Image Fusion
Hyperspectral imaging (HSI) has become a pivotal tool for environmental monitoring, particularly in identifying and analyzing hydrocarbon spills. This study presents an Internet of Things (IoT)-based framework for the collection, management, and analysis of hyperspectral data, employing a controlled experimental setup to simulate hydrocarbon contamination. Using a state-of-the-art hyperspectral camera, a dataset of 116 images was generated, encompassing temporal and spectral variations of gasoline, thinner, and motor oil spills.
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The increasing number of wildfires damages nature and human life, making the early detection of wildfires in complex outdoor environments critical. With the advancement of drones and remote sensing technology, infrared cameras have become essential for wildfire detection. However, as the demand for higher accuracy in detection algorithms grows, the detection model's size and computational costs increase, making it challenging to deploy high-precision detection algorithms on edge computing devices onboard drones for real-time fire detection.
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The file contains merely a small portion of our research results. The particular picture presented showcases the outcomes that were achieved through our fusion method when applied to the TNO test set. The "ir", which is the abbreviation for infrared light image, is characterized by being rich in thermal radiation information. However, it unfortunately has a rather low spatial resolution. On the other hand, the "vis", representing the visible light image, is abundant in scene information. But it has a drawback in that the human targets within it are not so distinctly visible.
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148 Views
The IARPA WRIVA program aims to develop software systems that can create photorealistic, navigable 3D site models using a highly limited corpus of imagery, to include ground level imagery, surveillance height imagery, airborne altitude imagery, and satellite imagery. Additionally, where imagery lacks metadata indicating geolocation, information about camera parameters, or is corrupted by artifacts, WRIVA seeks to detect and correct these factors to incorporate the imagery in site-modelling and other downstream image processing and analysis algorithms.
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206 Views
When training supervised deep learning models for despeckling SAR images, it is necessary to have a labeled dataset with pairs of images to be able to assess the quality of the filtering process. These pairs of images must be noisy and ground truth. The noisy images contain the speckle generated during the backscatter of the microwave signal, while the ground truth is generated through multitemporal fusion operations. In this paper, two operations are performed: mean and median.
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603 Views
These are tight pedestrian masks for the thermal images present in the KAIST Multispectral pedestrian dataset, available at https://soonminhwang.github.io/rgbt-ped-detection/
Both the thermal images themselves as well as the original annotations are a part of the parent dataset. Using the annotation files provided by the authors, we develop the binary segmentation masks for the pedestrians, using the Segment Anything Model from Meta.
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446 Views
The detection of the collapse of landslides trigerred by intense natural hazards, such as earthquakes and rainfall, allows rapid response to hazards which turned into disasters. The use of remote sensing imagery is mostly considered to cover wide areas and assess even more rapidly the threats. Yet, since optical images are sensitive to cloud coverage, their use is limited in case of emergency response. The proposed dataset is thus multimodal and targets the early detection of landslides following the disastrous earthquake which occurred in Haiti in 2021.
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850 Views
We are pleased to introduce the Qilin Watermelon Dataset, a unique collection of data aimed at investigating the relationship between a watermelon's appearance, tapping sound, and sweetness. This dataset is the result of our dedicated efforts to capture and record various aspects of Qilin watermelons, a special variety known for its exceptional taste and quality.
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1705 Views
CT (roentgen-ray computed tomography) A beam of x-rays is shot straight through the brain. As it comes out the other side, the beam is blunted slightly because it has hit dense living tissues on the way through. Blunting or "attenuation" of the x-ray comes from the density of the tissue encountered along the way. Very dense tissue like bone blocks lots of x-rays; grey matter blocks some and fluid even less. X-ray detectors positioned around the circumference of the scanner collect attenuation readings from multiple angles. A computerized algorithm reconstructs an image of each slice
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398 Views