Editorial for the special issue “Advances in object and activity detection in remote sensing imagery”

Advances in data collection and accessibility, such as unmanned aerial vehicle (UAV) technology, the availability of satellite imagery, and the increasing performance of deep learning models, have had significant impacts on solving various remote sensing problems and proposing new applications ranging from vegetation and wildlife monitoring to crowd monitoring. This Special Issue contains seven high-quality papers [1,2,3,4,5,6,7] approaching problems relating to object detection, semantic segmentation, and multi-modal data alignment. In terms of the methods utilized, it is not surprising that six of the seven papers on this issue involve the application of deep learning. The papers also attest to the powerful aspect of the field where researchers can collaborate and validate their work on open-source models and datasets

Vegetation high-impedance faults' high-frequency signatures via sparse coding

High-impedance faults (HIFs) behavior in power distribution systems depends on multiple factors, making it a challenging disturbance to model. Factors, such as network characteristics and impedance surface, can change the phenomena so intensely that insights about their behavior may not translate well between faults with different parameters. Signal processing techniques can help reveal patterns from specific types of fault, given the availability of sampled data from real faults. The methodology described in this article uses the shift-invariant sparse coding technique on a data set of staged vegetation HIFs to address this hypothesis. The technique facilitates the uncoupling of shifted and convoluted patterns present in the recorded fault signals, while a methodology to correlate them with fault occurrences is proposed. The investigation of underdiscussed high-frequency fault signals from a specific type of fault (small current vegetation HIFs) distinguishes this article from related works. The methodology to attest the found patterns as fault signatures and their analysis while using a particular high-frequency sampling method are key novel aspects presented. Nonetheless, the evidence of consistent behavior in real vegetation HIFs at higher frequencies that could assist their detection is the main contribution of this article. These results can enhance phenomena awareness and support future methodologies dealing with such disturbances

Features of ICU admission in x-ray images of Covid-19 patients

This paper presents an original methodology for extracting semantic features from X-rays images that correlate to severity from a data set with patient ICU admission labels through interpretable models. The validation is partially performed by a proposed method that correlates the extracted features with a separate larger data set that does not contain the ICU-outcome labels. The analysis points out that a few features explain most of the variance between patients admitted in ICUs or not. The methods herein can be viewed as a statistical approach highlighting the importance of features related to ICU admission that may have been only qualitatively reported. In between features shown to be over-represented in the external data set were ones like ‘Consolidation’ (1.67), ‘Alveolar’ (1.33), and ‘Effusion’ (1.3). A brief analysis on the locations also showed higher frequency in labels like ‘Bilateral’ (1.58) and Peripheral (1.28) in patients labelled with higher chances to be admitted in ICU. To properly handle the limited data sets, a state-of-the-art lung segmentation network was also trained and presented, together with the use of low-complexity and interpretable models to avoid overfitting