Search results for "hyperspectral"

Unmixing methods in novel applications of spectral imaging

2014

Angular Dependency of Hyperspectral Measurements over Wheat Characterized by a Novel UAV Based Goniometer

2015

In this study we present a hyperspectral flying goniometer system, based on a rotary-wing unmanned aerial vehicle (UAV) equipped with a spectrometer mounted on an active gimbal. We show that this approach may be used to collect multiangular hyperspectral data over vegetated environments. The pointing and positioning accuracy are assessed using structure from motion and vary from σ = 1° to 8° in pointing and σ = 0.7 to 0.8 m in positioning. We use a wheat dataset to investigate the influence of angular effects on the NDVI, TCARI and REIP vegetation indices. Angular effects caused significant variations on the indices: NDVI = 0.83–0.95; TCARI = 0.04–0.116; REIP = 729–735 nm. Our analysis high…

FPI Based Hyperspectral Imager for the Complex Surfaces : Calibration, Illumination and Applications

2022

Hyperspectral imaging (HSI) applications for biomedical imaging and dermatological applications have been recently under research interest. Medical HSI applications are non-invasive methods with high spatial and spectral resolution. HS imaging can be used to delineate malignant tumours, detect invasions, and classify lesion types. Typical challenges of these applications relate to complex skin surfaces, leaving some skin areas unreachable. In this study, we introduce a novel spectral imaging concept and conduct a clinical pre-test, the findings of which can be used to develop the concept towards a clinical application. The SICSURFIS spectral imager concept combines a piezo-actuated Fabry–Pé…

Discovering knowledge in various applications with a novel hyperspectral imager

2013

Hyperspectral imaging system in the delineation of Ill-defined basal cell carcinomas : a pilot study

2019

Background Basal cell carcinoma (BCC) is the most common skin cancer in the Caucasian population. Eighty per cent of BCCs are located on the head and neck area. Clinically ill‐defined BCCs often represent histologically aggressive subtypes, and they can have subtle subclinical extensions leading to recurrence and the need for re‐excisions. Objectives The aim of this pilot study was to test the feasibility of a hyperspectral imaging system (HIS) in vivo in delineating the preoperatively lateral margins of ill‐defined BCCs on the head and neck area. Methods Ill‐defined BCCs were assessed clinically with a dermatoscope, photographed and imaged with HIS. This was followed by surgical procedures…

UAS BASED TREE SPECIES IDENTIFICATION USING THE NOVEL FPI BASED HYPERSPECTRAL CAMERAS IN VISIBLE, NIR AND SWIR SPECTRAL RANGES

2016

Abstract. Unmanned airborne systems (UAS) based remote sensing offers flexible tool for environmental monitoring. Novel lightweight Fabry-Perot interferometer (FPI) based, frame format, hyperspectral imaging in the spectral range from 400 to 1600 nm was used for identifying different species of trees in a forest area. To the best of the authors’ knowledge, this was the first research where stereoscopic, hyperspectral VIS, NIR, SWIR data is collected for tree species identification using UAS. The first results of the analysis based on fusion of two FPI-based hyperspectral imagers and RGB camera showed that the novel FPI hyperspectral technology provided accurate geometric, radiometric and sp…

HYPERSPECTRAL REFLECTANCE SIGNATURES AND POINT CLOUDS FOR PRECISION AGRICULTURE BY LIGHT WEIGHT UAV IMAGING SYSTEM

2018

Abstract. The objective of this investigation was to study the use of a new type of a low-weight unmanned aerial vehicle (UAV) imaging system in the precision agriculture. The system consists of a novel Fabry-Perot interferometer based hyperspectral camera and a high-resolution small-format consumer camera. The sensors provide stereoscopic imagery in a 2D frame-format and they both weigh less than 500 g. A processing chain was developed for the production of high density point clouds and hyperspectral reflectance image mosaics (reflectance signatures), which are used as inputs in the agricultural application. We demonstrate the use of this new technology in the biomass estimation process, w…

VARIABILITY OF REMOTE SENSING SPECTRAL INDICES IN BOREAL LAKE BASINS

2018

Remotely sensed hyperspectral data has widely been used to determine water quality parameters in oceanic waters. However in freshwater basins the dependence between the hyperspectral data and the parameters is more complicated. In this work some ideas are presented concerning the study of this dependence. The data used in this study were collected from the lake Hiidenvesi in southern Finland. The hyperspectral data consists of reflectances in 36 bands in the wavelength area 508…878 nm and the separately measured water quality parameters are turbidity, blue-green algae, chlorophyll, pH and dissolved oxygen. Hyperspectral data was used as bare band reflectances, but also in the …

CHOOSING OF OPTIMAL REFERENCE SAMPLES FOR BOREAL LAKE CHLOROPHYLL A CONCENTRATION MODELING USING AERIAL HYPERSPECTRAL DATA

2018

Abstract. Optical remote sensing has potential to overcome the limitations of point estimations of lake water quality by providing spatial and temporal information. In open ocean waters the optical properties are dominated by phytoplankton density, while the relationship between color and the constituents is more complicated in inland waters varying regionally and seasonally. Concerning the difficulties relating to comprehensive modeling of complex inland and coastal waters, the alternative approach is considered in this paper: the raw digital numbers (DN) recorded using aerial remote hyperspectral sensing are used without corrections and derived by means of regression modeling to predict C…

Minimal learning machine in anomaly detection from hyperspectral images

2020

Abstract. Anomaly detection from hyperspectral data needs computationally efficient methods to process the data when the data gathering platform is a drone or a cube satellite. In this study, we introduce a minimal learning machine for hyperspectral anomaly detection. Minimal learning machine is a novel distance-based classification algorithm, which is now modified to detect anomalies. Besides being computationally efficient, minimal learning machine is also easy to implement. Based on the results, we show that minimal learning machine is efficient in detecting global anomalies from the hyperspectral data with low false alarm rate.