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Journal articles on the topic 'Signature hyperspectrale'
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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Kasilingam, Cendrayan, Arisiyappan Thirunavukkarasu, and Chandran Ramachandran. "Spectral signatures for iron ore deposits in Tirthamalai area, Dharmapuri District, Tamil Nadu, India." Journal of Applied and Natural Science 15, no. 1 (March 19, 2023): 107–15. http://dx.doi.org/10.31018/jans.v15i1.4160.
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The demand for iron ore has increased recently and employing geochemical and hyperspectral remote sensing techniques for discovering new ore and mineral resources have primarily been concentrated on the economic phases. The present study aimed to characterize the hyperspectral spectral signatures of iron ores of field samples to map the deposits that occurred in the Tirthamalai hill region, which lies in the parts of Harur Taluk, Dharmapuri district of Tamil Nadu state, India The measurement and study of spectral signatures of the different samples of the deposits showed strong spectral absorptions near 500 nm, 900 nm and 2400 nm wavelength regions and were confirmed with the Fourier Transform Infrared (FTIR) spectroscopy method. The spectral absorption characteristics of the samples were evaluated by the study of the physical, optical, and chemical characteristics of the samples. The study of hyperspectral and FTIR spectral signatures with petrographic and major chemical elements revealed the best absorption characteristics of the iron ore deposits of the study region and can be used elsewhere in the world. This report presents preliminary findings on the use of hyperspectral imaging to characterize iron ore. The mineralogical products produced from hyperspectral images may improve in situ grade control on an iron ore mine face. It will be extremely useful for businesses in measuring large numbers of commodities quickly and objectively.
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Raychaudhuri, Barun. "Synthesis of mixed pixel hyperspectral signatures." International Journal of Remote Sensing 33, no. 6 (October 7, 2011): 1954–66. http://dx.doi.org/10.1080/01431161.2011.610378.
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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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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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Khandelwal, A., and K. S. Rajan. "Sensor Simulation based Hyperspectral Image Enhancement with Minimal Spectral Distortion." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-8 (November 27, 2014): 179–85. http://dx.doi.org/10.5194/isprsannals-ii-8-179-2014.
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In the recent past, remotely sensed data with high spectral resolution has been made available and has been explored for various agricultural and geological applications. While these spectral signatures of the objects of interest provide important clues, the relatively poor spatial resolution of these hyperspectral images limits their utility and performance. In this context, hyperspectral image enhancement using multispectral data has been actively pursued to improve spatial resolution of such imageries and thus enhancing its use for classification and composition analysis in various applications. But, this also poses a challenge in terms of managing the trade-off between improved spatial detail and the distortion of spectral signatures in these fused outcomes. This paper proposes a strategy of using vector decomposition, as a model to transfer the spatial detail from relatively higher resolution data, in association with sensor simulation to generate a fused hyperspectral image while preserving the inter band spectral variability. The results of this approach demonstrates that the spectral separation between classes has been better captured and thus helped improve classification accuracies over mixed pixels of the original low resolution hyperspectral data. In addition, the quantitative analysis using a rank-correlation metric shows the appropriateness of the proposed method over the other known approaches with regard to preserving the spectral signatures.
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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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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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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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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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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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Babić, L., V. Miljković, I. Odak, and A. Đapo. "HYPERSPECTRAL IMAGING IN PRESERVATION OF CROATIA’S HISTORIC TREES: A CASE STUDY OF DEDEK OAK IN MAKSIMIR PARK." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 14, 2023): 1853–59. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-1853-2023.
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Abstract. This study focuses on the application of hyperspectral imaging techniques for the genetic preservation of historic trees, specifically investigating the 600-year-old "Dedek" Oak in Maksimir Park, Croatia. The research aims to contribute to the conservation of Croatia's natural heritage and underscore the potential of hyperspectral imaging in tree preservation. The Croatian Forest Research Institute has been actively involved in preserving the pedunculate oak trees, recognizing their cultural and ecological significance. Efforts have been made to cultivate seedlings from the original genetic material, thereby preventing the loss of valuable genes and ensuring their long-term preservation. Spectral signatures of "Dedek" Oak and its clones were captured using high-performance hyperspectral push broom cameras, including the HySpex VNIR-1800 and SWIR-384. Spectral camera scanning was conducted in the field and laboratory. The comparative analysis of the spectral samples revealed unique characteristics and demonstrated the effectiveness of hyperspectral imaging in studying historic trees for preservation purposes. The spectral signatures of vegetation display dynamic characteristics in terms of spectral resolution. Collecting and documenting these signatures is considerably more challenging, and their integration into spectral libraries should be approached with careful consideration. There are several spectral libraries that are organized by chapters and consist of samples that have a sufficient number of analysis and documentation to determine the quality of the spectrum. In this study, spectral signatures of pedunculate oak (Quercus robur L.) called Dedek and its clones were singled out. The objective of this research was to establish a foundation for a spectral library that would facilitate future research on the application of hyperspectral scanners for detecting protected tree species and their clones, all for the purpose of preservation of the genetic diversity of protected trees in Croatia. The findings of this research contribute to the conservation of Croatia's natural heritage by providing valuable insights into the genetic preservation of the 600-year-old "Dedek" Oak. Also, determining the spectral signatures of clones can help us with the identification of specific tree species (especially during aerial imaging, which is planned to be conducted in the future). In conclusion, this research showcases the importance of preserving historic trees and the potential of hyperspectral imaging as a valuable tool for genetic preservation efforts. The study's outcomes contribute to the long-term conservation of Croatia's natural heritage and provide a foundation for future research in the field of tree preservation and spectral library inception.
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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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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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Carrasco-García, María Gema, María Inmaculada Rodríguez-García, Juan Jesús Ruíz-Aguilar, Lipika Deka, David Elizondo, and Ignacio José Turias Domínguez. "Oil Spill Classification Using an Autoencoder and Hyperspectral Technology." Journal of Marine Science and Engineering 12, no. 3 (March 15, 2024): 495. http://dx.doi.org/10.3390/jmse12030495.
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Hyperspectral technology has been playing a leading role in monitoring oil spills in marine environments, which is an issue of international concern. In the case of monitoring oil spills in local areas, hyperspectral technology of small dimensions is the ideal solution. This research explores the use of encoded hyperspectral signatures to develop automated classifiers capable of discriminating between polluted and clean water and distinguishing between various types of oil. The overall objective is to leverage these classifiers to be able to improve the performance of conventional systems that rely solely on hyperspectral imagery. The acquisition of the hyperspectral signatures of water and hydrocarbons was carried out with a spectroradiometer. The range of the spectroradiometer used in this study covers the ranges between [350–1000] (visible near-infrared) and [1000–2500] (short-wavelength infrared). This gives detailed information regarding the targets of interest. Different neural autoencoders (AEs) have been developed to reduce inputs into different dimensions, from 1 to 15. Each of these encoded sets was used to train decision tree (DT) classifiers. The results are very promising, as they show that the AE models encoded data with correlation coefficients above 0.95. The classifiers trained with the different sets provide accuracies close to 1.
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Chang, Chein-I., Sumit Chakravarty, Chien-Shun Lo, and Chinsu Lin. "Spectral Feature Probabilistic Coding for Hyperspectral Signatures." IEEE Sensors Journal 10, no. 3 (March 2010): 395–409. http://dx.doi.org/10.1109/jsen.2009.2038119.
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Vyas, Saurabh, Amit Banerjee, and Philippe Burlina. "Estimating physiological skin parameters from hyperspectral signatures." Journal of Biomedical Optics 18, no. 5 (May 30, 2013): 057008. http://dx.doi.org/10.1117/1.jbo.18.5.057008.
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Pechlivani, Eleftheria Maria, Athanasios Papadimitriou, Sotirios Pemas, Nikolaos Giakoumoglou, and Dimitrios Tzovaras. "Low-Cost Hyperspectral Imaging Device for Portable Remote Sensing." Instruments 7, no. 4 (October 19, 2023): 32. http://dx.doi.org/10.3390/instruments7040032.
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Hyperspectral imaging has revolutionized various scientific fields by enabling a detailed analysis of objects and materials based on their spectral signatures. However, the high cost and complexity of commercial hyperspectral camera systems limit their accessibility to researchers and professionals. In this paper, a do-it-yourself (DIY) hyperspectral camera device that offers a cost-effective and user-friendly alternative to hyperspectral imaging is presented. The proposed device leverages off-the-shelf components, commercially available hardware parts, open-source software, and novel calibration techniques to capture and process hyperspectral imaging data. The design considerations, hardware components, and construction process are discussed, providing a comprehensive guide for building the device. Furthermore, the performance of the DIY hyperspectral camera is investigated through experimental evaluations with a multi-color 3D-printed box in order to validate its sensitivities to red, green, blue, orange and white colors.
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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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de Castro, Ana-Isabel, Montserrat Jurado-Expósito, María-Teresa Gómez-Casero, and Francisca López-Granados. "Applying Neural Networks to Hyperspectral and Multispectral Field Data for Discrimination of Cruciferous Weeds in Winter Crops." Scientific World Journal 2012 (2012): 1–11. http://dx.doi.org/10.1100/2012/630390.
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In the context of detection of weeds in crops for site-specific weed control, on-ground spectral reflectance measurements are the first step to determine the potential of remote spectral data to classify weeds and crops. Field studies were conducted for four years at different locations in Spain. We aimed to distinguish cruciferous weeds in wheat and broad bean crops, using hyperspectral and multispectral readings in the visible and near-infrared spectrum. To identify differences in reflectance between cruciferous weeds, we applied three classification methods: stepwise discriminant (STEPDISC) analysis and two neural networks, specifically, multilayer perceptron (MLP) and radial basis function (RBF). Hyperspectral and multispectral signatures of cruciferous weeds, and wheat and broad bean crops can be classified using STEPDISC analysis, and MLP and RBF neural networks with different success, being the MLP model the most accurate with 100%, or higher than 98.1%, of classification performance for all the years. Classification accuracy from hyperspectral signatures was similar to that from multispectral and spectral indices, suggesting that little advantage would be obtained by using more expensive airborne hyperspectral imagery. Therefore, for next investigations, we recommend using multispectral remote imagery to explore whether they can potentially discriminate these weeds and crops.
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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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Kuska, Matheus Thomas, Anna Brugger, Stefan Thomas, Mirwaes Wahabzada, Kristian Kersting, Erich-Christian Oerke, Ulrike Steiner, and Anne-Katrin Mahlein. "Spectral Patterns Reveal Early Resistance Reactions of Barley Against Blumeria graminis f. sp. hordei." Phytopathology® 107, no. 11 (November 2017): 1388–98. http://dx.doi.org/10.1094/phyto-04-17-0128-r.
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Differences in early plant–pathogen interactions are mainly characterized by using destructive methods. Optical sensors are advanced techniques for phenotyping host–pathogen interactions on different scales and for detecting subtle plant resistance responses against pathogens. A microscope with a hyperspectral camera was used to study interactions between Blumeria graminis f. sp. hordei and barley (Hordeum vulgare) genotypes with high susceptibility or resistance due to hypersensitive response (HR) and papilla formation. Qualitative and quantitative assessment of pathogen development was used to explain changes in hyperspectral signatures. Within 48 h after inoculation, genotype-specific changes in the green and red range (500 to 690 nm) and a blue shift of the red-edge inflection point were observed. Manual analysis indicated resistance-specific reflectance patterns from 1 to 3 days after inoculation. These changes could be linked to host plant modifications depending on individual host–pathogen interactions. Retrospective analysis of hyperspectral images revealed spectral characteristics of HR against B. graminis f. sp. hordei. For early HR detection, an advanced data mining approach localized HR spots before they became visible on the RGB images derived from hyperspectral imaging. The link among processes during pathogenesis and host resistance to changes in hyperspectral signatures provide evidence that sensor-based phenotyping is suitable to advance time-consuming and cost-expensive visual rating of plant disease resistances.
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Sadeghi-Tehran, Pouria, Nicolas Virlet, and Malcolm J. Hawkesford. "A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery." Remote Sensing 13, no. 5 (February 27, 2021): 898. http://dx.doi.org/10.3390/rs13050898.
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(1) Background: Information rich hyperspectral sensing, together with robust image analysis, is providing new research pathways in plant phenotyping. This combination facilitates the acquisition of spectral signatures of individual plant organs as well as providing detailed information about the physiological status of plants. Despite the advances in hyperspectral technology in field-based plant phenotyping, little is known about the characteristic spectral signatures of shaded and sunlit components in wheat canopies. Non-imaging hyperspectral sensors cannot provide spatial information; thus, they are not able to distinguish the spectral reflectance differences between canopy components. On the other hand, the rapid development of high-resolution imaging spectroscopy sensors opens new opportunities to investigate the reflectance spectra of individual plant organs which lead to the understanding of canopy biophysical and chemical characteristics. (2) Method: This study reports the development of a computer vision pipeline to analyze ground-acquired imaging spectrometry with high spatial and spectral resolutions for plant phenotyping. The work focuses on the critical steps in the image analysis pipeline from pre-processing to the classification of hyperspectral images. In this paper, two convolutional neural networks (CNN) are employed to automatically map wheat canopy components in shaded and sunlit regions and to determine their specific spectral signatures. The first method uses pixel vectors of the full spectral features as inputs to the CNN model and the second method integrates the dimension reduction technique known as linear discriminate analysis (LDA) along with the CNN to increase the feature discrimination and improves computational efficiency. (3) Results: The proposed technique alleviates the limitations and lack of separability inherent in existing pre-defined hyperspectral classification methods. It optimizes the use of hyperspectral imaging and ensures that the data provide information about the spectral characteristics of the targeted plant organs, rather than the background. We demonstrated that high-resolution hyperspectral imagery along with the proposed CNN model can be powerful tools for characterizing sunlit and shaded components of wheat canopies in the field. The presented method will provide significant advances in the determination and relevance of spectral properties of shaded and sunlit canopy components under natural light conditions.
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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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Chakravortty, S., and P. Subramaniam. "Fusion of Hyperspectral and Multispectral Image Data for Enhancement of Spectral and Spatial Resolution." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 1099–103. http://dx.doi.org/10.5194/isprsarchives-xl-8-1099-2014.
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Hyperspectral image enhancement has been a concern for the remote sensing society for detailed end member detection. Hyperspectral remote sensor collects images in hundreds of narrow, continuous spectral channels, whereas multispectral remote sensor collects images in relatively broader wavelength bands. However, the spatial resolution of the hyperspectral sensor image is comparatively lower than that of the multispectral. As a result, spectral signatures from different end members originate within a pixel, known as mixed pixels. This paper presents an approach for obtaining an image which has the spatial resolution of the multispectral image and spectral resolution of the hyperspectral image, by fusion of hyperspectral and multispectral image. The proposed methodology also addresses the band remapping problem, which arises due to different regions of spectral coverage by multispectral and hyperspectral images. Therefore we apply algorithms to restore the spatial information of the hyperspectral image by fusing hyperspectral bands with only those bands which come under each multispectral band range. The proposed methodology is applied over Henry Island, of the Sunderban eco-geographic province. The data is collected by the Hyperion hyperspectral sensor and LISS IV multispectral sensor.
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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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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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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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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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Olyaei, Mohammadali, and Ardeshir Ebtehaj. "Uncovering Plastic Litter Spectral Signatures: A Comparative Study of Hyperspectral Band Selection Algorithms." Remote Sensing 16, no. 1 (December 31, 2023): 172. http://dx.doi.org/10.3390/rs16010172.
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This article provides insights into the optical signatures of plastic litter based on a published laboratory-scale reflectance data set (350–2500 nm) of dry and wet plastic debris under clear and turbid waters using different band selection techniques, including sparse variable selection, density peak clustering, and hierarchical clustering. The variable selection method identifies important wavelengths by minimizing a reconstruction error metric, while clustering approaches rely on the strengths of the correlation and local density of the spectra. Analyses of the data reveal three distinct absorption lines at 560, 740, and 980 nm that produce relatively broad reflectance peaks in the measured spectra of wet plastics around 475–490, 635–650, 810–815, and 1070 nm. The results of band selection consistently identify three important regions across 450–470, 650–690, and 1050–1100 nm that are close to the reflectance peaks of the mean of wet plastic spectra over clear and turbid waters. However, as the number of isolated important wavelengths increases, the results of the methodologies diverge. Density peak clustering identifies additional wavelengths in the short-wave infrared (SWIR) region of 1170–1180 nm) as a result of a high local density of the reflectance points. In contrast, hierarchical clustering isolates more wavelengths in the visible range of 365–400 nm due to weak correlations of nearby wavelengths. The results of the clustering methods are not consistent with the visual inspection of the signatures as peaks and valleys in the spectra, which are effectively captured by the variable selection method. It is also found that the presence of suspended sediments can (i) shift the important wavelength towards higher values in the visible part of the spectrum by less than 50 nm, (ii) attenuate the magnitude of wet plastic reflectance by up to 80% across the entire spectrum, and (iii) manifest a similar spectral signature with plastic litter from 1070 to 1100 nm.
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Banerjee, Bikram Pratap, and Simit Raval. "A Particle Swarm Optimization Based Approach to Pre-tune Programmable Hyperspectral Sensors." Remote Sensing 13, no. 16 (August 20, 2021): 3295. http://dx.doi.org/10.3390/rs13163295.
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Identification of optimal spectral bands often involves collecting in-field spectral signatures followed by thorough analysis. Such rigorous field sampling exercises are tedious, cumbersome, and often impractical on challenging terrain, which is a limiting factor for programmable hyperspectral sensors mounted on unmanned aerial vehicles (UAV-hyperspectral systems), requiring a pre-selection of optimal bands when mapping new environments with new target classes with unknown spectra. An innovative workflow has been designed and implemented to simplify the process of in-field spectral sampling and its realtime analysis for the identification of optimal spectral wavelengths. The band selection optimization workflow involves particle swarm optimization with minimum estimated abundance covariance (PSO-MEAC) for the identification of a set of bands most appropriate for UAV-hyperspectral imaging, in a given environment. The criterion function, MEAC, greatly simplifies the in-field spectral data acquisition process by requiring a few target class signatures and not requiring extensive training samples for each class. The metaheuristic method was tested on an experimental site with diversity in vegetation species and communities. The optimal set of bands were found to suitably capture the spectral variations between target vegetation species and communities. The approach streamlines the pre-tuning of wavelengths in programmable hyperspectral sensors in mapping applications. This will additionally reduce the total flight time in UAV-hyperspectral imaging, as obtaining information for an optimal subset of wavelengths is more efficient, and requires less data storage and computational resources for post-processing the data.
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Sture, Øystein, Ben Snook, and Martin Ludvigsen. "Obtaining Hyperspectral Signatures for Seafloor Massive Sulphide Exploration." Minerals 9, no. 11 (November 10, 2019): 694. http://dx.doi.org/10.3390/min9110694.
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Seafloor massive sulphide (SMS) deposits are hosts to a wide range of economic minerals, and may become an important resource in the future. The exploitation of these resources is associated with considerable expenses, and a return on investment may depend on the availability of multiple deposits. Therefore, efficient exploration methodologies for base metal deposits are important for future deep sea mining endeavours. Underwater hyperspectral imaging (UHI) has been demonstrated to be able to differentiate between different types of materials on the seafloor. The identification of possible end-members from field data requires prior information in the form of representative signatures for distinct materials. This work presents hyperspectral imaging applied to a selection of materials from the Loki’s Castle active hydrothermal vent site in a laboratory setting. A methodology for compensating for systematic effects and producing the reflectance spectra is detailed, and applied to recover the spectral signatures from the samples. The materials investigated were found to be distinguishable using unsupervised dimensionality reduction methods, and may be used as a reference for future field application.
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Alizade Naeini, A., A. Jamshidzadeh, M. Saadatseresht, and S. Homayouni. "AN EFFICIENT INITIALIZATION METHOD FOR K-MEANS CLUSTERING OF HYPERSPECTRAL DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-2/W3 (October 22, 2014): 35–39. http://dx.doi.org/10.5194/isprsarchives-xl-2-w3-35-2014.
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K-means is definitely the most frequently used partitional clustering algorithm in the remote sensing community. Unfortunately due to its gradient decent nature, this algorithm is highly sensitive to the initial placement of cluster centers. This problem deteriorates for the high-dimensional data such as hyperspectral remotely sensed imagery. To tackle this problem, in this paper, the spectral signatures of the endmembers in the image scene are extracted and used as the initial positions of the cluster centers. For this purpose, in the first step, A Neyman–Pearson detection theory based eigen-thresholding method (i.e., the HFC method) has been employed to estimate the number of endmembers in the image. Afterwards, the spectral signatures of the endmembers are obtained using the Minimum Volume Enclosing Simplex (MVES) algorithm. Eventually, these spectral signatures are used to initialize the k-means clustering algorithm. The proposed method is implemented on a hyperspectral dataset acquired by ROSIS sensor with 103 spectral bands over the Pavia University campus, Italy. For comparative evaluation, two other commonly used initialization methods (i.e., Bradley & Fayyad (BF) and Random methods) are implemented and compared. The confusion matrix, overall accuracy and Kappa coefficient are employed to assess the methods’ performance. The evaluations demonstrate that the proposed solution outperforms the other initialization methods and can be applied for unsupervised classification of hyperspectral imagery for landcover mapping.
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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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Shao, Zhenfeng, Weixun Zhou, Qimin Cheng, Chunyuan Diao, and Lei Zhang. "An effective hyperspectral image retrieval method using integrated spectral and textural features." Sensor Review 35, no. 3 (June 15, 2015): 274–81. http://dx.doi.org/10.1108/sr-10-2014-0716.
Full textAbstract:
Purpose – The purpose of this paper is to improve the retrieval results of hyperspectral image by integrating both spectral and textural features. For this purpose, an improved multiscale opponent representation for hyperspectral texture is proposed to represent the spatial information of the hyperspectral scene. Design/methodology/approach – In the presented approach, end-member signatures are extracted as spectral features by means of the widely used end-member induction algorithm N-FINDR, and the improved multiscale opponent representation is extracted from the first three principal components of the hyperspectral data based on Gabor filters. Then, the combination similarity between query image and other images in the database is calculated, and the first k more similar images are returned in descending order of the combination similarity. Findings – Some experiments are calculated using the airborne hyperspectral data of Washington DC Mall. According to the experimental results, the proposed method improves the retrieval results, especially for image categories that have regular textural structures. Originality/value – The paper presents an effective retrieval method for hyperspectral images.
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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.
Full textAbstract:
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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Agrawal, Rajesh, and Narendra Bawane. "Adaptive Lifting Transform for Classification of Hyperspectral Signatures." Advances in Remote Sensing 04, no. 02 (2015): 138–46. http://dx.doi.org/10.4236/ars.2015.42012.
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