Статті в журналах: "Hyperspectral signature"

1

Papp, Adam, Julian Pegoraro, Daniel Bauer, Philip Taupe, Christoph Wiesmeyr, and Andreas Kriechbaum-Zabini. "Automatic Annotation of Hyperspectral Images and Spectral Signal Classification of People and Vehicles in Areas of Dense Vegetation with Deep Learning." Remote Sensing 12, no. 13 (July 1, 2020): 2111. http://dx.doi.org/10.3390/rs12132111.

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Despite recent advances in image and video processing, the detection of people or cars in areas of dense vegetation is still challenging due to landscape, illumination changes and strong occlusion. In this paper, we address this problem with the use of a hyperspectral camera—installed on the ground or possibly a drone—and detection based on spectral signatures. We introduce a novel automatic method for annotating spectral signatures based on a combination of state-of-the-art deep learning methods. After we collected millions of samples with our method, we used a deep learning approach to train a classifier to detect people and cars. Our results show that, based only on spectral signature classification, we can achieve an Matthews Correlation Coefficient of 0.83. We evaluate our classification method in areas with varying vegetation and discuss the limitations and constraints that the current hyperspectral imaging technology has. We conclude that spectral signature classification is possible with high accuracy in uncontrolled outdoor environments. Nevertheless, even with state-of-the-art compact passive hyperspectral imaging technology, high dynamic range of illumination and relatively low image resolution continue to pose major challenges when developing object detection algorithms for areas of dense vegetation.

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2

Wang, Jing. "Progressive coding for hyperspectral signature characterization." Optical Engineering 45, no. 9 (September 1, 2006): 097002. http://dx.doi.org/10.1117/1.2353113.

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Gromov, V. P., L. I. Lebedev, and V. E. Turlapov. "Analysis and object markup of hyperspectral images for machine learning methods." Information Technology and Nanotechnology, no. 2391 (2019): 309–17. http://dx.doi.org/10.18287/1613-0073-2019-2391-309-317.

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The development of the nominal sequence of steps for analyzing the HSI proposed by Landgrebe, which is necessary in the context of the appearance of reference signature libraries for environmental monitoring, is discussed. The approach is based on considering the HSI pixel as a signature that stores all spectral features of an object and its states, and the HSI as a whole - as a two-dimensional signature field. As a first step of the analysis, a procedure is proposed for detecting a linear dependence of signatures by the magnitude of the Pearson correlation coefficient. The main apparatus of analysis, as in Landgrebe sequence, is the method of principal component analysis, but it is no longer used to build classes and is applied to investigate the presence in the class of subclasses essential for the applied area. The experimental material includes such objects as water, swamps, soil, vegetation, concrete, pollution. Selection of object samples on the image is made by the user. From the studied images of HSI objects, a base of reference signatures for classes (subclasses) of objects is formed, which in turn can be used to automate HSI markup with the aim of applying machine learning methods to recognize HSI objects and their states.

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4

Hartfield, Kyle, Jeffrey K. Gillan, Cynthia L. Norton, Charles Conley, and Willem J. D. van Leeuwen. "A Novel Spectral Index to Identify Cacti in the Sonoran Desert at Multiple Scales Using Multi-Sensor Hyperspectral Data Acquisitions." Land 11, no. 6 (May 26, 2022): 786. http://dx.doi.org/10.3390/land11060786.

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Accurate identification of cacti, whether seen as an indicator of ecosystem health or an invasive menace, is important. Technological improvements in hyperspectral remote sensing systems with high spatial resolutions make it possible to now monitor cacti around the world. Cacti produce a unique spectral signature because of their morphological and anatomical characteristics. We demonstrate in this paper that we can leverage a reflectance dip around 972 nm, due to cacti’s morphological structure, to distinguish cacti vegetation from non-cacti vegetation in a desert landscape. We also show the ability to calculate two normalized vegetation indices that highlight cacti. Furthermore, we explore the impacts of spatial resolution by presenting spectral signatures from cacti samples taken with a handheld field spectroradiometer, drone-based hyperspectral sensor, and aerial hyperspectral sensor. These cacti indices will help measure baseline levels of cacti around the world and examine changes due to climate, disturbance, and management influences.

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Chang, Chein-I., Sumit Chakravarty, Hsian-Min Chen, and Yen-Chieh Ouyang. "Spectral derivative feature coding for hyperspectral signature analysis." Pattern Recognition 42, no. 3 (March 2009): 395–408. http://dx.doi.org/10.1016/j.patcog.2008.07.016.

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6

Kim, Sungho, Jungho Kim, Jinyong Lee, and Junmo Ahn. "AS-CRI: A New Metric of FTIR-Based Apparent Spectral-Contrast Radiant Intensity for Remote Thermal Signature Analysis." Remote Sensing 11, no. 7 (April 1, 2019): 777. http://dx.doi.org/10.3390/rs11070777.

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Infrared signature analysis that considers both the target and background is fundamentally important to the development of target detection systems as well as in the design of ships for thermal stealth. This paper presents the analysis results of long-term infrared signature variations in terms of the apparent spectral-contrast radiant intensity measured using Fourier transform infrared (FTIR)-based hyperspectral images. A novel apparent spectral-contrast radiant intensity (AS-CRI) measure is proposed to evaluate the spectral infrared signature accurately at the sensor point of view. The spectral information by AS-CRI can provide the optimal band for either target detection or thermal stealth purposes, considering the background and atmospheric transmittance. In addition, the effects of seasonal and weather variations were analyzed from the long-term hyperspectral image database constructed during 2018.01–2018.08 (three times a day). A TELOPS HYPER-CAM MWE camera was adopted to acquire 374 bands in 1.5–5.5 μm. The automatic weather system (AWS) can provide 24 h weather recordings for the signature evaluation. The experimental results validate the utility of the novel AS-CRI method to find spectral bands for a range of infrared signature applications including small infrared target detection.

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7

MESSINGER, DAVID W., CARL SALVAGGIO, and NATALIE M. SINISGALLI. "DETECTION OF GASEOUS EFFLUENTS FROM AIRBORNE LWIR HYPERSPECTRAL IMAGERY USING PHYSICS-BASED SIGNATURES." International Journal of High Speed Electronics and Systems 17, no. 04 (December 2007): 801–12. http://dx.doi.org/10.1142/s0129156407004990.

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Detection of gaseous effluent plumes from airborne platforms provides a unique challenge to the remote sensing community. The measured signatures are a complicated combination of phenomenology including effects of the atmosphere, spectral characteristics of the background material under the plume, temperature contrast between the gas and the surface, and the concentration of the gas. All of these quantities vary spatially further complicating the detection problem. In complex scenes simple estimation of a “residual” spectrum may not be possible due to the variability in the scene background. A common detection scheme uses a matched filter formalism to compare laboratory-measured gas absorption spectra with measured pixel radiances. This methodology can not account for the variable signature strengths due to concentration path length and temperature contrast, nor does it take into account measured signatures that are observed in both absorption and emission in the same scene. We have developed a physics-based, forward model to predict in-scene signatures covering a wide range in gas / surface properties. This target space is reduced to a set of basis vectors using a geometrical model of the space. Corresponding background basis vectors are derived to describe the non-plume pixels in the image. A Generalized Likelihood Ratio Test is then used to discriminate between plume and non-plume pixels. Several species can be tested for iteratively. The algorithm is applied to airborne LWIR hyperspectral imagery collected by the Airborne Hyperspectral Imager (AHI) over a chemical facility with some ground truth. When compared to results from a clutter matched filter the physics-based signature approach shows significantly improved performance for the data set considered here.

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8

Honkavaara, E., T. Hakala, O. Nevalainen, N. Viljanen, T. Rosnell, E. Khoramshahi, R. Näsi, R. Oliveira, and A. Tommaselli. "GEOMETRIC AND REFLECTANCE SIGNATURE CHARACTERIZATION OF COMPLEX CANOPIES USING HYPERSPECTRAL STEREOSCOPIC IMAGES FROM UAV AND TERRESTRIAL PLATFORMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 17, 2016): 77–82. http://dx.doi.org/10.5194/isprs-archives-xli-b7-77-2016.

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Light-weight hyperspectral frame cameras represent novel developments in remote sensing technology. With frame camera technology, when capturing images with stereoscopic overlaps, it is possible to derive 3D hyperspectral reflectance information and 3D geometric data of targets of interest, which enables detailed geometric and radiometric characterization of the object. These technologies are expected to provide efficient tools in various environmental remote sensing applications, such as canopy classification, canopy stress analysis, precision agriculture, and urban material classification. Furthermore, these data sets enable advanced quantitative, physical based retrieval of biophysical and biochemical parameters by model inversion technologies. Objective of this investigation was to study the aspects of capturing hyperspectral reflectance data from unmanned airborne vehicle (UAV) and terrestrial platform with novel hyperspectral frame cameras in complex, forested environment.

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9

Honkavaara, E., T. Hakala, O. Nevalainen, N. Viljanen, T. Rosnell, E. Khoramshahi, R. Näsi, R. Oliveira, and A. Tommaselli. "GEOMETRIC AND REFLECTANCE SIGNATURE CHARACTERIZATION OF COMPLEX CANOPIES USING HYPERSPECTRAL STEREOSCOPIC IMAGES FROM UAV AND TERRESTRIAL PLATFORMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 17, 2016): 77–82. http://dx.doi.org/10.5194/isprsarchives-xli-b7-77-2016.

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Light-weight hyperspectral frame cameras represent novel developments in remote sensing technology. With frame camera technology, when capturing images with stereoscopic overlaps, it is possible to derive 3D hyperspectral reflectance information and 3D geometric data of targets of interest, which enables detailed geometric and radiometric characterization of the object. These technologies are expected to provide efficient tools in various environmental remote sensing applications, such as canopy classification, canopy stress analysis, precision agriculture, and urban material classification. Furthermore, these data sets enable advanced quantitative, physical based retrieval of biophysical and biochemical parameters by model inversion technologies. Objective of this investigation was to study the aspects of capturing hyperspectral reflectance data from unmanned airborne vehicle (UAV) and terrestrial platform with novel hyperspectral frame cameras in complex, forested environment.

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10

Miljković, V., and D. Gajski. "ADAPTATION OF INDUSTRIAL HYPERSPECTRAL LINE SCANNER FOR ARCHAEOLOGICAL APPLICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 15, 2016): 343–45. http://dx.doi.org/10.5194/isprs-archives-xli-b5-343-2016.

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The spectral characteristic of the visible light reflected from any of archaeological artefact is the result of the interaction of its surface illuminated by incident light. Every particular surface depends on what material it is made of and/or which layers put on it has its spectral signature. Recent archaeometry recognises this information as very valuable data to extend present documentation of artefacts and as a new source for scientific exploration. However, the problem is having an appropriate hyperspectral imaging system available and adopted for applications in archaeology. In this paper, we present the new construction of the hyperspectral imaging system, made of industrial hyperspectral line scanner ImSpector V9 and CCD-sensor PixelView. The hyperspectral line scanner is calibrated geometrically, and hyperspectral data are geocoded and converted to the hyperspectral cube. The system abilities are evaluated for various archaeological artefacts made of different materials. Our experience in applications, visualisations, and interpretations of collected hyperspectral data are explored and presented.

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11

Miljković, V., and D. Gajski. "ADAPTATION OF INDUSTRIAL HYPERSPECTRAL LINE SCANNER FOR ARCHAEOLOGICAL APPLICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 15, 2016): 343–45. http://dx.doi.org/10.5194/isprsarchives-xli-b5-343-2016.

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The spectral characteristic of the visible light reflected from any of archaeological artefact is the result of the interaction of its surface illuminated by incident light. Every particular surface depends on what material it is made of and/or which layers put on it has its spectral signature. Recent archaeometry recognises this information as very valuable data to extend present documentation of artefacts and as a new source for scientific exploration. However, the problem is having an appropriate hyperspectral imaging system available and adopted for applications in archaeology. In this paper, we present the new construction of the hyperspectral imaging system, made of industrial hyperspectral line scanner ImSpector V9 and CCD-sensor PixelView. The hyperspectral line scanner is calibrated geometrically, and hyperspectral data are geocoded and converted to the hyperspectral cube. The system abilities are evaluated for various archaeological artefacts made of different materials. Our experience in applications, visualisations, and interpretations of collected hyperspectral data are explored and presented.

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12

Urbina Ortega, Carlos, Eduardo Quevedo Gutiérrez, Laura Quintana, Samuel Ortega, Himar Fabelo, Lucana Santos Falcón, and Gustavo Marrero Callico. "Towards Real-Time Hyperspectral Multi-Image Super-Resolution Reconstruction Applied to Histological Samples." Sensors 23, no. 4 (February 7, 2023): 1863. http://dx.doi.org/10.3390/s23041863.

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Hyperspectral Imaging (HSI) is increasingly adopted in medical applications for the usefulness of understanding the spectral signature of specific organic and non-organic elements. The acquisition of such images is a complex task, and the commercial sensors that can measure such images is scarce down to the point that some of them have limited spatial resolution in the bands of interest. This work proposes an approach to enhance the spatial resolution of hyperspectral histology samples using super-resolution. As the data volume associated to HSI has always been an inconvenience for the image processing in practical terms, this work proposes a relatively low computationally intensive algorithm. Using multiple images of the same scene taken in a controlled environment (hyperspectral microscopic system) with sub-pixel shifts between them, the proposed algorithm can effectively enhance the spatial resolution of the sensor while maintaining the spectral signature of the pixels, competing in performance with other state-of-the-art super-resolution techniques, and paving the way towards its use in real-time applications.

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13

Khoshsokhan, S., R. Rajabi, and H. Zayyani. "DISTRIBUTED UNMIXING OF HYPERSPECTRAL DATAWITH SPARSITY CONSTRAINT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 26, 2017): 145–50. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-145-2017.

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Spectral unmixing (SU) is a data processing problem in hyperspectral remote sensing. The significant challenge in the SU problem is how to identify endmembers and their weights, accurately. For estimation of signature and fractional abundance matrices in a blind problem, nonnegative matrix factorization (NMF) and its developments are used widely in the SU problem. One of the constraints which was added to NMF is sparsity constraint that was regularized by L1/2 norm. In this paper, a new algorithm based on distributed optimization has been used for spectral unmixing. In the proposed algorithm, a network including single-node clusters has been employed. Each pixel in hyperspectral images considered as a node in this network. The distributed unmixing with sparsity constraint has been optimized with diffusion LMS strategy, and then the update equations for fractional abundance and signature matrices are obtained. Simulation results based on defined performance metrics, illustrate advantage of the proposed algorithm in spectral unmixing of hyperspectral data compared with other methods. The results show that the AAD and SAD of the proposed approach are improved respectively about 6 and 27 percent toward distributed unmixing in SNR=25dB.

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14

Pervez, W., S. A. Khan, and Valiuddin. "HYPERSPECTRAL HYPERION IMAGERY ANALYSIS AND ITS APPLICATION USING SPECTRAL ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W2 (March 10, 2015): 169–75. http://dx.doi.org/10.5194/isprsarchives-xl-3-w2-169-2015.

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Rapid advancement in remote sensing open new avenues to explore the hyperspectral Hyperion imagery pre-processing techniques, analysis and application for land use mapping. The hyperspectral data consists of 242 bands out of which 196 calibrated/useful bands are available for hyperspectral applications. Atmospheric correction applied to the hyperspectral calibrated bands make the data more useful for its further processing/ application. Principal component (PC) analysis applied to the hyperspectral calibrated bands reduced the dimensionality of the data and it is found that 99% of the data is held in first 10 PCs. Feature extraction is one of the important application by using vegetation delineation and normalized difference vegetation index. The machine learning classifiers uses the technique to identify the pixels having significant difference in the spectral signature which is very useful for classification of an image. Supervised machine learning classifier technique has been used for classification of hyperspectral image which resulted in overall efficiency of 86.6703 and Kappa co-efficient of 0.7998.

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15

Chein-I Chang. "Target signature-constrained mixed pixel classification for hyperspectral imagery." IEEE Transactions on Geoscience and Remote Sensing 40, no. 5 (May 2002): 1065–81. http://dx.doi.org/10.1109/tgrs.2002.1010894.

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16

Wang, Su, Chuin-Mu Wang, Mann-Li Chang, Ching-Tsorng Tsai, and Chein-I. Chang. "Applications of Kalman Filtering to Single Hyperspectral Signature Analysis." IEEE Sensors Journal 10, no. 3 (March 2010): 547–63. http://dx.doi.org/10.1109/jsen.2009.2038546.

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17

Licciardi, Giorgio, Costantino Del Gaudio, and Jocelyn Chanussot. "Non-Linear Spectral Unmixing for the Estimation of the Distribution of Graphene Oxide Deposition on 3D Printed Composites." Applied Sciences 10, no. 21 (November 3, 2020): 7792. http://dx.doi.org/10.3390/app10217792.

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Hyperspectral analysis is a well-established technique that can be suitably implemented in several application fields, including materials science. This approach allows us to deal with data samples containing spatial and spectral information at very high resolution, thus enabling us to evaluate materials properties at a nanoscale level. As a proof of concept, hyperspectral imaging was here considered to investigate 3D printed polymer matrix composites, considering graphene oxide (GO) as a nanofiller. Commercial polycaprolactone and polylactic acid filaments were firstly treated with GO to be then printed into testing specimens. Raman analysis was performed to assess the GO distribution on samples surface by mapping different regions of interest and the collected data were the input of a custom-made algorithm for hyperspectral image analysis, tailored to detect the GO signature. Findings showed a valuable matching to Raman maps and were also characterized by the positive feature of avoiding to set specific conditions to perform the investigation as GO Raman distribution was carried out by fixing the wavenumber at 1580 cm−1, which is representative of the G band of the nanofiller. This occurrence might lead to an uneven intensity representation related to possible peak shifts which can bias the acquired results. Differently, hyperspectral imaging needs a minimal set of data input, i.e., the spectral signatures of neat materials, to directly identify the searched nanomaterial. More in-depth investigations need to be performed to fully validate the proposed approach, but the here presented results already show the potential and versatility of hyperspectral analysis in the materials science field.

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18

El-Sharkawy, Yasser H., Sherif Elbasuney, Sara M. Radwan, Mostafa A. Askar, and Gharieb S. El-Sayyad. "Total RNA nonlinear polarization: towards facile early diagnosis of breast cancer." RSC Advances 11, no. 53 (2021): 33319–25. http://dx.doi.org/10.1039/d1ra05599b.

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Nonlinear polarization has been considered as a marvelous tool for several medical applications, and the capability to monitor any changes in RNA's spectral signature due to breast cancer was evaluated by hyperspectral camera.

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19

Srinivas, Umamahesh, Yi Chen, Vishal Monga, Nasser Nasrabadi, and Trac Tran. "Exploiting Sparsity in Hyperspectral Image Classification via Graphical Models." Geoscience and Remote Sensing Letters, IEEE 10, no. 3 (November 2012): 505–9. http://dx.doi.org/10.1109/lgrs.2012.2211858.

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A significant recent advance in hyperspectral image (HSI) classification relies on the observation that the spectral signature of a pixel can be represented by a sparse linear combination of training spectra from an overcomplete dictionary. A spatiospectral notion of sparsity is further captured by developing a joint sparsity model, wherein spectral signatures of pixels in a local spatial neighborhood (of the pixel of interest) are constrained to be represented by a common collection of training spectra, albeit with different weights. A challenging open problem is to effectively capture the class conditional correlations between these multiple sparse representations corresponding to different pixels in the spatial neighborhood. We propose a probabilistic graphical model framework to explicitly mine the conditional dependences between these distinct sparse features. Our graphical models are synthesized using simple tree structures which can be discriminatively learnt (even with limited training samples) for classification. Experiments on benchmark HSI data sets reveal significant improvements over existing approaches in classification rates as well as robustness to choice of training.

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20

SCHAUM, A. "ADVANCED HYPERSPECTRAL ALGORITHMS FOR TACTICAL TARGET DETECTION AND DISCRIMINATION." International Journal of High Speed Electronics and Systems 18, no. 03 (September 2008): 531–44. http://dx.doi.org/10.1142/s0129156408005540.

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Region-of-interest cueing by hyperspectral imaging systems for tactical reconnaissance has emphasized wide area coverage, low false alarm rates, and the search for manmade objects. Because they often appear embedded in complex environments and can exhibit large intrinsic spectral variability, these targets usually cannot be characterized by consistent signatures that might facilitate the detection process. Template matching techniques that focus on distinctive and persistent absorption features, such as those characterizing gases or liquids, prove ineffectual for most hard-body targets. High-performance autonomous detection requires instead the integration of limited and uncertain signature knowledge with a statistical approach. Effective techniques devised in this way using Gaussian models have transitioned to fielded systems. These first-generation algorithms are described here, along with heuristic modifications that have proven beneficial. Higher-performance Gaussian-based algorithms are also described, but sensitivity to parameter selection can prove problematical. Finally, a next-generation parameter-free non-Gaussian method is outlined whose performance compares favorably with the best Gaussian methods.

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21

Ma, Pengfei, Jiaoli Li, Ying Zhuo, Pu Jiao, and Genda Chen. "Coating Condition Detection and Assessment on the Steel Girder of a Bridge through Hyperspectral Imaging." Coatings 13, no. 6 (May 29, 2023): 1008. http://dx.doi.org/10.3390/coatings13061008.

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The organic coating of bridge steel girders is subjected to physical scratches, corrosion, and aging in natural weathering. The breakdown of the coating may cause serviceability and safety problems if left unnoticed. Conventional coating inspection is time-consuming and lacks information about the coating’s chemical integrity. A hyperspectral imaging method is proposed to detect the condition of steel coatings based on coating-responsive features in reflectance spectra. A field test was conducted on the real-world bridge, which shows obvious signs of degradation. The hyperspectral signature enables an assessment of the coating’s health and defect severity. The results indicated that the coating scratch can be effectively located in the domain of a hyperspectral image and the scratch depth can be determined by mapping a scratch depth indicator (SDI = R532 nm/R641 nm). Rust sources and products in steel girders can be identified by the unique spectral signatures in the VNIR range, and the rust stains (and thus stain areas) scattered on the coating can be pinpointed at pixel level by the chloride rust (CR) indicators >1.11 (CR = R733 nm/R841 nm). The chemical integrity of a topcoat is demonstrated by the short-wave infrared spectroscopy and the topcoat degradation can be evaluated by the decreased absorption at 8000 cm−1 and 5850 cm−1. Hyperspectral imaging enables faster and more reliable coating condition detection by the spectral features and provides an alternative for multi-object coating detection.

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Dhole, Pravin V., Vijay D. Dhangar, Sulochana D. Shejul, and Prof Bharti W. Gawali. "Machine Learning Approach for Spectral Signature Based Chemical Composition Analysis using Hyperspectral Data." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (October 31, 2023): 279–86. http://dx.doi.org/10.22214/ijraset.2023.55922.

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Abstract: Number of techniques to identify falsified medications, including thin layer chromatography (TLC), analytical methods, eye examination, and the use of sophisticated labs with strong equipment and technical specialists. Such techniques take more time and call for sophisticated labs, specialized specialists in that field, and sample preparation. This research uses hyperspectral data to develop a spectral pattern for identifying falsified medications. Near infrared spectroscopic techniques are commonly used for this task because of their many advantages. For this research, we used the wide spectral range ASD Field Spec4 Spectroradiometer (350-2500 nm). We chose 43 solid pharmaceutical medicines from various brands for our task. The tablet powder has been altered by adding various amounts of calcium carbonate (CaCO3) to imitate falsified medicines. Then, we gather the spectral signature throughout all stages of contamination and use machine learning categorization methods to assess it. Partial least squares regression has a remarkable accuracy rate of 98.9% for hyperspectral data with NIR spectroscopy, while logistic regression, support vector machines, and random forests all provide accuracy of 75%. This hyperspectral data coupled with a database of pharmaceutical spectra creates a practical field-testing method to identify fake medications that is quick, simple to use, and requires no technological expertise.

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Zhelezova, Sofia V., Elena V. Pakholkova, Vladislav E. Veller, Mikhail A. Voronov, Eugenia V. Stepanova, Alena D. Zhelezova, Anton V. Sonyushkin, Timur S. Zhuk, and Alexey P. Glinushkin. "Hyperspectral Non-Imaging Measurements and Perceptron Neural Network for Pre-Harvesting Assessment of Damage Degree Caused by Septoria/Stagonospora Blotch Diseases of Wheat." Agronomy 13, no. 4 (April 1, 2023): 1045. http://dx.doi.org/10.3390/agronomy13041045.

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The detection and identification of plant diseases is a fundamental task for sustainable crop production. Septoria tritici and Stagonospora nodorum blotch (STB and SNB) are two of the most common diseases of cereal crops that cause significant economic damage. Both pathogens are difficult to identify at early stages of infection. Determining the degree of the disease at a late infection stage is useful for assessing cereal crops before harvesting, as it allows the assessment of potential yield losses. Hyperspectral sensing could allow for automatic recognition of Septoria harmfulness on wheat in field conditions. In this research, we aimed to collect information on the hyperspectral data on wheat plants with different lesion degrees of STB&SNB and to create and train a neural network for the detection of lesions on leaves and ears caused by STB&SNB infection at the late stage of disease development. Spring wheat was artificially infected twice with Septoria pathogens in the stem elongation stage and in the heading stage. Hyperspectral reflections and brightness measurements were collected in the field on wheat leaves and ears on the 37th day after STB and the 30th day after SNB pathogen inoculation using an Ocean Insight “Flame” VIS-NIR hyperspectrometer. Obtained non-imaging data were pre-treated, and the perceptron model neural network (PNN) was created and trained based on a pairwise comparison of datasets for healthy and diseased plants. Both statistical and neural network approaches showed the high quality of the differentiation between healthy and damaged wheat plants by the hyperspectral signature. A comparison of the results of visual recognition and automatic STB&SNB estimation showed that the neural network was equally effective in the quality of the disease definition. The PNN, based on a neuron model of hyperspectral signature with a spectral step of 6 nm and 2000–4000 value datasets, showed a high quality of detection of the STB&SNB severity. There were 0.99 accuracy, 0.94 precision, 0.89 recall and 0.91 F-score metrics of the PNN model after 10,000 learning epochs. The estimation accuracy of diseased/healthy leaves ranged from 88.1 to 97.7% for different datasets. The accuracy of detection of a light and medium degree of disease was lower (38–66%). This method of non-imaging hyperspectral signature classification could be useful for the identification of the STB and SNB lesion degree identification in field conditions for pre-harvesting crop estimation.

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Che’Ya, Nik Norasma, Nur Adibah Mohidem, Nor Athirah Roslin, Mohammadmehdi Saberioon, Mohammad Zakri Tarmidi, Jasmin Arif Shah, Wan Fazilah Fazlil Ilahi, and Norsida Man. "Mobile Computing for Pest and Disease Management Using Spectral Signature Analysis: A Review." Agronomy 12, no. 4 (April 16, 2022): 967. http://dx.doi.org/10.3390/agronomy12040967.

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The demand for mobile applications in agriculture is increasing as smartphones are continuously developed and used for many purposes; one of them is managing pests and diseases in crops. Using mobile applications, farmers can detect early infection and improve the specified treatment and precautions to prevent further infection from occurring. Furthermore, farmers can communicate with agricultural authorities to manage their farm from home, and efficiently obtain information such as the spectral signature of crops. Therefore, the spectral signature can be used as a reference to detect pests and diseases with a hyperspectral sensor more efficiently than the conventional method, which takes more time to monitor the entire crop field. This review aims to show the current and future trends of mobile computing based on spectral signature analysis for pest and disease management. In this review, the use of mobile applications for pest and disease monitoring is evaluated based on image processing, the systems developed for pest and disease extraction, and the structure of steps outlined in developing a mobile application. Moreover, a comprehensive literature review on the utilisation of spectral signature analysis for pest and disease management is discussed. The spectral reflectance used in monitoring plant health and image processing for pest and disease diagnosis is mentioned. The review also elaborates on the integration of a spectral signature library within mobile application devices to obtain information about pests and disease in crop fields by extracting information from hyperspectral datasets. This review demonstrates the necessary scientific knowledge for visualising the spectral signature of pests and diseases using a mobile application, allowing this technology to be used in real-world agricultural settings.

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Jamaludin, Muhammad Ikhwan, Abdul Nasir Matori, Mohammad Faize Kholik, and Munirah Mohd Mokhtar. "Development Spectral Library of Vegetation Stress for Hydrocarbon Seepage." Applied Mechanics and Materials 567 (June 2014): 693–98. http://dx.doi.org/10.4028/www.scientific.net/amm.567.693.

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Spectral Library is data archive of spectral signatures of various natural and man-made materials. In oil and gas industry, spectral library might not be heard often, but these tools can pose a great help for future oil and gas exploration. A developing spectral library for hydrocarbon is basically a new advancement in this field, and this project may implement the spectral library on global hydrocarbon seeps in the future. In this paper, the procedure in the developing spectral library from vegetation stress was demonstrated. In order to obtain these spectral signatures of hydrocarbon, the usage of hyperspectral remote sensing analysis and spectroradiometer is required. But for the early stage of development, spectrophotometer with the range up to 740 ηm was used to extract the spectral signature of the plants in term of green percentage. Nine samples of palm oil trees represent the stressed vegetation was planted with 10% and 40% porosity of crude oil and control samples having three palm oil trees for each of those. The vegetation has been left in an open environment with enough sunlight and watered daily. The significance result of changes in green percentage in the spectral signature of the trees with different porosity level was compared to the control samples. It shows that the existence of crude oil influences the health of the vegetation which has been notified through the spectral signature of the plant. This paper signifies a first step towards the development of spectral signature of hydrocarbon for oil and gas exploration.

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Yang, Zhonglin, Yanhua Cao, Shutian Liu, Camel Tanougast, Walter Blondel, Zhengjun Liu, and Hang Chen. "A Novel Signature and Authentication Cryptosystem for Hyperspectral Image by Using Triangular Association Encryption Algorithm in Gyrator Domains." Applied Sciences 12, no. 15 (July 29, 2022): 7649. http://dx.doi.org/10.3390/app12157649.

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A novel optical signature and authentication cryptosystem is proposed by applying triangular association encryption algorithm (TAEA) and 3D Arnold transform in Gyrator domains. Firstly, a triangular association encryption algorithm (TAEA) is designed, which makes it possible to turn the diffusion of pixel values within bands into the diffusion within and between bands. Besides, the image signature function is considered and utilized in the proposed cryptosystem. Without the image signature, the original image cannot be restored even if all of the keys are obtained. Moreover, the image integrity authentication function is provided to prevent pixel values from being tampered with. Through the numerical simulation of various types of attacks, the effectiveness and capability of the proposed hyperspectral data signature and authentication cryptosystem is verified.

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27

Yang, Shuyuan, Hongjing Zhou, Min Wang, Zhixi Feng, and Licheng Jiao. "Fuzzy Signature-Based Discriminative Subspace Projection for Hyperspectral Data Classification." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, no. 9 (September 2016): 4196–202. http://dx.doi.org/10.1109/jstars.2015.2456102.

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28

Schaum, Alan. "Methods of Hyperspectral Detection Based on a Single Signature Sample." IEEE Sensors Journal 10, no. 3 (March 2010): 518–23. http://dx.doi.org/10.1109/jsen.2009.2038131.

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29

Lebedev, L. I., Yu V. Yasakov, T. H. Utesheva, V. P. Gromov, A. V. Borusjak, and V. E. Turlapov. "Complex analysis and monitoring of the environment based on earth sensing data." Computer Optics 43, no. 2 (April 2019): 282–95. http://dx.doi.org/10.18287/2412-6179-2019-43-2-282-295.

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We study a problem of complex analysis and monitoring of the environment based on Earth Sensing Data, with the emphasis on the use of hyperspectral images (HSI), and propose a solution based on developing algorithmic procedures for HSI processing and storage. HSI is considered as a two-dimensional field of pixel signatures. Methods are proposed for evaluating the similarity of a HSI pixel signature with a reference signature, via simple alignment transformations: identical; amplitude scaling; shift along y-axis; and a combination of the last two. A clustering / recognition method with self-learning is proposed, which determines values of the transformation parameters that ensure the alignment of the current pixel signature with the reference signature. Similarity with the reference is determined by a standard deviation value. A HSI compression method with controlled losses has been proposed. The method forms a basis via accumulating reference signatures and represents the rest of the signatures by parameters matching them with the already detected class-reference signature. In an experiment with the GSI f100520t01p00-12 data of the AVIRIS spectrometer, the method provided a 2 % loss and compression coefficients of the original HSI ranging from 43 to 165 for various types of alignment transformation, while not requiring archiving and thus maintaining active access to the HSI and using the list of references as an analogue of the HSI palette. An algorithm for the formation of dense groups of detectable objects (for example, oil spots) and their nonconvex contouring, controlled by 4 parameters, is proposed. A pilot version of the concept of geographic information system (GIS) and an appropriate database management system (DBMS) was built and implemented, which provides monitoring and is based on the prioritized processing and storage of the HSI, which serve as a data source for the system. A laboratory complex with new algorithms for processing and storing the GSE is introduced into the structure of the system.

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Yueming Wang, Yueming Wang, Feng Xie Feng Xie, and and Jianyu Wang and Jianyu Wang. "Short-wave infrared signature and detection of aicraft in flight based on space-borne hyperspectral imagery." Chinese Optics Letters 14, no. 12 (2016): 122801–4. http://dx.doi.org/10.3788/col201614.122801.

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31

JIJÓN-PALMA, Mario Ernesto, Caisse AMISSE, Jaime Carlos MACUÁCUA, and Jorge Antonio Silva CENTENO. "Noisy band selection based on the integration of the Stacked-Autoencoder and Convolutional Neural Network approaches for hyperspectral data." Geosciences = Geociências 42, no. 2 (September 15, 2023): 269–80. http://dx.doi.org/10.5016/geociencias.v42i2.16976.

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The presence of noise on hyperspectral images causes degradation and hinders efficiency of processing for land cover classification. In this sense, removing noise or detecting noisy bands automatically on hyperspectral images becomes a challenge for research in remote sensing. To cope this problem, an integrated model (SAE-1DCNN) is presented in this study, based on Stacked-Autoencoders (SAE) and Convolutional Neural Networks (CNN) algorithms for the selection and exclusion of noisy bands. The proposed model employs convolutional layers to improve the performance of autoencoders focused on discriminating the training data by analyzing the hyperspectral signature of the pixel. Thus, in the SAE-1DCNN model, information can be compressed, and then redundant information can be detected and extracted by taking advantage of the efficiency of the deep architecture based on the convolutional and pooling layers. Hyperspectral data from the AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) sensor were used to evaluate the performance of the proposed automatic method based on feature selection. The results showed effectiveness to identify noisy bands automatically, suggesting that the proposed methodology was found to be promising and can be an alternative to identify noisy bands within the scope of hyperspectral data pre-processing. Keywords: noisy bands; feature selection; convolutional neural network; stacked-autoencoders; hyperspectral data

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Bacca, Jorge Luis, and Henry Arguello. "Sparse Subspace Clustering in Hyperspectral Images using Incomplete Pixels." TecnoLógicas 22, no. 46 (September 20, 2019): 1–14. http://dx.doi.org/10.22430/22565337.1205.

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Spectral image clustering is an unsupervised classification method which identifies distributions of pixels using spectral information without requiring a previous training stage. The sparse subspace clustering-based methods (SSC) assume that hyperspectral images lie in the union of multiple low-dimensional subspaces. Using this, SSC groups spectral signatures in different subspaces, expressing each spectral signature as a sparse linear combination of all pixels, ensuring that the non-zero elements belong to the same class. Although these methods have shown good accuracy for unsupervised classification of hyperspectral images, the computational complexity becomes intractable as the number of pixels increases, i.e. when the spatial dimension of the image is large. For this reason, this paper proposes to reduce the number of pixels to be classified in the hyperspectral image, and later, the clustering results for the missing pixels are obtained by exploiting the spatial information. Specifically, this work proposes two methodologies to remove the pixels, the first one is based on spatial blue noise distribution which reduces the probability to remove cluster of neighboring pixels, and the second is a sub-sampling procedure that eliminates every two contiguous pixels, preserving the spatial structure of the scene. The performance of the proposed spectral image clustering framework is evaluated in three datasets showing that a similar accuracy is obtained when up to 50% of the pixels are removed, in addition, it is up to 7.9 times faster compared to the classification of the data sets without incomplete pixels.

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Orozco, Jairo, Vidya Manian, Estefania Alfaro, Harkamal Walia, and Balpreet K. Dhatt. "Graph Convolutional Network Using Adaptive Neighborhood Laplacian Matrix for Hyperspectral Images with Application to Rice Seed Image Classification." Sensors 23, no. 7 (March 27, 2023): 3515. http://dx.doi.org/10.3390/s23073515.

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Graph convolutional neural network architectures combine feature extraction and convolutional layers for hyperspectral image classification. An adaptive neighborhood aggregation method based on statistical variance integrating the spatial information along with the spectral signature of the pixels is proposed for improving graph convolutional network classification of hyperspectral images. The spatial-spectral information is integrated into the adjacency matrix and processed by a single-layer graph convolutional network. The algorithm employs an adaptive neighborhood selection criteria conditioned by the class it belongs to. Compared to fixed window-based feature extraction, this method proves effective in capturing the spectral and spatial features with variable pixel neighborhood sizes. The experimental results from the Indian Pines, Houston University, and Botswana Hyperion hyperspectral image datasets show that the proposed AN-GCN can significantly improve classification accuracy. For example, the overall accuracy for Houston University data increases from 81.71% (MiniGCN) to 97.88% (AN-GCN). Furthermore, the AN-GCN can classify hyperspectral images of rice seeds exposed to high day and night temperatures, proving its efficacy in discriminating the seeds under increased ambient temperature treatments.

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Bleszynski, Monika, Shaun Mann, and Maciej Kumosa. "Visualizing Polymer Damage Using Hyperspectral Imaging." Polymers 12, no. 9 (September 12, 2020): 2071. http://dx.doi.org/10.3390/polym12092071.

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Silicone rubbers (SIRs) are common industrial materials which are often used for electrical insulation including weather sheds on non-ceramic insulators (NCIs). While SIRs are typically resilient to outside environments, aging can damage SIRs’ favorable properties such as hydrophobicity and electrical resistance. Detecting SIR aging and damage, however, can be difficult, especially in service. In this study we used hyperspectral imaging (HSI) and previously investigated aging methods as a proof of concept to show how HSI may be used to detect various types of aging damage in different SIR materials. The spectral signature changes in four different SIRs subjected to four different in-service aging environments all occurred between 400––650 nm. Therefore, remote sensing of NCIs using HSI could concentrate on bands below 700 nm to successfully detect in service SIR damage.

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35

Hu, Wei, Yangyu Huang, Li Wei, Fan Zhang, and Hengchao Li. "Deep Convolutional Neural Networks for Hyperspectral Image Classification." Journal of Sensors 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/258619.

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Recently, convolutional neural networks have demonstrated excellent performance on various visual tasks, including the classification of common two-dimensional images. In this paper, deep convolutional neural networks are employed to classify hyperspectral images directly in spectral domain. More specifically, the architecture of the proposed classifier contains five layers with weights which are the input layer, the convolutional layer, the max pooling layer, the full connection layer, and the output layer. These five layers are implemented on each spectral signature to discriminate against others. Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than some traditional methods, such as support vector machines and the conventional deep learning-based methods.

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36

Jafari, Ali, Mohammad Ebadzadeh, and Reza Safabakhsh. "MATERIAL SIGNATURE ORTHONORMAL MAPPING IN HYPERSPECTRAL UNMIXING TO ADDRESS ENDMEMBER VARIABILITY." Advances in Science and Technology Research Journal 10, no. 29 (2016): 71–84. http://dx.doi.org/10.12913/22998624/61935.

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37

Schmidt, Johannes, Fabian Ewald Fassnacht, Angela Lausch, and Sebastian Schmidtlein. "Assessing the functional signature of heathland landscapes via hyperspectral remote sensing." Ecological Indicators 73 (February 2017): 505–12. http://dx.doi.org/10.1016/j.ecolind.2016.10.017.

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38

Saadi, R., M. Hasanlou, and A. Safari. "CLASSIFIER FUSION OF POLSAR, HYPERSPECTRAL AND PAN REMOTE SENSING DATA FOR IMPROVING LAND USE CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 19, 2019): 913–16. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-913-2019.

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Abstract. The combined use of PolSAR and hyperspectral data can improve the classification accuracy. This paper proposes a new classification approach for combining use of PolSAR and hyperspectral image data sets. At the first step, polarization signature is generated from coherency matrix of PolSAR image data. In the second step, in order to improve spatial resolution, the Hyperion image was pan-sharped with the ALI Pan image. In the third step, the Random Forest (RF) classifier is used for classifying PolSAR and hyperspectral data sets in five different classes including: Water (Wa), urban area (Ur), vegetation (Vg), road (Ro), and soil (So). Then, in order to fuse the output of RF for incorporated two data sets, simple majority voting (MV) and weighted majority voting (WMV) methods are used. Three UAVSAR, Hyperion and ALI images that acquired on April 2015 was chosen for this study. The results showed the ability of the polarimetric data for classifying urban and vegetation, and hyperspectral images for water, soil and road classes. Also, the combination of two data sets by using of WMV method causes the improvements of the classification performance.

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39

Spiller, D., L. Ansalone, S. Amici, A. Piscini, and P. P. Mathieu. "ANALYSIS AND DETECTION OF WILDFIRES BY USING PRISMA HYPERSPECTRAL IMAGERY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 28, 2021): 215–22. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-215-2021.

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Abstract. This paper deals with the analysis and detection of wildfires by using PRISMA imagery. Precursore IperSpettrale della Missione Applicativa (Hyperspectral Precursor of the Application Mission, PRISMA) is a new hyperspectral mission by ASI (Agenzia Spaziale Italiana, Italian Space Agency) launched in 2019. This mission provides hyperspectral images with a spectral range of 0.4–2.5 µm and an average spectral resolution less than 10 nm. In this work, we used the PRISMA hypercube acquired during the Australian bushfires of December 2019 in New South Wales. The analysis of the image is presented considering the unique amount of information contained in the continuous spectral signature of the hypercube. The Carbon dioxide Continuum-Interpolated Band Ratio (CO2 CIBR), Hyperspectral Fire Detection Index (HFDI), and Normalized Burn Index (NBR) will be used to analyze the informative content of the image, along with the analysis of some specific visible, near-infrared and shortwave-infrared bands. A multiclass classification is presented by using a I-dimensional convolutional neural network (CNN), and the results will be compared with the ones given by a support vector machine classifier reported in literature. Finally, some preliminary results related to wildfire temperature estimation are presented.

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Kashani, Amir H., Mark Wong, Nicole Koulisis, Chein-I. Chang, Gabriel Martin, and Mark S. Humayun. "Hyperspectral imaging of retinal microvascular anatomy." Journal of Biomedical Engineering and Informatics 2, no. 1 (November 22, 2015): 139. http://dx.doi.org/10.5430/jbei.v2n1p139.

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Background: Hyperspectral image processing has been applied to many aspects of astronomical and earth science research. Furthermore, advances in computed tomographic imaging spectroscopy and diffraction grating design have allowed biological applications for non-invasive tissue analysis. Herein, we describe a hyperspectral computed tomographic imaging spectroscope (HCTIS) that provides high spatial, spectral and temporal resolution ideal for imaging biological tissue in vivo. Methods: We demonstrate proof-of-principle application of the HCTIS by imaging and mapping the microvascular anatomy of the retina of a model organism (rabbit) in vivo. The imaging procedure allows rapid and dense spectral sampling, is non-toxic, non-invasive, and easily adaptable to a commercially available fundus camera system. Results: HCTIS provides highly co-registered temporal, spatial and spectral data with resolution capable of reconstructing the fine vascular tree of the rabbit retina in vivo. Conclusions: We show that HCTIS allows for reliable and reproducible tissue classification and detection using signature discriminant analysis. Future applications of this system may provide promising diagnostic methods for diseases of many tissues.

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41

Capelle, Sonja K., and Stephen A. Macko. "Finding the Spectral Signature of 15Nitrogen Isotopes in Plants by Hyperspectral Techniques." Journal of Remote Sensing Technology 4, no. 1 (December 31, 2016): 115–20. http://dx.doi.org/10.18005/jrst0401009.

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42

Manna, B., B. Samanta, D. Chakravarty, D. Dutta, A. Chowdhury, A. Santra, and A. Banerjee. "Hyperspectral signature analysis using neural network for grade estimation of copper ore." IOP Conference Series: Earth and Environmental Science 169 (July 31, 2018): 012108. http://dx.doi.org/10.1088/1755-1315/169/1/012108.

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43

Lazzeri, Giacomo, William Frodella, Guglielmo Rossi, and Sandro Moretti. "Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy." Sensors 21, no. 12 (June 9, 2021): 3982. http://dx.doi.org/10.3390/s21123982.

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Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth.

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Ma, Chen, Junjun Jiang, Huayi Li, Xiaoguang Mei, and Chengchao Bai. "Hyperspectral Image Classification via Spectral Pooling and Hybrid Transformer." Remote Sensing 14, no. 19 (September 21, 2022): 4732. http://dx.doi.org/10.3390/rs14194732.

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Hyperspectral images (HSIs) contain spatially structured information and pixel-level sequential spectral attributes. The continuous spectral features contain hundreds of wavelength bands and the differences between spectra are essential for achieving fine-grained classification. Due to the limited receptive field of backbone networks, convolutional neural networks (CNNs)-based HSI classification methods show limitations in modeling spectral-wise long-range dependencies with fixed kernel size and a limited number of layers. Recently, the self-attention mechanism of transformer framework is introduced to compensate for the limitations of CNNs and to mine the long-term dependencies of spectral signatures. Therefore, many joint CNN and Transformer architectures for HSI classification have been proposed to obtain the merits of both networks. However, these architectures make it difficult to capture spatial–spectral correlation and CNNs distort the continuous nature of the spectral signature because of the over-focus on spatial information, which means that the transformer can easily encounter bottlenecks in modeling spectral-wise similarity and long-range dependencies. To address this problem, we propose a neighborhood enhancement hybrid transformer (NEHT) network. In particular, a simple 2D convolution module is adopted to achieve dimensionality reduction while minimizing the distortion of the original spectral distribution by stacked CNNs. Then, we extract group-wise spatial–spectral features in a parallel design to enhance the representation capability of each token. Furthermore, a feature fusion strategy is introduced to increase subtle discrepancies of spectra. Finally, the self-attention of transformer is employed to mine the long-term dependencies between the enhanced feature sequences. Extensive experiments are performed on three well-known datasets and the proposed NEHT network shows superiority over state-of-the-art (SOTA) methods. Specifically, our proposed method outperforms the SOTA method by 0.46%, 1.05% and 0.75% on average in overall accuracy, average accuracy and kappa coefficient metrics.

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Christilda, A. Josephine, and R. Manoharan. "Accuracy Measurement of Hyperspectral Image Classification in Remote Sensing with the Light Spectrum-based Affinity Propagation Clustering-based Segmentation." International Journal of Electrical and Electronics Research 12, no. 1 (January 20, 2024): 28–35. http://dx.doi.org/10.37391/ijeer.120105.

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The area of remote sensing and computer vision includes the challenge of hyperspectral image classification. It entails grouping pixels in hyperspectral pictures into several classes according to their spectral signature. Hyperspectral photographs are helpful for a variety of applications, including vegetation study, mineral mapping, and mapping urban land use, since they include information on an object's reflectance in hundreds of small, contiguous wavelength bands. This task's objective is to correctly identify and categorize several item categories in the image. Many approaches have been stated by several researchers in this field to enhance the accuracy of the segmentation and accuracy. However, fails to attain the optimal accuracy due to the intricate nature of the images. To tackle these issues, we propose a novel Modified Extreme Learning machine (M-ELM) approach for the credible hyperspectral image classification outcomes with the publicly available Kaggle datasets. Before the classification, the input images are segmented using the Light Spectrum-based modified affinity propagation clustering technique (LSO-MAPC). In the beginning, the images are pre-processed using the non-linear diffusion partial differential equations technique which effectively pre-processed the image spatially. Experiments are effectuated to analyze the performance of the proposed method and compared it with state-of-art works in a quantitative way. The proposed approach ensures a classification accuracy of 96%.

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46

Cohen, Yuval, Yitzhak August, Dan G. Blumberg, and Stanley R. Rotman. "Evaluating Subpixel Target Detection Algorithms in Hyperspectral Imagery." Journal of Electrical and Computer Engineering 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/103286.

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Our goal in this work is to demonstrate that detectors behave differently for different images and targets and to propose a novel approach to proper detector selection. To choose the algorithm, we analyze image statistics, the target signature, and the target's physical size, but we do not need any type of ground truth. We demonstrate our ability to evaluate detectors and find the best settings for their free parameters by comparing our results using the following stochastic algorithms for target detection: the constrained energy minimization (CEM), generalized likelihood ratio test (GLRT), and adaptive coherence estimator (ACE) algorithms. We test our concepts by using the dataset and scoring methodology of the Rochester Institute of Technology (RIT) Target Detection Blind Test project. The results show that our concept correctly ranks algorithms for the particular images and targets including in the RIT dataset.

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47

Bilius, Laura Bianca, and Ştefan Gheorghe Pentiuc. "Efficient Unsupervised Classification of Hyperspectral Images Using Voronoi Diagrams and Strong Patterns." Sensors 20, no. 19 (October 5, 2020): 5684. http://dx.doi.org/10.3390/s20195684.

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Hyperspectral images (HSIs) are a powerful tool to classify the elements from an area of interest by their spectral signature. In this paper, we propose an efficient method to classify hyperspectral data using Voronoi diagrams and strong patterns in the absence of ground truth. HSI processing consumes a great deal of computing resources because HSIs are represented by large amounts of data. We propose a heuristic method that starts by applying Parafac decomposition for reduction and to construct the abundances matrix. Furthermore, the representative nodes from the abundances map are searched for. A multi-partition of these nodes is found, and based on this, strong patterns are obtained. Then, based on the hierarchical clustering of strong patterns, an optimum partition is found. After strong patterns are labeled, we construct the Voronoi diagram to extend the classification to the entire HSI.

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Geipel, J., and A. Korsaeth. "Hyperspectral Aerial Imaging for Grassland Yield Estimation." Advances in Animal Biosciences 8, no. 2 (June 1, 2017): 770–75. http://dx.doi.org/10.1017/s2040470017000619.

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In this study, we investigated the potential of airborne imaging spectroscopy for in-season grassland yield estimation. We utilized an unmanned aerial vehicle and a hyperspectral imager to measure radiation, ranging from 455 to 780 nm. Initially, we assessed the spectral signature of five typical grassland species by principal component analysis, and identified a distinct reflectance difference, especially between the erectophil grasses and the planophil clover leaves. Then, we analyzed the reflectance of a typical Norwegian sward composition at different harvest dates. In order to estimate yields (dry matter, DM), several powered partial least squares (PPLS) regression and linear regression (LR) models were fitted to the reflectance data and prediction performance of these models were compared with that of simple LR models, based on selected vegetation indices and plant height. We achieved the highest prediction accuracies by means of PPLS, with relative errors of prediction from 9.1 to 11.8% (329 to 487 kg DM ha−1) for the individual harvest dates and 14.3% (558 kg DM ha−1) for a generalized model.

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Gouisset, E., G. Rioland, F. Bourcier, D. Faye, P. Walter, and F. Infante. "Detection and characterization of contamination with fluorescence spectroscopy." IOP Conference Series: Materials Science and Engineering 1287, no. 1 (August 1, 2023): 012025. http://dx.doi.org/10.1088/1757-899x/1287/1/012025.

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Abstract In the field of failure analysis and in particular molecular and particulate contamination, being able to detect any trace of contaminants during the integration of an orbital spacecraft is crucial. In this context, fluorescence allows not only to detect but also to discriminate contaminants. We studied the fluorescence response of two epoxy adhesives, typical sources of spacecraft contamination in orbit with a portable broadband hyperspectral instrument (UV-Vis-NIR) developed in collaboration with the CNES and Intraspec Technologies, but also with a commercial spectrofluorometer. These measurements had two objectives, evaluate the performance of our hyperspectral instrument in order to identify prospect of improvement, but as well study the pertinence of fluorescence signature study in the contamination field. The first goal brings out that the hyperspectral instrument is capable of imaging the scene and allows us to extract fluorescence spectra from the image, but it still needs development, especially in term of sensitivity in UV range. The second goal shows promising results. Fluorescence studies with the spectrofluorometer emphasize that fluorescence spectra are specific to the chemical nature of the contaminant, which allows us to clearly discriminate them.

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Serrano, Jorge, José Fábrega, Evelyn Quirós, Javier Sánchez Galán, and José Ulises Jiménez. "Análisis Prospectivo de la Detección Hiperespectral de Cultivos de Arroz (Oryza Sativa L.)." KnE Engineering 3, no. 1 (February 11, 2018): 69. http://dx.doi.org/10.18502/keg.v3i1.1414.

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The objective of this work is to perform a prospective analysis of the wavelengths that can be used to recognize a rice crop due to its phenological status and variety. For this purpose, field measurements of spectral signature in the 350 nm -1049 nm range were employed. The rice cultivars FCA 616FL and IDIAP 54-05 were used. The study site was located in the Juan Hombrón area in the Coclé province, Panama. A principal component analysis (PCA) was carried out, which resulted in the lengths 728.16, 677.89 and 785.48 nm let phenological differentiation within the cultivar FCA 616FL and IDIAP 54-05, the lengths 729.72, 814.58 and 666.81 nm let distinguish between crop varieties FCA 616FL and IDIAP 54-05 in vegetative phase.Keywords: Rice, reflectance, hyperspectral signature, phonological state.

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