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2 results for “Line-Scanning”
Three Annotated Anomaly Detection Datasets for Line-Scan Algorithms
<h1>Summary</h1> <p>This dataset contains two hyperspectral and one multispectral anomaly detection images, and their corresponding binary pixel masks. They were initially used for real-time anomaly detection in line-scanning, but they can be used for any anomaly detection task.</p> <p>They are in .npy file format (will add tiff or geotiff variants in the future), with the image datasets being in the order of (height, width, channels). The SNP dataset was collected using sentinelhub, and the Synthetic dataset was collected from AVIRIS. The Python code used to analyse these datasets can be found at: https://github.com/WiseGamgee/HyperAD</p> <h1>How to Get Started</h1> <p>All that is needed to load these datasets is Python (preferably 3.8+) and the NumPy package. Example code for loading the Beach Dataset if you put it in a folder called "data" with the python script is:</p> <pre><code>import numpy as np # Load image file hsi_array = np.load("data/beach_hsi.npy") n_pixels, n_lines, n_bands = hsi_array.shape print(f"This dataset has {n_pixels} pixels, {n_lines} lines, and {n_bands}.") # Load image mask mask_array = np.load("data/beach_mask.npy") m_pixels, m_lines = mask_array.shape print(f"The corresponding anomaly mask is {m_pixels} pixels by {m_lines} lines.")</code></pre> <h1>Citing the Datasets</h1> <p>If you use any of these datasets, please cite the following paper:</p> <pre><code>@article{garske2024erx,</code><br><code> title={ERX - a Fast Real-Time Anomaly Detection Algorithm for Hyperspectral Line-Scanning},</code><br><code> author={Garske, Samuel and Evans, Bradley and Artlett, Christopher and Wong, KC},</code><br><code> journal={arXiv preprint arXiv:2408.14947},</code><br><code> year={2024},</code><br><code>}</code></pre> <div> <pre>If you use the beach dataset please cite the following paper as well (original source):</pre> </div> <pre><code>@article{mao2022openhsi, title={OpenHSI: A complete open-source hyperspectral imaging solution for everyone}, author={Mao, Yiwei and Betters, Christopher H and Evans, Bradley and Artlett, Christopher P and Leon-Saval, Sergio G and Garske, Samuel and Cairns, Iver H and Cocks, Terry and Winter, Robert and Dell, Timothy}, journal={Remote Sensing}, volume={14}, number={9}, pages={2244}, year={2022}, publisher={MDPI} }</code></pre>
Raw data accompanying the manuscript "Super-resolution fluorescence microscopy by line-scanning with an unmodified two-photon microscope"
<p>Raw data sets (.tif files) of data utilized to demonstrate 2D SIM with an unmodified multi photon intravital fluorescence microscope.</p>
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