A Robust Context-Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification.

Hyperspectral imaging is an area of active research with many applications in remote sensing, mineral exploration, and environmental monitoring. Deep learning and, in particular, convolution-based approaches are the current state-of-the-art classification models. However, in the presence of noisy hyperspectral datasets, these deep convolutional neural networks underperform. In this paper, we proposed a feature augmentation approach to increase noise resistance in imbalanced hyperspectral classification. Our method calculates context-based features, and it uses a deep convolutional neuronet (DCN). We tested our proposed approach on the Pavia datasets and compared three models, DCN, PCA + DCN, and our context-based DCN, using the original datasets and the datasets plus noise. Our experimental results show that DCN and PCA + DCN perform well on the original datasets but not on the noisy datasets. Our robust context-based DCN was able to outperform others in the presence of noise and was able to maintain a comparable classification accuracy on clean hyperspectral images.

Dynamic in-stream fate modeling of xenobiotic organic compounds: a case study of linear alkylbenzene sulfonates in the Lambro River, Italy.

Using a conceptual hydraulic model, a one-dimensional dynamic river water quality model has been developed to assess the short-term fate of linear alkylbenzene sulfonates (LAS) in the river compartments water and benthic sediment. The model assumes local equilibrium sorption and that both dissolved and sorbed chemical are available for biodegradation. To investigate the interaction of nutrient dynamics and organic contaminant fate, the model is coupled with a basic water quality model. On the basis of the Lambro River (Italy) as a case study, the result shows that the model predictions agree well with the measured data set. The model output sensitivity to model parameters has been tested, and the results depict that the model is highly sensitive to the biodegrading parameters. Also, a comparison of a steady state with a dynamic simulation and the effect of nutrient dynamics on the LAS fate in the Lambro River as a scenario analysis are presented. The results indicate the usefulness of the proposed model for the short-term simulation of organic contaminant fate in unsteady environmental conditions.