Deep Learning for In-Orbit Cloud Segmentation and Classification in Hyperspectral Satellite Data

This article explores the latest Convolutional Neural Networks (CNNs) for cloud detection aboard hyperspectral satellites. The performance of the latest 1D CNN (1D-Justo-LiuNet) and two recent 2D CNNs (nnU-net and 2D-Justo-UNet-Simple) for cloud segmentation and classification is assessed. Evaluation criteria include precision and computational efficiency for in-orbit deployment. Experiments utilize NASA's EO-1 Hyperion data, with varying spectral channel numbers after Principal Component Analysis. Results indicate that 1D-Justo-LiuNet achieves the highest accuracy, outperforming 2D CNNs, while maintaining compactness with larger spectral channel sets, albeit with increased inference times. However, the performance of 1D CNN degrades with significant channel reduction. In this context, the 2D-Justo-UNet-Simple offers the best balance for in-orbit deployment, considering precision, memory, and time costs. While nnU-net is suitable for on-ground processing, deployment of lightweight 1D-Justo-LiuNet is recommended for high-precision applications. Alternatively, lightweight 2D-Justo-UNet-Simple is recommended for balanced costs between timing and precision in orbit.

Prodromal Diagnosis of Lewy Body Diseases Based on the Assessment of Graphomotor and Handwriting Difficulties

To this date, studies focusing on the prodromal diagnosis of Lewy body diseases (LBDs) based on quantitative analysis of graphomotor and handwriting difficulties are missing. In this work, we enrolled 18 subjects diagnosed with possible or probable mild cognitive impairment with Lewy bodies (MCI-LB), 7 subjects having more than 50% probability of developing Parkinson's disease (PD), 21 subjects with both possible/probable MCI-LB and probability of PD > 50%, and 37 age- and gender-matched healthy controls (HC). Each participant performed three tasks: Archimedean spiral drawing (to quantify graphomotor difficulties), sentence writing task (to quantify handwriting difficulties), and pentagon copying test (to quantify cognitive decline). Next, we parameterized the acquired data by various temporal, kinematic, dynamic, spatial, and task-specific features. And finally, we trained classification models for each task separately as well as a model for their combination to estimate the predictive power of the features for the identification of LBDs. Using this approach we were able to identify prodromal LBDs with 74% accuracy and showed the promising potential of computerized objective and non-invasive diagnosis of LBDs based on the assessment of graphomotor and handwriting difficulties.

Exploration of Various Fractional Order Derivatives in Parkinson's Disease Dysgraphia Analysis

Parkinson's disease (PD) is a common neurodegenerative disorder with a prevalence rate estimated to 2.0% for people aged over 65 years. Cardinal motor symptoms of PD such as rigidity and bradykinesia affect the muscles involved in the handwriting process resulting in handwriting abnormalities called PD dysgraphia. Nowadays, online handwritten signal (signal with temporal information) acquired by the digitizing tablets is the most advanced approach of graphomotor difficulties analysis. Although the basic kinematic features were proved to effectively quantify the symptoms of PD dysgraphia, a recent research identified that the theory of fractional calculus can be used to improve the graphomotor difficulties analysis. Therefore, in this study, we follow up on our previous research, and we aim to explore the utilization of various approaches of fractional order derivative (FD) in the analysis of PD dysgraphia. For this purpose, we used the repetitive loops task from the Parkinson's disease handwriting database (PaHaW). Handwritten signals were parametrized by the kinematic features employing three FD approximations: Gr\"unwald-Letnikov's, Riemann-Liouville's, and Caputo's. Results of the correlation analysis revealed a significant relationship between the clinical state and the handwriting features based on the velocity. The extracted features by Caputo's FD approximation outperformed the rest of the analyzed FD approaches. This was also confirmed by the results of the classification analysis, where the best model trained by Caputo's handwriting features resulted in a balanced accuracy of 79.73% with a sensitivity of 83.78% and a specificity of 75.68%.

Preliminary experiments on thermal emissivity adjustment for face images

In this paper we summarize several applications based on thermal imaging. We emphasize the importance of emissivity adjustment for a proper temperature measurement. A new set of face images acquired at different emissivity values with steps of 0.01 is also presented and will be distributed for free for research purposes. Among the utilities, we can mention: a) the possibility to apply corrections once an image is acquired with a wrong emissivity value and it is not possible to acquire a new one; b) privacy protection in thermal images, which can be obtained with a low emissivity factor, which is still suitable for several applications, but hides the identity of a user; c) image processing for improving temperature detection in scenes containing objects of different emissivity.

Contribution of the Temperature of the Objects to the Problem of Thermal Imaging Focusing

When focusing an image, depth of field, aperture and distance from the camera to the object, must be taking into account, both, in visible and in infrared spectrum. Our experiments reveal that in addition, the focusing problem in thermal spectrum is also hardly dependent of the temperature of the object itself (and/or the scene).

A Naturalistic Database of Thermal Emotional Facial Expressions and Effects of Induced Emotions on Memory

This work defines a procedure for collecting naturally induced emotional facial expressions through the vision of movie excerpts with high emotional contents and reports experimental data ascertaining the effects of emotions on memory word recognition tasks. The induced emotional states include the four basic emotions of sadness, disgust, happiness, and surprise, as well as the neutral emotional state. The resulting database contains both thermal and visible emotional facial expressions, portrayed by forty Italian subjects and simultaneously acquired by appropriately synchronizing a thermal and a standard visible camera. Each subject's recording session lasted 45 minutes, allowing for each mode (thermal or visible) to collect a minimum of 2000 facial expressions from which a minimum of 400 were selected as highly expressive of each emotion category. The database is available to the scientific community and can be obtained contacting one of the authors. For this pilot study, it was found that emotions and/or emotion categories do not affect individual performance on memory word recognition tasks and temperature changes in the face or in some regions of it do not discriminate among emotional states.

Face segmentation: A comparison between visible and thermal images

Face segmentation is a first step for face biometric systems. In this paper we present a face segmentation algorithm for thermographic images. This algorithm is compared with the classic Viola and Jones algorithm used for visible images. Experimental results reveal that, when segmenting a multispectral (visible and thermal) face database, the proposed algorithm is more than 10 times faster, while the accuracy of face segmentation in thermal images is higher than in case of Viola-Jones

Perceptual Features as Markers of Parkinson's Disease: The Issue of Clinical Interpretability

Up to 90% of patients with Parkinson's disease (PD) suffer from hypokinetic dysathria (HD) which is also manifested in the field of phonation. Clinical signs of HD like monoloudness, monopitch or hoarse voice are usually quantified by conventional clinical interpretable features (jitter, shimmer, harmonic-to-noise ratio, etc.). This paper provides large and robust insight into perceptual analysis of 5 Czech vowels of 84 PD patients and proves that despite the clinical inexplicability the perceptual features outperform the conventional ones, especially in terms of discrimination power (classification accuracy ACC = 92 %, sensitivity SEN = 93 %, specificity SPE = 92 %) and partial correlation with clinical scores like UPDRS (Unified Parkinson's disease rating scale), MMSE (Mini-mental state examination) or FOG (Freezing of gait questionnaire), where p < 0.0001.

Identification of Hypokinetic Dysarthria Using Acoustic Analysis of Poem Recitation

Up to 90 % of patients with Parkinson's disease (PD) suffer from hypokinetic dysarthria (HD). In this work, we analysed the power of conventional speech features quantifying imprecise articulation, dysprosody, speech dysfluency and speech quality deterioration extracted from a specialized poem recitation task to discriminate dysarthric and healthy speech. For this purpose, 152 speakers (53 healthy speakers, 99 PD patients) were examined. Only mildly strong correlation between speech features and clinical status of the speakers was observed. In the case of univariate classification analysis, sensitivity of 62.63% (imprecise articulation), 61.62% (dysprosody), 71.72% (speech dysfluency) and 59.60% (speech quality deterioration) was achieved. Multivariate classification analysis improved the classification performance. Sensitivity of 83.42% using only two features describing imprecise articulation and speech quality deterioration in HD was achieved. We showed the promising potential of the selected speech features and especially the use of poem recitation task to quantify and identify HD in PD.

Robust and Complex Approach of Pathological Speech Signal Analysis

This paper presents a study of the approaches in the state-of-the-art in the field of pathological speech signal analysis with a special focus on parametrization techniques. It provides a description of 92 speech features where some of them are already widely used in this field of science and some of them have not been tried yet (they come from different areas of speech signal processing like speech recognition or coding). As an original contribution, this work introduces 36 completely new pathological voice measures based on modulation spectra, inferior colliculus coefficients, bicepstrum, sample and approximate entropy and empirical mode decomposition. The significance of these features was tested on 3 (English, Spanish and Czech) pathological voice databases with respect to classification accuracy, sensitivity and specificity.