Wuhan University

About: Wuhan University is a education organization based out in Wuhan, China. It is known for research contribution in the topics: Computer science & Population. The organization has 92849 authors who have published 92882 publications receiving 1691049 citations. The organization is also known as: WHU & Wuhan College.

Processing of visually evoked innate fear by a non-canonical thalamic pathway.

Abstract: The ability of animals to respond to life-threatening stimuli is essential for survival. Although vision provides one of the major sensory inputs for detecting threats across animal species, the circuitry underlying defensive responses to visual stimuli remains poorly defined. Here, we investigate the circuitry underlying innate defensive behaviours elicited by predator-like visual stimuli in mice. Our results demonstrate that neurons in the superior colliculus (SC) are essential for a variety of acute and persistent defensive responses to overhead looming stimuli. Optogenetic mapping revealed that SC projections to the lateral posterior nucleus (LP) of the thalamus, a non-canonical polymodal sensory relay, are sufficient to mimic visually evoked fear responses. In vivo electrophysiology experiments identified a di-synaptic circuit from SC through LP to the lateral amygdale (Amg), and lesions of the Amg blocked the full range of visually evoked defensive responses. Our results reveal a novel collicular-thalamic-Amg circuit important for innate defensive responses to visual threats.

Random-Selection-Based Anomaly Detector for Hyperspectral Imagery

Abstract: Anomaly detection in hyperspectral images is of great interest in the target detection domain since it requires no prior information and makes full use of the spectral differences revealed in hyperspectral images. The current anomaly detection methods are susceptible to anomalies in the processing window range or the image scope. In addition, for the local anomaly detection methods themselves, it is difficult to determine the window size suitable for processing background statistics. This paper proposes an anomaly detection method based on the random selection of background pixels, the random-selection-based anomaly detector (RSAD). Pixels are randomly selected from the image scene to represent the background statistics; the random selections are performed a sufficient number of times; blocked adaptive computationally efficient outlier nominators are used to detect anomalies each time after a proper subset of background pixels is selected; finally, a fusion procedure is employed to avoid contamination of the background statistics by anomaly pixels. In addition, the real-time implementation of the RSAD is also developed by random selection from updating data and QR decomposition. Several hyperspectral data sets are used in the experiments, and the RSAD shows a better performance than the current hyperspectral anomaly detection algorithms. The real-time version also outperforms its real-time counterparts.

Missing Data Reconstruction in Remote Sensing Image With a Unified Spatial–Temporal–Spectral Deep Convolutional Neural Network

Abstract: Because of the internal malfunction of satellite sensors and poor atmospheric conditions such as thick cloud, the acquired remote sensing data often suffer from missing information, i.e., the data usability is greatly reduced. In this paper, a novel method of missing information reconstruction in remote sensing images is proposed. The unified spatial–temporal–spectral framework based on a deep convolutional neural network (CNN) employs a unified deep CNN combined with spatial–temporal–spectral supplementary information. In addition, to address the fact that most methods can only deal with a single missing information reconstruction task, the proposed approach can solve three typical missing information reconstruction tasks: 1) dead lines in Aqua Moderate Resolution Imaging Spectroradiometer band 6; 2) the Landsat Enhanced Thematic Mapper Plus scan line corrector-off problem; and 3) thick cloud removal. It should be noted that the proposed model can use multisource data (spatial, spectral, and temporal) as the input of the unified framework. The results of both simulated and real-data experiments demonstrate that the proposed model exhibits high effectiveness in the three missing information reconstruction tasks listed above.