# Extraction of Visible Boundaries for Cadastral Mapping Based on UAV Imagery
## 4 Discussion
### Strengths and Limitations of the Automatic Extraction Method Used  
The ENVI FX module handled the full extent of the resampled UAV orthoimages, and no additional image tiling or image conversions were required. ENVI FX provided closed object boundaries directly in vector format, topologically correct polygons. Therefore, no additional image post-processing step, such as vectorization of detected object boundaries, was needed (Figure 5). Thus, the visible object boundaries generated can be directly used for further processing and analysis within geographic information systems (GIS). Additionally, the final output consists of spatial, spectral and textural attributes which are assigned automatically to each extracted object and saved in the attribute table. The vectorized and geo-referenced visible object boundaries, as interpreted in this research, are crucial in cadastral applications especially for the purposes of land plot boundary delineations. Overall, ENVI FX has the potential to automatically delineate visible cadastral boundaries, especially in rural areas. A comparison of the results regarding the accuracy assessment obtained in this study and the accuracies obtained in the studies [26; 27; 28] cannot be done at this time for a number of reasons. First, not all the reviewed feature extraction methods have been applied to UAV imagery. Second, different UAVs may provide different quality of orthoimages. Third, the nature, size, location, and the characteristics of the study objects are far too different. In order to make a reliable comparison on accuracy assessments of different feature extraction methods, first of all, each method has to be studied individually and later tested at the same study area(s). However, the image processing approach of different feature extractions methods may be comparable. From the reviewed feature extraction methods that have already been applied for detection of visible cadastral boundaries, it can be seen that the MRS method, ESP method, and mean-segmentation method also do not require further image tiling and the final output of the boundary map was in vector format [27; 28]. In contrast, vectorization of detected object boundaries was needed for the gPb contour detection method. In addition, it was reported that the method is inapplicable when processing UAV images of more than 1000 pixels in width and height [26]. Similar issues regarding the vectorization of detected object boundaries were reported in [38], where Canny and Sobel edge detection algorithms were used. In order to obtain topologically correct polygons, an additional feature extraction method was used aiming to connect the edges. ENVI FX allowed some shadow areas in the UAV orthoimage to be extracted as boundaries; however, these do not represent real boundaries in the field. In order to minimize the influence of shadows on feature extraction, it is recommended to capture images in the local time where the solar zenith angle has the smallest possible value. However, the solar zenith angle depends on the geographic location of the study area. Additionally, some other factors such as weather conditions also influence the quality of captured images. To avoid such issues, it is preferable to capture images on a cloudy day without wind. Although ENVI FX has proved to be efficient, one of its limitations is that it is not an open-source tool like mean-shift segmentation, gPb contour detection, Canny, and Sobel, which might be a reason why it is not often used in the land surveyor community. In addition, the extracted objects from the resampled UAV orthoimages were following the pixel borders and further shape simplification was required to make them comparable to spatial units in cadastral applications.