Ref lectance Spectroscopy

Balas, Costas; Pappas, Christos; Epitropou, George

doi:10.1201/b10951-10

Cited by 13 publications

(13 citation statements)

References 1 publication

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“…Based on the development of sensor technology and remote sensing platforms, more accurate and timely satellite images with high spectral and spatial resolutions, which have a positive impact on identification accuracy [24], can be easily acquired. Unmanned aerial vehicles (UAVs), as emerging unmanned aircraft systems, are increasingly being used as remote sensing platforms [25].…”

Section: Introductionmentioning

confidence: 99%

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

et al. 2018

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Mangroves are one of the most important coastal wetland ecosystems, and the compositions and distributions of mangrove species are essential for conservation and restoration efforts. Many studies have explored this topic using remote sensing images that were obtained by satellite-borne and airborne sensors, which are known to be efficient for monitoring the mangrove ecosystem. With improvements in carrier platforms and sensor technology, unmanned aerial vehicles (UAVs) with high-resolution hyperspectral images in both spectral and spatial domains have been used to monitor crops, forests, and other landscapes of interest. This study aims to classify mangrove species on Qi'ao Island using object-based image analysis techniques based on UAV hyperspectral images obtained from a commercial hyperspectral imaging sensor (UHD 185) onboard a UAV platform. First, the image objects were obtained by segmenting the UAV hyperspectral image and the UAV-derived digital surface model (DSM) data. Second, spectral features, textural features, and vegetation indices (VIs) were extracted from the UAV hyperspectral image, and the UAV-derived DSM data were used to extract height information. Third, the classification and regression tree (CART) method was used to selection bands, and the correlation-based feature selection (CFS) algorithm was employed for feature reduction. Finally, the objects were classified into different mangrove species and other land covers based on their spectral and spatial characteristic differences. The classification results showed that when considering the three features (spectral features, textural features, and hyperspectral VIs), the overall classification accuracies of the two classifiers used in this paper, i.e., k-nearest neighbor (KNN) and support vector machine (SVM), were 76.12% (Kappa = 0.73) and 82.39% (Kappa = 0.801), respectively. After incorporating tree height into the classification features, the accuracy of species classification increased, and the overall classification accuracies of KNN and SVM reached 82.09% (Kappa = 0.797) and 88.66% (Kappa = 0.871), respectively. It is clear that SVM outperformed KNN for mangrove species classification. These results also suggest that height information is effective for discriminating mangrove species with similar spectral signatures, but different heights. In addition, the classification accuracy and performance of SVM can be further improved by feature reduction. The overall results provided evidence for the effectiveness and potential of UAV hyperspectral data for mangrove species identification.

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Section: Introductionmentioning

confidence: 99%

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

et al. 2018

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“…The acoustic waves change the refractive index of the crystal by compressing and relaxing the crystal lattice. The variations of the refractive index make the crystal work like a transmission diffraction grating …”

Section: Basic Principlesmentioning

confidence: 99%

“…Birefringence of the liquid crystal would change with the electric field. Using different power electrical fields, pass‐band of the total filter may be adjusted …”

Section: Basic Principlesmentioning

confidence: 99%

A new manufacturable filter design approach for spectral reflectance estimation

Nahavandi

Tehran

2016

Color Research & Application

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Selection of the best possible filter set among a set of available filters is the obvious method of increasing dimension of camera signals for spectral reflectance reconstruction. There are also methods that are focusing on the filter design regardless of noticing to the constructability of the designed filters. This study shows that direct optimization of physical variables of filter manufacturing technique is more reliable than indirect approach of designing and then physical manufacturing of the designed filters. Direct optimization of the transmission‐controlling primaries in filter manufacturing process would guarantee having the designed filters in reality. Combination of some solvent dyes was used as the spectral transmission matching system for filter manufacturing. As a conventional technique, filters were designed and best possible dye concentrations that match the desired filters were calculated. As an alternative approach, filters were also designed using direct optimization of dyes concentrations. The results showed that direct optimization of dye concentrations exhibits better performance in comparison with the conventional technique. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 316–326, 2017

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“…It collects a "stack" of images, each one corresponding to a specific wavelength, the so called "spectral cube". A complete spectrum can be calculated for every image pixel, which may be represented as a vector in a multidimensional "spectral space [1]. SI systems are used in a wide field of applications such as biomedical sciences [2], remote sensing, Art preservation etc.…”

Section: Introductionmentioning

confidence: 99%

“…The so called snapshot SI systems allow for the simultaneous acquisition of the Spectral Cube during a single sensor's exposure. A variety of designs have been presented so far with different degrees of sophistication, ranging from the ones that are based on an array of filtered imaging sensors to those based on codedaperture/compressed-sensing principles [1] [5]. Although these systems permit the instantaneous acquisition of a complete spectral cube, long lasting post-processing procedures, such as image registration tasks or computational intensive steps of decoding the multiplexed spectral cube, render them unable to perform SI in real time.…”

Section: Introductionmentioning

confidence: 99%

Artificial spectral vision for studying the spectral, the spatial and the temporal characteristics of dynamic optical phenomena in biomedicine

Iliou

Epitropou

Kavvadias

et al. 2014

2014 IEEE International Conference on Imaging Systems and Techniques (IST) Proceedings

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In this paper we present a novel snapshot spectral imager with the distinct advantage of being able to display maps representing different spectral classes in a scene in video rates. The Simultaneous capturing of spectral images through a common aperture is combined with spectral estimation algorithms for improving spectral resolution at no cost to the spatial resolution. The developed real time spectral mapper (RTSM) is validated in a pilot study as a blood oxygen saturation mapper, where its great value in studying the spatiotemporal characteristics of dynamic optical effects was clearly established.

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Ref lectance Spectroscopy

Cited by 13 publications

References 1 publication

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

A new manufacturable filter design approach for spectral reflectance estimation

Artificial spectral vision for studying the spectral, the spatial and the temporal characteristics of dynamic optical phenomena in biomedicine

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