Cloud formations often obscure optical satellite-based monitoring of the Earth's surface, thus limiting Earth observation (EO) activities such as land cover mapping, ocean color analysis, and cropland monitoring. The integration of machine learning (ML) methods within the remote sensing domain has significantly improved performance on a wide range of EO tasks, including cloud detection and filtering, but there is still much room for improvement. A key bottleneck is that ML methods typically depend on large amounts of annotated data for training, which is often difficult to come by in EO contexts. This is especially true for the task of cloud optical thickness (COT) estimation. A reliable estimation of COT enables more fine-grained and application-dependent control compared to using pre-specified cloud categories, as is commonly done in practice. To alleviate the COT data scarcity problem, in this work we propose a novel synthetic dataset for COT estimation, where top-of-atmosphere radiances have been simulated for 12 of the spectral bands of the Multi-Spectral Instrument (MSI) sensor onboard Sentinel-2 platforms. These data points have been simulated under consideration of different cloud types, COTs, and ground surface and atmospheric profiles. Extensive experimentation of training several ML models to predict COT from the measured reflectivity of the spectral bands demonstrates the usefulness of our proposed dataset. Generalization to real data is also demonstrated on two satellite image datasets -- one that is publicly available, and one which we have collected and annotated. The synthetic data, the newly collected real dataset, code and models have been made publicly available at https://github.com/aleksispi/ml-cloud-opt-thick.

Abstract--Deep perceptual loss is a type of loss function in computer vision that aims to mimic human perception by using the deep features extracted from neural networks. In recent years, the method has been applied to great effect on a host of interesting computer vision tasks, especially for tasks with image or image-like outputs, such as image synthesis, segmentation, depth prediction, and more. Despite the increased interest and broader use, more effort is needed toward exploring which networks to use for calculating deep perceptual loss and from which layers to extract the features. This work aims to rectify this by systematically evaluating a host of commonly used and readily available, pretrained networks for a number of different feature extraction points on four existing use cases of deep perceptual loss. The use cases of perceptual similarity, super-resolution, image segmentation, and dimensionality reduction, are evaluated through benchmarks. The procedure followed in this work. In the last decade, machine learning for computer vision One of the methods that makes use of loss networks that have has evolved significantly. This evolution is mainly due to proven effective for a range of computer vision applications the developments in artificial neural networks. An essential is deep perceptual loss. Deep perceptual loss aims to create focus of these developments has been the calculation of the loss functions for machine learning models that mimic human loss used to train models. One group of loss functions build perception of similarity. This is typically done by calculating on using a neural network to calculate the loss for another the similarity of the output image and ground truth by how machine learning model. Among these methods are milestone similar the deep features (activations) of the loss network are achievements such as adversarial examples [1], generative when either image is used as input. Benchmark 3 [7] Deep perceptual loss is well suited for image synthesis, where evaluates U-nets [21] trained with the loss networks on the it is utilized to improve performance on various tasks.

Decoding inner speech from the brain signal via hybridisation of fMRI and EEG data is explored to investigate the performance benefits over unimodal models. Two different bimodal fusion approaches are examined: concatenation of probability vectors output from unimodal fMRI and EEG machine learning models, and data fusion with feature engineering. Same task inner speech data are recorded from four participants, and different processing strategies are compared and contrasted to previously-employed hybridisation methods. Data across participants are discovered to encode different underlying structures, which results in varying decoding performances between subject-dependent fusion models. Decoding performance is demonstrated as improved when pursuing bimodal fMRI-EEG fusion strategies, if the data show underlying structure.