## 4 Discussion  
We have focused on giving more rigor to our detail-oriented seep-spill discrimination (Section 2.4). This second data-driven effort to try to improve the slick category categorization has benefited from the findings of our earlier investigations (Section 2.3). The study presented herein bridges our academic oil-slick remote sensing investigation and a specific ongoing operational application of the petroleum industry (Section 2.2). Because the seep-spill discrimination research is at an early stage of development, there is a continuing necessity to devote scientific attention to it. As this need tends to increase with time, reliable means of improving the capabilities to differentiate the slick category are required [29; 30; 31; 32; 33; 34]. There is a lack of information in the literature concerning this topic; see [29] and references therein. There have been a number of review papers, but these have focused on the detection and characterization of oil slicks in satellite remotely sensed images, and have not addressed the categorization of slicks into seeps or spills--e.g., [80]. We recognize our linear technique exploring LDA-based algorithms is one of several possible approaches leading to improving seep-spill discrimination skills. So far, our approach has been to explore simple methods before moving on to more complex ones. Other multivariate data analyses (e.g., cubist or random forest) may also lead to better slick category discrimination. Nevertheless, further studies are needed to investigate whether these approaches can be more successful in discriminating seeps from spills than our reported results: sound overall accuracy of about 70% and practical levels of the associated standard statistical metrics--e.g., ~80% of sensitivity, ~75% of specificity, ~65% and ~75% of positive and negative predictive values, respectively. These have been reached while evaluating our algorithms using all 4562 oil slicks for training. We look forward to see the exploratory data analysis promoted by our study motivating other scholars to investigate alternative methods to discriminate the categories of oil slicks at the sea surface. The slick category of our earlier investigations occurred with practical overall accuracy levels of ~70% [29; 30; 31; 32]. This was found when starting the analyses with different sets of variables (>500 in [29; 30] and 19 in [31; 32]) and selecting uncorrelated attributes in two different ways: CFS and the UPGMA dendrograms both together with PCA in [29; 30], and the simple use of a stricter UPGMA phenon cut-off but without PCA in [31; 32]. Notwithstanding the best seep-spill discrimination power of our current approach being comparable to our previous investigations (~70%), we have improved it in the sense that our LDA-based efficiency is improved as we started the analysis with only 13 variables, instead of ~500 [29; 30] or 19 [31; 32] as before (Figure 3). To this matter, the outcomes of our simple LDA approaches can guide the selection of variables to be possibly used in more complex analyses. In fact, the possibility to explore fewer variables is indeed an advantage to any eventual operational use of our seep-spill discrimination strategy. Regarding the UPGMA dendrograms (Figures 4 and 5), we observe that the SAR basic statistics variables exhibit significant statistical correlation and group among themselves: the green-blue group. The pixel distribution metrics and the Area-Per pair also have significant statistical correlation: the gray-yellow group. From these two larger groups we selected VAR and KUR, while in the first refined study we chose AVG and SKW [31; 32]. This difference is rooted in the analysis of the major correlation matrices that show the former pair has less correlation than the latter with all other variables in all 39-data instances.