Search results for "spektrikuvaus"
showing 5 items of 25 documents
Tree Species Identification Using 3D Spectral Data and 3D Convolutional Neural Network
2018
In this study we apply 3D convolutional neural network (CNN) for tree species identification. Study includes the three most common Finnish tree species. Study uses a relatively large high-resolution spectral data set, which contains also a digital surface model for the trees. Data has been gathered using an unmanned aerial vehicle, a framing hyperspectral imager and a regular RGB camera. Achieved classification results are promising by with overall accuracy of 96.2 % for the classification of the validation data set. nonPeerReviewed
Hyperspectral Imaging of Macroinvertebrates : a Pilot Study for Detecting Metal Contamination in Aquatic Ecosystems
2018
The applicability of spectral analysis in detection of freshwater metal contamination was assessed by developing and testing a novel hyperspectral imaging (HSI) application for aquatic insect larvae (Trichoptera: Hydropsychidae). Larvae were first exposed to four different cadmium (Cd) concentrations: 0, 1, 10, and 100 μg L−1 for 96 h. Individual larvae were then preserved in ethanol, inspected with microscopy for the number of anomalies in larval gills, and imaged by hyperspectral camera operating with wavebands between 500 and 850 nm. Three additional larvae from each exposure were analyzed for tissue Cd concentration. Although the larval tissue Cd concentrations correlated positively wit…
Comparison of Machine Learning Methods in Stochastic Skin Optical Model Inversion
2020
In this study, we compare six different machine learning methods in the inversion of a stochastic model for light propagation in layered media, and use the inverse models to estimate four parameters of the skin from the simulated data: melanin concentration, hemoglobin volume fraction, and thicknesses of epidermis and dermis. The aim of this study is to determine the best methods for stochastic model inversion in order to improve current methods in skin related cancer diagnostics and in the future develop a non-invasive way to measure the physical parameters of the skin based partially on the results of the study. Of the compared methods, which are convolutional neural network, multi-layer …
Convolutional neural networks in skin cancer detection using spatial and spectral domain
2019
Skin cancers are world wide deathly health problem, where significant life and cost savings could be achieved if detection of cancer can be done in early phase. Hypespectral imaging is prominent tool for non-invasive screening. In this study we compare how use of both spectral and spatial domain increase classification performance of convolutional neural networks. We compare five different neural network architectures for real patient data. Our models gain same or slightly better positive predictive value as clinicians. Towards more general and reliable model more data is needed and collection of training data should be systematic. peerReviewed
The hyperspectral and smartphone technology in CBRNE countermeasures and defence
2016
Caused by industrial and military use as well as other sources of chemical, biological, radiological, nuclear and high-yield explosive (CBRNE) materials, the global threat of weapons of mass destruction (WMDs) remains in spite of such weapons being internationally prohibited. With these materials, industrial and transportation accidents are likely in all countries and can also be triggered by natural disasters, such as in Fukushima in 2011. In addition, governments cannot fully control the manufacturing and usage of WMDs, as extreme terrorists have access to as well as the knowledge and motivation to use such materials. Due to multiple large-scale risks, the countering of CBRNE threats requ…