Camouflaged Target Detection Based on Snapshot Multispectral Imaging

The spectral information contained in the hyperspectral images (HSI) distinguishes the intrinsic properties of a target from the background, which is widely used in remote sensing. However, the low imaging speed and high data redundancy caused by the high spectral resolution of imaging spectrometers limit their application in scenarios with the real-time requirement. In this work, we achieve the precise detection of camouflaged targets based on snapshot multispectral imaging technology and band selection methods in urban-related scenes. Specifically, the camouflaged target detection algorithm combines the constrained energy minimization (CEM) algorithm and the improved maximum between-class variance (OTSU) algorithm (t-OTSU), which is proposed to obtain the initial target detection results and adaptively segment the target region. Moreover, an object region extraction (ORE) algorithm is proposed to obtain a complete target contour that improves the target detection capability of multispectral images (MSI). The experimental results show that the proposed algorithm has the ability to detect different camouflaged targets by using only four bands. The detection accuracy is above 99%, and the false alarm rate is below 0.2%. The research achieves the effective detection of camouflaged targets and has the potential to provide a new means for real-time multispectral sensing in complex scenes.

Multispectral Sensing and Data Integration for the Study of Heritage Architecture

The recording and processing of terrestrial multispectral information can have significant value for built heritage studies. The efficient adoption of active and passive sensing techniques operating at multiple wavelengths and the integrated analyses of the produced data is essential for enhanced observation of historical architecture, especially for the implementation of rapid non-destructive surveys, which can provide an overall assessment of the state-of-preservation of a historical structure to indicate areas of interest for more detailed diagnostics. Based on this rationale, the presented work aims at providing methods for prompt recording, fusion, and integrated visual analysis of two-dimensional multispectral results to study architectural heritage. Spectral images—captured with a modified digital camera—thermograms, photogrammetrically produced orthophoto-maps, and spatial raster data produced from point clouds are integrated and analyzed. The results are evaluated within the scope of studying building materials, deterioration patterns, and hidden defects, towards the employment of advanced geomatics approaches to monitor built heritage effectively.

Multispectral sensing of biological liquids with hollow-core microstructured optical fibres

The state of the art in optical biosensing is focused on reaching high sensitivity at a single wavelength by using any type of optical resonance. This common strategy, however, disregards the promising possibility of simultaneous measurements of a bioanalyte’s refractive index over a broadband spectral domain. Here, we address this issue by introducing the approach of in-fibre multispectral optical sensing (IMOS). The operating principle relies on detecting changes in the transmission of a hollow-core microstructured optical fibre when a bioanalyte is streamed through it via liquid cells. IMOS offers a unique opportunity to measure the refractive index at 42 wavelengths, with a sensitivity up to ~3000 nm per refractive index unit (RIU) and a figure of merit reaching 99 RIU−1 in the visible and near-infra-red spectral ranges. We apply this technique to determine the concentration and refractive index dispersion for bovine serum albumin and show that the accuracy meets clinical needs.

Hyperspectral Classification of Cyperus esculentus Clones and Morphologically Similar Weeds

Cyperus esculentus (yellow nutsedge) is one of the world’s worst weeds as it can cause great damage to crops and crop production. To eradicate C. esculentus, early detection is key—a challenging task as it is often confused with other Cyperaceae and displays wide genetic variability. In this study, the objective was to classify C. esculentus clones and morphologically similar weeds. Hyperspectral reflectance between 500 and 800 nm was tested as a measure to discriminate between (I) C. esculentus and morphologically similar Cyperaceae weeds, and between (II) different clonal populations of C. esculentus using three classification models: random forest (RF), regularized logistic regression (RLR) and partial least squares–discriminant analysis (PLS–DA). RLR performed better than RF and PLS–DA, and was able to adequately classify the samples. The possibility of creating an affordable multispectral sensing tool, for precise in-field recognition of C. esculentus plants based on fewer spectral bands, was tested. Results of this study were compared against simulated results from a commercially available multispectral camera with four spectral bands. The model created with customized bands performed almost equally well as the original PLS–DA or RLR model, and much better than the model describing multispectral image data from a commercially available camera. These results open up the opportunity to develop a dedicated robust tool for C. esculentus recognition based on four spectral bands and an appropriate classification model.

Experiment of Multispectral Sensing Sensor for Urban Road Materials in Outdoor Environment

This research is the first attempt to conduct several experiments of multispectralsensing sensor for urban road materials in outdoor environment. This research aims to classifyfive urban road materials that are aggregates, asphalts, concrete, clay, natural fibre includingvegetation and water. There were 9 cameras in the multispectral sensing sensor. Seven cameraattached with narrow band optical filter with the centre spectrum at 710nm, 730nm, 750nm,800nm, 870nm, 905nm and 950nm. One camera attached with 720 nm normalization band useshigh pass optical filter. Another camera attached with UV/IR cut optical filter works as a RGBcamera. The images results, that have been taken, are processed in MATLAB to get the imagingindex results from the multispectral system. Naïve Bayes classifier is used in Weka to classifythe urban road materials with vegetation and water. The first classification and testing thatclassifies five urban road materials with vegetation and water have accuracy results ranged from0 % to 32% while the accuracy results without vegetation and water have better accuracy resultsranged from 0 % to 55 %.