoccupied aircraft system (UAS) imagery, segmentation of RE and WV-3 imagery, classification using a support vector machine, and filtering the classification output by elevation using LiDAR data.  
(Figure 2). This allowed the same training areas to be identified for both the RE and WV-3 imagery even though segmentation was performed separately for each imagery type. Training polygons included between 5000-6000 m\({}^{2}\) of each class in order to accurately capture spectral variation (Figure 4) and prevent training data imbalance between classes [48].  
Figure 4: _Cont._  
Figure 3: **(A)** shows a UAS mosaic (one of the five total) which was used as a reference to manually generate training polygons; only a subset of training polygons are displayed here as an example. **(B)** shows a WorldView-3 image in false color with the training polygons from frame A. overlaid. **(C)** shows the WorldView-3 image from Frame B post-segmentation with training polygons. The segment with the most overlap from the training polygons is input into the classification algorithm as the final training input. Note: the UAS imagery is 24\(\times\) higher resolution than the WV imagery, and 100\(\times\) higher resolution than RapidEye, allowing the analyst to determine classes with much higher confidence.  
Following segmentation, a Support Vector Machine (SVM) was used to classify all four image products. SVMs have been shown to handle large images, are less vulnerable to image noise, do not require a normal distribution of reflectance values, and provide classification results on par with other top algorithms [49]. SVMs are commonly used for classification applications, our contribution here lies in the use of added decision nodes for water classification, testing of image texture and NDVI, and UAS for SVM training and validation. The SVM output was split into Upland and Wetland classes using the LiDAR DEM acquired from NOAA, based on the tidal range of this region. Areas 0.9 m above MLLW were assigned to the Upland class [35] following the definition of these two classes (Table 1). Very high-resolution satellites often have many spectrally mixed pixels and segments, leading to a blur between land cover types that, when averaged together, may be incorrect. These isolated pixel groups (n < 40 for WV, n < 6 for RE) were removed, and re-classified using a majority filter in the final classification map.  
Figure 4: All training sample spectral signatures averaged by class for (**a**) WorldView-3 and (**b**) RapidEye sensors. Note seven classes are presented here instead of nine because Scrub-Shrub and Supratidal wetland vs. upland classes were delineated only based on the elevation and not spectral differences.