Exploring unsupervised domain adaptation approaches for water parameters estimation from satellite images

In this paper, we compare several domain adaptation approaches in classifying water quality in reservoirs using spectral data from satellite images to two optical parameters: turbidity and chlorophyll-a. This assessment adds a new possibility in monitoring these water quality parameters, in addition to the traditional in-situ investigation, which is expensive and time-consuming. The study acquired images from two data sources characterized by different geographic regions (USA and Brazil) and verified the inference quality of the model trained in the source domain on samples from the target domain. The experiments used two classifiers, OSCVM and ANN, for domain adaptation methods based on instances, features, and depth. The results suggest domain adaptation is an efficient alternative when labeled data is scarce. Furthermore, we evaluate the need to handle imbalanced data, a characteristic of real-world problems like the data explored here. Based on promising accuracy results, we show that applying domain adaptation techniques in databases with little data, such as the Brazilian database, and without labeled data, is an efficient and low-cost alternative that can be useful in monitoring reservoirs in different regions.