Hyperspectral image segmentation: a comprehensive survey
Hyperspectral Images, which are high-dimensional in nature and capture bands over
hundreds of wavelengths of the electromagnetic spectrum. These images have piqued …
hundreds of wavelengths of the electromagnetic spectrum. These images have piqued …
Machine learning and deep learning techniques for spectral spatial classification of hyperspectral images: A comprehensive survey
The growth of Hyperspectral Image (HSI) analysis is due to technology advancements that
enable cameras to collect hundreds of continuous spectral information of each pixel in an …
enable cameras to collect hundreds of continuous spectral information of each pixel in an …
[HTML][HTML] Robust deep learning-based semantic organ segmentation in hyperspectral images
S Seidlitz, J Sellner, J Odenthal, B Özdemir… - Medical Image …, 2022 - Elsevier
Semantic image segmentation is an important prerequisite for context-awareness and
autonomous robotics in surgery. The state of the art has focused on conventional RGB video …
autonomous robotics in surgery. The state of the art has focused on conventional RGB video …
Hyperspectral band selection using attention-based convolutional neural networks
Hyperspectral imaging has become a mature technology which brings exciting possibilities
in various domains, including satellite image analysis. However, the high dimensionality and …
in various domains, including satellite image analysis. However, the high dimensionality and …
Evaluating algorithms for anomaly detection in satellite telemetry data
Detecting anomalies in telemetry data captured on-board a spacecraft is critical to ensure its
safe operation. Although there exist various techniques for automatically detecting point …
safe operation. Although there exist various techniques for automatically detecting point …
Towards on-board hyperspectral satellite image segmentation: Understanding robustness of deep learning through simulating acquisition conditions
Although hyperspectral images capture very detailed information about the scanned objects,
their efficient analysis, transfer, and storage are still important practical challenges due to …
their efficient analysis, transfer, and storage are still important practical challenges due to …
[HTML][HTML] An improved SqueezeNet model for the diagnosis of lung cancer in CT scans
M Tsivgoulis, T Papastergiou… - Machine Learning with …, 2022 - Elsevier
Lung cancer is the leading cause of cancer deaths nowadays and its early detection and
treatment plays an important role in survival of patients. The main challenge is to acquire an …
treatment plays an important role in survival of patients. The main challenge is to acquire an …
Taking artificial intelligence into space through objective selection of hyperspectral earth observation applications: To bring the “brain” close to the “eyes” of satellite …
AM Wijata, MF Foulon, Y Bobichon… - … and Remote Sensing …, 2023 - ieeexplore.ieee.org
Recent advances in remote sensing hyperspectral imaging and artificial intelligence (AI)
bring exciting opportunities to various fields of science and industry that can directly benefit …
bring exciting opportunities to various fields of science and industry that can directly benefit …
Atmospheric correction of vegetation reflectance with simulation-trained deep learning for ground-based hyperspectral remote sensing
Background Vegetation spectral reflectance obtained with hyperspectral imaging (HSI) offer
non-invasive means for the non-destructive study of their physiological status. The light …
non-invasive means for the non-destructive study of their physiological status. The light …
Squeezing adaptive deep learning methods with knowledge distillation for on-board cloud detection
Cloud detection is a pivotal satellite image pre-processing step that can be performed on
board a satellite to tag useful images. It can reduce the amount of data to downlink by …
board a satellite to tag useful images. It can reduce the amount of data to downlink by …