Abstract
Advances in machine learning have changed many fields of study and it has also drawn attention in a variety of remote sensing applications. In particular, deep convolutional neural networks (CNNs) have proven very useful in fields such as image recognition; however, the use of CNNs in large-scale remote sensing landcover classifications still needs further investigation. We set out to test CNN-based landcover classification against a more conventional XGBoost shallow learning algorithm for mapping a notoriously difficult group of landcover classes, wetland class as defined by the Canadian Wetland Classification System. We developed two wetland inventory style products for a large (397,958 km2) area in the Boreal Forest region of Alberta, Canada, using Sentinel-1, Sentinel-2, and ALOS DEM data acquired in Google Earth Engine. We then tested the accuracy of these two products against three validation data sets (two photo-interpreted and one field). The CNN-generated wetland product proved to be more accurate than the shallow learning XGBoost wetland product by 5%. The overall accuracy of the CNN product was 80.2% with a mean F1-score of 0.58. We believe that CNNs are better able to capture natural complexities within wetland classes, and thus may be very useful for complex landcover classifications. Overall, this CNN framework shows great promise for generating large-scale wetland inventory data and may prove useful for other landcover mapping applications.
Highlights
Machine learning—a method where a computer discovers rules to execute a data processing task, given training examples—can generally be divided into two categories: Shallow learning and deep learning methods [1]
We focus on the prediction of wetland class, as defined by the Canadian Wetland Classification System [23], and the prediction of upland and open water classes to fill out the classified map
With the current status of machine learning and the history of Canadian wetland mapping in mind, we propose a simple goal for this study: To compare deep learning (CNN) classifications with shallow learning (XGBoost) classifications for wetland class mapping over a large region of Alberta, Canada
Summary
Machine learning—a method where a computer discovers rules to execute a data processing task, given training examples—can generally be divided into two categories: Shallow learning and deep learning methods [1]. Countless studies have used random forest [4], support vector machine (SVM) [5], boosted regression trees [6,7], and many other algorithms to classify landcover from Earth observation and remote sensing data These algorithms typically work on a pixel-level or object-level. Pixel-level algorithms extract the numerical value of the remote sensing inputs (i.e., vegetation index value, radar backscatter, relative elevation) and match that to a known landcover class (i.e., forest) With this numerical data, a shallow learning model can be built through methods such as kernel methods/decision boundaries [8], decision trees [9], and gradient boosting [10]. Both pixel- and object-based methods are widely used and both have their pros and cons [12]
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