Journal articles: 'HYPER SPECTRAL IMAGE CLASSIFICATION'

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HUANG Hong, 黄. 鸿., 陈美利 CHEN Mei-li, 段宇乐 DUAN Yu-le, and 石光耀 SHI Guang-yao. "Hyper-spectral image classification using spatial-spectral manifold reconstruction." Optics and Precision Engineering 26, no. 7 (2018): 1827–36. http://dx.doi.org/10.3788/ope.20182607.1827.

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Javadi, P. "USE SATELLITE IMAGES AND IMPROVE THE ACCURACY OF HYPERSPECTRAL IMAGE WITH THE CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 343–49. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-343-2015.

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The best technique to extract information from remotely sensed image is classification. The problem of traditional classification methods is that each pixel is assigned to a single class by presuming all pixels within the image. Mixed pixel classification or spectral unmixing, is a process that extracts the proportions of the pure components of each mixed pixel. This approach is called spectral unmixing. Hyper spectral images have higher spectral resolution than multispectral images. In this paper, pixel-based classification methods such as the spectral angle mapper, maximum likelihood classification and subpixel classification method (linear spectral unmixing) were implemented on the AVIRIS hyper spectral images. Then, pixel-based and subpixel based classification algorithms were compared. Also, the capabilities and advantages of spectral linear unmixing method were investigated. The spectral unmixing method that implemented here is an effective technique for classifying a hyperspectral image giving the classification accuracy about 89%. The results of classification when applying on the original images are not good because some of the hyperspectral image bands are subject to absorption and they contain only little signal. So it is necessary to prepare the data at the beginning of the process. The bands can be stored according to their variance. In bands with a high variance, we can distinguish the features from each other in a better mode in order to increase the accuracy of classification. Also, applying the MNF transformation on the hyperspectral images increase the individual classes accuracy of pixel based classification methods as well as unmixing method about 20 percent and 9 percent respectively.

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Alhayani, Bilal, and Haci Ilhan. "Hyper spectral Image classification using Dimensionality Reduction Techniques." IJIREEICE 5, no. 4 (April 15, 2017): 71–74. http://dx.doi.org/10.17148/ijireeice.2017.5414.

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Sharif, I., and S. Khare. "Comparative Analysis of Haar and Daubechies Wavelet for Hyper Spectral Image Classification." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 937–41. http://dx.doi.org/10.5194/isprsarchives-xl-8-937-2014.

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With the number of channels in the hundreds instead of in the tens Hyper spectral imagery possesses much richer spectral information than multispectral imagery. The increased dimensionality of such Hyper spectral data provides a challenge to the current technique for analyzing data. Conventional classification methods may not be useful without dimension reduction pre-processing. So dimension reduction has become a significant part of Hyper spectral image processing. This paper presents a comparative analysis of the efficacy of Haar and Daubechies wavelets for dimensionality reduction in achieving image classification. Spectral data reduction using Wavelet Decomposition could be useful because it preserves the distinction among spectral signatures. Daubechies wavelets optimally capture the polynomial trends while Haar wavelet is discontinuous and resembles a step function. The performance of these wavelets are compared in terms of classification accuracy and time complexity. This paper shows that wavelet reduction has more separate classes and yields better or comparable classification accuracy. In the context of the dimensionality reduction algorithm, it is found that the performance of classification of Daubechies wavelets is better as compared to Haar wavelet while Daubechies takes more time compare to Haar wavelet. The experimental results demonstrate the classification system consistently provides over 84% classification accuracy.

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Shanmugapriya, G., and . "An Efficient Spectral Spatial Classification for Hyper Spectral Images." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 1050. http://dx.doi.org/10.14419/ijet.v7i3.12.17630.

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An expanded random walker comprises of two primary advances ghostly spatial order strategy for hyper Ghastly pictures. To begin with go to pixel astute order by utilizing bolster vector machine (SVM) which is arrangement likelihood maps for a hyper unearthly picture. Probabilities of hyper phantom Pixel have a place with various classes. The second approach is getting pixel shrewd likelihood maps are upgraded broadened arbitrary walker calculation. Pixel astute measurements data by SVM classifier, spatial relationship between neighboring pixels displayed through weight of diagram edges preparation and test tests demonstrated irregular walkers. These 3 components utilizing for the class of validating pixel are resolved. So, these three elements considered in ERW. The proposed technique demonstrates great order performs for three generally utilized genuine hyper otherworldly informational collections even the quantity of preparing tests is moderately little.

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Banit', Ibtissam, N. A. ouagua, Mounir Ait Kerroum, Ahmed Hammouch, and Driss Aboutajdine. "Band selection by mutual information for hyper-spectral image classification." International Journal of Advanced Intelligence Paradigms 8, no. 1 (2016): 98. http://dx.doi.org/10.1504/ijaip.2016.074791.

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TANG Yan-hui, 唐艳慧, 赵鹏 ZHAO Peng, and 王承琨 WANG Cheng-kun. "Texture classification algorithm of wood hyper-spectral image based on multi-fractal spectra." Chinese Journal of Liquid Crystals and Displays 34, no. 12 (2019): 1182–90. http://dx.doi.org/10.3788/yjyxs20193412.1182.

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Lavanya, K., R. Jaya Subalakshmi, T. Tamizharasi, Lydia Jane, and Akila Victor. "Unsupervised Unmixing and Segmentation of Hyper Spectral Images Accounting for Soil Fertility." Scalable Computing: Practice and Experience 23, no. 4 (December 23, 2022): 291–301. http://dx.doi.org/10.12694/scpe.v23i4.2031.

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A crucial component of precision agriculture is the capability to assess the fertility of soil by looking at the precise distribution and composition of its different constituents. This study aims to investigate how different machine learning models may be used to assess soil fertility using hyperspectral pictures. The development of images using a random mixing of different soil components is the first phase, and the hyper spectral bands utilized to create the images are not used again during the analysis procedure. The resulting end members are then acquired by applying the NFINDR algorithm to the process of spectral unmixing this image. The comparison between these end members and the band values of the known elements is then quantified., i.e. it is represented as a graph of band values obtained through spectral unmixing. Finally we quantify the similarities between both graphs and proceed towards the classification of the hyper spectral image as fertile or infertile. In order to classify the hyper spectral image as fertile or infertile, we quantify the similarities between the two graphs. Clustering and picture segmentation algorithms have been devised to help with this process, and a comparison is then made to show which techniques are the most effective.

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Zhang, Tianxiang, Wenxuan Wang, Jing Wang, Yuanxiu Cai, Zhifang Yang, and Jiangyun Li. "Hyper-LGNet: Coupling Local and Global Features for Hyperspectral Image Classification." Remote Sensing 14, no. 20 (October 20, 2022): 5251. http://dx.doi.org/10.3390/rs14205251.

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Hyperspectral sensors provide an opportunity to capture the intensity of high spatial/spectral information and enable applications for high-level earth observation missions, such as accurate land cover mapping and target/object detection. Currently, convolutional neural networks (CNNs) are good at coping with hyperspectral image processing tasks because of the strong spatial and spectral feature extraction ability brought by hierarchical structures, but the convolution operation in CNNs is limited to local feature extraction in both dimensions. In the meanwhile, the introduction of the Transformer structure has provided an opportunity to capture long-distance dependencies between tokens from a global perspective; however, Transformer-based methods have a restricted ability to extract local information because they have no inductive bias, as CNNs do. To make full use of these two methods’ advantages in hyperspectral image processing, a dual-flow architecture named Hyper-LGNet to couple local and global features is firstly proposed by integrating CNN and Transformer branches to deal with HSI spatial-spectral information. In particular, a spatial-spectral feature fusion module (SSFFM) is designed to maximally integrate spectral and spatial information. Three mainstream hyperspectral datasets (Indian Pines, Pavia University and Houston 2013) are utilized to evaluate the proposed method’s performance. Comparative results show that the proposed Hyper-LGNet achieves state-of-the-art performance in comparison with the other nine approaches concerning overall accuracy (OA), average accuracy (AA) and kappa index. Consequently, it is anticipated that, by coupling CNN and Transformer structures, this study can provide novel insights into hyperspectral image analysis.

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Li, Runya, and Shenglian Li. "Multimedia Image Data Analysis Based on KNN Algorithm." Computational Intelligence and Neuroscience 2022 (April 12, 2022): 1–8. http://dx.doi.org/10.1155/2022/7963603.

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In order to improve the authenticity of multispectral remote sensing image data analysis, the KNN algorithm and hyperspectral remote sensing technology are used to organically combine advanced multimedia technology with spectral technology to subdivide the spectrum. Different classification methods are used to classify CHRIS 0°, and the results are analyzed and compared: SVM classification accuracy is the highest 72 8448%, Kappa coefficient is 0.6770, and SVM is used to classify CHRIS images from five angles, and the results are compared and analyzed: the classification accuracy is from high to low, and the order is FZA = 0 > FZA = −36 > FZA = −55 > FZA = 36 > FZA = 55; SVM is used to classify the multiangle combined image, and the result is compared with the CHRIS 0° result: the overall classification accuracy of angle-combined image types is lower than that of single-angle images; the SVM is used to classify the band-combined image, and the result is compared with CHRIS 0°: the overall classification accuracy of the band combination image forest type is very low, and the effect is not as good as the combining multiangle image classification results. It is verified that if CHRIS multiangle hyper-spectral data are used for classification, the SVM method should be used to classify spectral remote sensing image data with the best effect.

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Munishamaiaha, Kavitha, Senthil Kumar Kannan, DhilipKumar Venkatesan, Michał Jasiński, Filip Novak, Radomir Gono, and Zbigniew Leonowicz. "Hyperspectral Image Classification with Deep CNN Using an Enhanced Elephant Herding Optimization for Updating Hyper-Parameters." Electronics 12, no. 5 (February 27, 2023): 1157. http://dx.doi.org/10.3390/electronics12051157.

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Deep learning approaches based on convolutional neural networks (CNNs) have recently achieved success in computer vision, demonstrating significant superiority in the domain of image processing. For hyperspectral image (HSI) classification, convolutional neural networks are an efficient option. Hyperspectral image classification approaches are often based on spectral information. Convolutional neural networks are used for image classification in order to achieve greater performance. The complex computation in convolutional neural networks requires hyper-parameters that attain high accuracy outputs, and this process needs more computational time and effort. Following up on the proposed technique, a bio-inspired metaheuristic strategy based on an enhanced form of elephant herding optimization is proposed in this research paper. It allows one to automatically search for and target the suitable values of convolutional neural network hyper-parameters. To design an automatic system for hyperspectral image classification, the enhanced elephant herding optimization (EEHO) with the AdaBound optimizer is implemented for the tuning and updating of the hyper-parameters of convolutional neural networks (CNN–EEHO–AdaBound). The validation of the convolutional network hyper-parameters should produce a highly accurate response of high-accuracy outputs in order to achieve high-level accuracy in HSI classification, and this process takes a significant amount of processing time. The experiments are carried out on benchmark datasets (Indian Pines and Salinas) for evaluation. The proposed methodology outperforms state-of-the-art methods in a performance comparative analysis, with the findings proving its effectiveness. The results show the improved accuracy of HSI classification by optimising and tuning the hyper-parameters.

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Kavitha, K., and Dr S. Arivazhagan. "A Novel Feature Derivation Technique for SVM based Hyper Spectral Image Classification." International Journal of Computer Applications 1, no. 15 (February 25, 2010): 27–34. http://dx.doi.org/10.5120/327-496.

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Segonne, Charlotte, Nathalie Huret, Sébastien Payan, Mathieu Gouhier, and Valéry Catoire. "A Spectra Classification Methodology of Hyperspectral Infrared Images for Near Real-Time Estimation of the SO2 Emission Flux from Mount Etna with LARA Radiative Transfer Retrieval Model." Remote Sensing 12, no. 24 (December 16, 2020): 4107. http://dx.doi.org/10.3390/rs12244107.

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Fast and accurate quantification of gas fluxes emitted by volcanoes is essential for the risk mitigation of explosive eruption, and for the fundamental understanding of shallow eruptive processes. Sulphur dioxide (SO2), in particular, is a reliable indicator to predict upcoming eruptions, and its systemic characterization allows the rapid assessment of sudden changes in eruptive dynamics. In this regard, infrared (IR) hyperspectral imaging is a promising new technology for accurately measure SO2 fluxes day and night at a frame rate down to 1 image per second. The thermal infrared region is not very sensitive to particle scattering, which is an asset for the study of volcanic plume. A ground based infrared hyperspectral imager was deployed during the IMAGETNA campaign in 2015 and provided high spectral resolution images of the Mount Etna (Sicily, Italy) plume from the North East Crater (NEC), mainly. The LongWave InfraRed (LWIR) hyperspectral imager, hereafter name Hyper-Cam, ranges between 850–1300 cm−1 (7.7–11.8 µm). The LATMOS (Laboratoire Atmosphères Milieux Observations Spatiales) Atmospheric Retrieval Algorithm (LARA), which is used to retrieve the slant column densities (SCD) of SO2, is a robust and a complete radiative transfer model, well adapted to the inversion of ground-based remote measurements. However, the calculation time to process the raw data and retrieve the infrared spectra, which is about seven days for the retrieval of one image of SO2 SCD, remains too high to infer near real-time (NRT) SO2 emission fluxes. A spectral image classification methodology based on two parameters extracting spectral features in the O3 and SO2 emission bands was developed to create a library. The relevance is evaluated in detail through tests. From data acquisition to the generation of SO2 SCD images, this method requires only ~40 s per image, which opens the possibility to infer NRT estimation of SO2 emission fluxes from IR hyperspectral imager measurements.

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Mr. B. Naga Rajesh. "Effective Morphological Transformation and Sub-pixel Classification of Clustered Images." International Journal of New Practices in Management and Engineering 8, no. 01 (March 31, 2019): 08–14. http://dx.doi.org/10.17762/ijnpme.v8i01.74.

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The main aim of this research work is to perform the morphological operations with reduced time complexity and area complexity. Morphological operation is the key element in any image processing. Finding the maximum and minimum using a window of defined size will imply to the morphological dilation and erosion respectively. So the proposed algorithm should be fast in the comparison and sorting, this way the time complexity could be reduced. It’s believed that the anchor concept will fetch this cause. The idea behind this is it fixes a pixel and setting it as the center pixel all the surrounding pixels will be processed. Moreover this is now been implemented for rectangular structuring element. This paper attempts the same for flat and 3D structuring elements. Hyper-spectral Imaging is a developing zone of remote detecting applications. Hyper-spectral pictures incorporate more extravagant and better otherworldly data than the multi-spectral pictures got previously. Hyper-otherworldly pictures are described by an exchange off between the unearthly and spatial resolution. The principle issue of the hyper-ghostly information is the generally low spatial goal. For arrangement, the serious issue brought about by low spatial goal is the blended pixels. Blended pixels alluded to the pixels which are involved by more than one land spread class. In the proposed procedure another strategy is utilized to address the issue of blended pixels and to get a better spatial goal of the land spread characterization maps. The strategy misuses the upsides of both picture bunching methods and phantom dimming calculations, so as to decide the fragmentary plenitudes of the classes at a sub-pixel scale. Spatial regularization by Flank planning method is at last performed to spatially find the got classes at sub-pixel level.

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Arumuga Maria Devi, T., and P. Darwin. "Hyper Spectral Fruit Image Classification for Deep Learning Approaches and Neural Network Techniques." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 30, no. 03 (June 2022): 357–83. http://dx.doi.org/10.1142/s0218488522400116.

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In the field of agro-business technology, computerization contributes to productivity, monetary turnover of events along local viability. The interest in tariffs in addition to the consistency analysis is influenced by the mix of leafy foods. The most tangible aspect of the food derived from the earth is the implementation that influences the need for, the customer’s desires as well as the judgment of the market. Although people may plan and assess, time-concentrated, complex, subjective, costly, and handily influenced by environmental variables is problematic. Subsequently, a shrewd natural product evaluation system is needed. Deep learning has achieved remarkable milestones in the field of conventional computers. In this article, we use deep learning techniques on the topic of hyperspectral image exploration. Unlike traditional machine vision exercises, the only thing to do with a gander is the spatial setting; our proposed solution would use both the spatial setting and the phantom relationship to enhance the hyperspectral image grouping. In clear words, we endorse four new deep learning models, in particular the 3D Convolutionary Neural Network (3D-CNN) and the Repetitive 3D Convolutionary Neural Network (R-3D-CNN) for hyperspectral image recognition.

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Hohmann, Martin, Damaris Hecht, Benjamin Lengenfelder, Moritz Späth, Florian Klämpfl, and Michael Schmidt. "Proof of Principle for Direct Reconstruction of Qualitative Depth Information from Turbid Media by a Single Hyper Spectral Image." Sensors 21, no. 8 (April 19, 2021): 2860. http://dx.doi.org/10.3390/s21082860.

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In medical applications, hyper-spectral imaging is becoming more and more common. It has been shown to be more effective for classification and segmentation than normal RGB imaging because narrower wavelength bands are used, providing a higher contrast. However, until now, the fact that hyper-spectral images also contain information about the three-dimensional structure of turbid media has been neglected. In this study, it is shown that it is possible to derive information about the depth of inclusions in turbid phantoms from a single hyper-spectral image. Here, the depth information is encoded by a combination of scattering and absorption within the phantom. Although scatter-dominated regions increase the backscattering for deep vessels, absorption has the opposite effect. With this argumentation, it makes sense to assume that, under certain conditions, a wavelength is not influenced by the depth of the inclusion and acts as an iso-point. This iso-point could be used to easily derive information about the depth of an inclusion. In this study, it is shown that the iso-point exists in some cases. Moreover, it is shown that the iso-point can be used to obtain precise depth information.

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into, Mur, and Nur Rochmah DPA. "Classification of Hyper spectral Image Using Support Vector Machine and Marker-Controlled Watershed." International Journal of Computer Trends and Technology 27, no. 2 (September 25, 2015): 70–75. http://dx.doi.org/10.14445/22312803/ijctt-v27p112.

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Liu, Q. J., L. H. Jing, L. M. Wang, and Q. Z. Lin. "A Method of Particle Swarm Optimized SVM Hyper-spectral Remote Sensing Image Classification." IOP Conference Series: Earth and Environmental Science 17 (March 18, 2014): 012205. http://dx.doi.org/10.1088/1755-1315/17/1/012205.

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Marwaha, R., A. Kumar, P. L. N. Raju, and Y. V. N. Krishna Murthy. "Target detection algorithm for airborne thermal hyperspectral data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 827–32. http://dx.doi.org/10.5194/isprsarchives-xl-8-827-2014.

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Airborne hyperspectral imaging is constantly being used for classification purpose. But airborne thermal hyperspectral image usually is a challenge for conventional classification approaches. The Telops Hyper-Cam sensor is an interferometer-based imaging system that helps in the spatial and spectral analysis of targets utilizing a single sensor. It is based on the technology of Fourier-transform which yields high spectral resolution and enables high accuracy radiometric calibration. The Hypercam instrument has 84 spectral bands in the 868 cm−1 to 1280 cm−1 region (7.8 μm to 11.5 μm), at a spectral resolution of 6 cm−1 (full-width-half-maximum) for LWIR (long wave infrared) range. Due to the Hughes effect, only a few classifiers are able to handle high dimensional classification task. MNF (Minimum Noise Fraction) rotation is a data dimensionality reducing approach to segregate noise in the data. In this, the component selection of minimum noise fraction (MNF) rotation transformation was analyzed in terms of classification accuracy using constrained energy minimization (CEM) algorithm as a classifier for Airborne thermal hyperspectral image and for the combination of airborne LWIR hyperspectral image and color digital photograph. On comparing the accuracy of all the classified images for airborne LWIR hyperspectral image and combination of Airborne LWIR hyperspectral image with colored digital photograph, it was found that accuracy was highest for MNF component equal to twenty. The accuracy increased by using the combination of airborne LWIR hyperspectral image with colored digital photograph instead of using LWIR data alone.

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Giulietti, Nicola, Silvia Discepolo, Paolo Castellini, and Milena Martarelli. "Correction of Substrate Spectral Distortion in Hyper-Spectral Imaging by Neural Network for Blood Stain Characterization." Sensors 22, no. 19 (September 27, 2022): 7311. http://dx.doi.org/10.3390/s22197311.

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In the recent past, hyper-spectral imaging has found widespread application in forensic science, performing both geometric characterization of biological traces and trace classification by exploiting their spectral emission. Methods proposed in the literature for blood stain analysis have been shown to be effectively limited to collaborative surfaces. This proves to be restrictive in real-case scenarios. The problem of the substrate material and color is then still an open issue for blood stain analysis. This paper presents a novel method for blood spectra correction when contaminated by the influence of the substrate, exploiting a neural network-based approach. Blood stains hyper-spectral images deposited on 12 different substrates for 12 days at regular intervals were acquired via a hyper-spectral camera. The data collected were used to train and test the developed neural network model. Starting from the spectra of a blood stain deposited in a generic substrate, the algorithm at first recognizes whether it is blood or not, then allows to obtain the spectra that the same blood stain, at the same time, would have on a reference white substrate with a mean absolute percentage error of 1.11%. Uncertainty analysis has also been performed by comparing the ground truth reflectance spectra with the predicted ones by the neural model.

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Karthikeyan, A., S. Pavithra, and P. M. Anu. "Detection and Classification of 2D and 3D Hyper Spectral Image using Enhanced Harris Corner Detector." Scalable Computing: Practice and Experience 21, no. 1 (March 19, 2020): 93–100. http://dx.doi.org/10.12694/scpe.v21i1.1625.

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Image classification and visualization is a challenging task in hyper spectral imaging system. To overcome thisissue, here the proposed algorithm incorporates normalized correlation into active corner point of an image representation structure to perform hasty recognition by matching algorithm. Matching algorithms can be of two major categories, based on correlation and based on its features based on correlation and on its feature detection. Proposed algorithms often ignore issues related to scale and orientation and also those to be determined during the localization step. The task of localization involves finding the right region within the search image and passing this region to the verification process. A Harris corner detector is an advancedapproach to detect and extract a huge number of corner points in the input image. We integrate all the extracted corner points into a possible task to locate candidate regions in input image. In terms of detection and classification the proposed method has got better result.

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Awad, Mohamad M., and Marco Lauteri. "Self-Organizing Deep Learning (SO-UNet)—A Novel Framework to Classify Urban and Peri-Urban Forests." Sustainability 13, no. 10 (May 16, 2021): 5548. http://dx.doi.org/10.3390/su13105548.

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Forest-type classification is a very complex and difficult subject. The complexity increases with urban and peri-urban forests because of the variety of features that exist in remote sensing images. The success of forest management that includes forest preservation depends strongly on the accuracy of forest-type classification. Several classification methods are used to map urban and peri-urban forests and to identify healthy and non-healthy ones. Some of these methods have shown success in the classification of forests where others failed. The successful methods used specific remote sensing data technology, such as hyper-spectral and very high spatial resolution (VHR) images. However, both VHR and hyper-spectral sensors are very expensive, and hyper-spectral sensors are not widely available on satellite platforms, unlike multi-spectral sensors. Moreover, aerial images are limited in use, very expensive, and hard to arrange and manage. To solve the aforementioned problems, an advanced method, self-organizing–deep learning (SO-UNet), was created to classify forests in the urban and peri-urban environment using multi-spectral, multi-temporal, and medium spatial resolution Sentinel-2 images. SO-UNet is a combination of two different machine learning technologies: artificial neural network unsupervised self-organizing maps and deep learning UNet. Many experiments have been conducted, and the results showed that SO-UNet overwhelms UNet significantly. The experiments encompassed different settings for the parameters that control the algorithms.

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Xie, Jiaxing, Jiajun Hua, Shaonan Chen, Peiwen Wu, Peng Gao, Daozong Sun, Zhendong Lyu, Shilei Lyu, Xiuyun Xue, and Jianqiang Lu. "HyperSFormer: A Transformer-Based End-to-End Hyperspectral Image Classification Method for Crop Classification." Remote Sensing 15, no. 14 (July 11, 2023): 3491. http://dx.doi.org/10.3390/rs15143491.

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Crop classification of large-scale agricultural land is crucial for crop monitoring and yield estimation. Hyperspectral image classification has proven to be an effective method for this task. Most current popular hyperspectral image classification methods are based on image classification, specifically on convolutional neural networks (CNNs) and recurrent neural networks (RNNs). In contrast, this paper focuses on methods based on semantic segmentation and proposes a new transformer-based approach called HyperSFormer for crop hyperspectral image classification. The key enhancement of the proposed method is the replacement of the encoder in SegFormer with an improved Swin Transformer while keeping the SegFormer decoder. The entire model adopts a simple and uniform transformer architecture. Additionally, the paper introduces the hyper patch embedding (HPE) module to extract spectral and local spatial information from the hyperspectral images, which enhances the effectiveness of the features used as input for the model. To ensure detailed model processing and achieve end-to-end hyperspectral image classification, the transpose padding upsample (TPU) module is proposed for the model’s output. In order to address the problem of insufficient and imbalanced samples in hyperspectral image classification, the paper designs an adaptive min log sampling (AMLS) strategy and a loss function that incorporates dice loss and focal loss to assist model training. Experimental results using three public hyperspectral image datasets demonstrate the strong performance of HyperSFormer, particularly in the presence of imbalanced sample data, complex negative samples, and mixed sample classes. HyperSFormer outperforms state-of-the-art methods, including fast patch-free global learning (FPGA), a spectral–spatial-dependent global learning framework (SSDGL), and SegFormer, by at least 2.7% in the mean intersection over union (mIoU). It also improves the overall accuracy and average accuracy values by at least 0.9% and 0.3%, respectively, and the kappa coefficient by at least 0.011. Furthermore, ablation experiments were conducted to determine the optimal hyperparameter and loss function settings for the proposed method, validating the rationality of these settings and the fusion loss function.

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Shaikh, Muhammad Saad, Keyvan Jaferzadeh, Benny Thörnberg, and Johan Casselgren. "Calibration of a Hyper-Spectral Imaging System Using a Low-Cost Reference." Sensors 21, no. 11 (May 27, 2021): 3738. http://dx.doi.org/10.3390/s21113738.

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In this paper, we present a hyper-spectral imaging system and practical calibration procedure using a low-cost calibration reference made of polytetrafluoroethylene. The imaging system includes a hyperspectral camera and an active source of illumination with a variable spectral distribution of intensity. The calibration reference is used to measure the relative reflectance of any material surface independent of the spectral distribution of light and camera sensitivity. Winter road conditions are taken as a test application, and several spectral images of snow, icy asphalt, dry asphalt, and wet asphalt were made at different exposure times using different illumination spectra. Graphs showing measured relative reflectance for different road conditions support the conclusion that measurements are independent of illumination. Principal component analysis of the acquired spectral data for road conditions shows well separated data clusters, demonstrating the system’s suitability for material classification.

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Hobbs, Steven, Andrew Lambert, Michael J. Ryan, David J. Paull, and John Haythorpe. "Appraisal of Low-Cost Pushbroom Hyper-Spectral Sensor Systems for Material Classification in Reflectance." Sensors 21, no. 13 (June 27, 2021): 4398. http://dx.doi.org/10.3390/s21134398.

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Near infrared (NIR) remote sensing has applications in vegetation analysis as well as geological investigations. For extra-terrestrial applications, this is particularly relevant to Moon, Mars and asteroid exploration, where minerals exhibiting spectral phenomenology between 600 and 800 nm have been identified. Recent progress in the availability of processors and sensors has created the possibility of development of low-cost instruments able to return useful scientific results. In this work, two Raspberry Pi camera types and a panchromatic astronomy camera were trialed within a pushbroom sensor to determine their utility in measuring and processing the spectrum in reflectance. Algorithmic classification of all 15 test materials exhibiting spectral phenomenology between 600 and 800 nm was easily performed. Calibration against a spectrometer considers the effects of the sensor, inherent image processing pipeline and compression. It was found that even the color Raspberry Pi cameras that are popular with STEM applications were able to record and distinguish between most minerals and, contrary to expectations, exploited the infra-red secondary transmissions in the Bayer filter to gain a wider spectral range. Such a camera without a Bayer filter can markedly improve spectral sensitivity but may not be necessary.

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Dyson, Jack, Adriano Mancini, Emanuele Frontoni, and Primo Zingaretti. "Deep Learning for Soil and Crop Segmentation from Remotely Sensed Data." Remote Sensing 11, no. 16 (August 9, 2019): 1859. http://dx.doi.org/10.3390/rs11161859.

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One of the most challenging problems in precision agriculture is to correctly identify and separate crops from the soil. Current precision farming algorithms based on artificially intelligent networks use multi-spectral or hyper-spectral data to derive radiometric indices that guide the operational management of agricultural complexes. Deep learning applications using these big data require sensitive filtering of raw data to effectively drive their hidden layer neural network architectures. Threshold techniques based on the normalized difference vegetation index (NDVI) or other similar metrics are generally used to simplify the development and training of deep learning neural networks. They have the advantage of being natural transformations of hyper-spectral or multi-spectral images that filter the data stream into a neural network, while reducing training requirements and increasing system classification performance. In this paper, to calculate a detailed crop/soil segmentation based on high resolution Digital Surface Model (DSM) data, we propose the redefinition of the radiometric index using a directional mathematical filter. To further refine the analysis, we feed this new radiometric index image of about 3500 × 4500 pixels into a relatively small Convolution Neural Network (CNN) designed for general image pattern recognition at 28 × 28 pixels to evaluate and resolve the vegetation correctly. We show that the result of applying a DSM filter to the NDVI radiometric index before feeding it into a Convolutional Neural Network can potentially improve crop separation hit rate by 65%.

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Pelletier, Charlotte, Geoffrey Webb, and François Petitjean. "Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series." Remote Sensing 11, no. 5 (March 4, 2019): 523. http://dx.doi.org/10.3390/rs11050523.

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Latest remote sensing sensors are capable of acquiring high spatial and spectral Satellite Image Time Series (SITS) of the world. These image series are a key component of classification systems that aim at obtaining up-to-date and accurate land cover maps of the Earth’s surfaces. More specifically, current SITS combine high temporal, spectral and spatial resolutions, which makes it possible to closely monitor vegetation dynamics. Although traditional classification algorithms, such as Random Forest (RF), have been successfully applied to create land cover maps from SITS, these algorithms do not make the most of the temporal domain. This paper proposes a comprehensive study of Temporal Convolutional Neural Networks (TempCNNs), a deep learning approach which applies convolutions in the temporal dimension in order to automatically learn temporal (and spectral) features. The goal of this paper is to quantitatively and qualitatively evaluate the contribution of TempCNNs for SITS classification, as compared to RF and Recurrent Neural Networks (RNNs) —a standard deep learning approach that is particularly suited to temporal data. We carry out experiments on Formosat-2 scene with 46 images and one million labelled time series. The experimental results show that TempCNNs are more accurate than the current state of the art for SITS classification. We provide some general guidelines on the network architecture, common regularization mechanisms, and hyper-parameter values such as batch size; we also draw out some differences with standard results in computer vision (e.g., about pooling layers). Finally, we assess the visual quality of the land cover maps produced by TempCNNs.

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Fan, Yuhai, Yuiqing Wan, Hui Wang, Xingke Yang, Min Liang, Chunjuan Pan, Shaopeng Zhang, Wenbo Wang, and Furong Tan. "Application of an airborne hyper-spectral survey system CASI/SASI in the gold-silver-lead-zinc ore district of Huaniushan, Gansu, China." Geologia Croatica 74, no. 1 (February 28, 2021): 73–83. http://dx.doi.org/10.4154/gc.2021.04.

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The airborne hyper-spectral survey system CASI/SASI, which has an integrated system for gathering both image an spectral data, is at the cutting edge developments in the remote-sensing field. It can be used to directly identify surface objects based on diagnostic spectral characteristics. In this paper, the CASI/SASI were used in the Huaniushan gold-silver-lead-zinc ore district–Gansu to produce a lithologic map, identify altered minerals, and map the mineralized-alteration zones. Radiometric correction, radiometric calibration, atmospheric correction (spectral reconstruction), and geometric corrections were carried out in ENVI to pre-process the measured data. A FieldSpec ® Pro FR portable spectrometer was used to obtain the spectral signatures of all types of rock samples, ore deposits, and mineralized-alteration zones. We extracted and analyzed the spectral characteristics of typical alteration minerals. On the basis of hyper-spectral data, ground-spectral data processing, and comparative analysis of the measured image spectrum, we used the spectral-angle-mapping (SAM) and mixture-tuned matchedfiltering (MTMF) methods to perform hyperspectral-alteration mineral mapping of wall rock and mineralized-alteration-zone hyperspectral identification. Hyperspectral- remote- sensing geological- classification maps were produced as well as distribution maps of all kinds of alteration minerals and mineralized-alteration zones. Based on geological comprehensive analysis and field investigations, the range of mineral alteration was proven to be the same as shown by the remote-sensing imagery. Indications are that airborne hyperspectral- remote-sensing -image CASI/SASI offer good application results and show a promising potential as a tool in geological investigations. The results will provide the basis for hyperspectral remote-sensing prospecting in the same or similar unexplored areas.

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Coppo, Peter, Leandro Chiarantini, and Luciano Alparone. "End-to-End Image Simulator for Optical Imaging Systems: Equations and Simulation Examples." Advances in Optical Technologies 2013 (January 15, 2013): 1–23. http://dx.doi.org/10.1155/2013/295950.

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The theoretical description of a simplified end-to-end software tool for simulation of data produced by optical instruments, starting from either synthetic or airborne hyperspectral data, is described and some simulation examples of hyperspectral and panchromatic images for existing and future design instruments are also reported. High spatial/spectral resolution images with low intrinsic noise and the sensor/mission specifications are used as inputs for the simulations. The examples reported in this paper show the capabilities of the tool for simulating target detection scenarios, data quality assessment with respect to classification performance and class discrimination, impact of optical design on image quality, and 3D modelling of optical performances. The simulator is conceived as a tool (during phase 0/A) for the specification and early development of new Earth observation optical instruments, whose compliance to user’s requirements is achieved through a process of cost/performance trade-off. The Selex Galileo simulator, as compared with other existing image simulators for phase C/D projects of space-borne instruments, implements all modules necessary for a complete panchromatic and hyper spectral image simulation, and it allows excellent flexibility and expandability for new integrated functions because of the adopted IDL-ENVI software environment.

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Singh, Suraj Kumar, Shruti Kanga, and Sudhanshu. "Assessment of Geospatial Approaches Used for Classification of Crops." International Journal of Mathematical, Engineering and Management Sciences 3, no. 3 (September 1, 2018): 271–79. http://dx.doi.org/10.33889/ijmems.2018.3.3-019.

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Harvests distinguishing proof from remotely detected pictures is fundamental because of utilization of remote identifying images as a contribution for rural and monetary arranging by the government and private offices. Accessible satellite sensors like IRS AWIFS, LISS, SPOT 5 and furthermore LANDSAT, MODIS are great wellsprings of multispectral information with various spatial resolutions and Hyperion, Hy-Map, AVIRIS are great wellsprings of hyper-Spectral. The technique for current research is choice of satellite information; utilization of appropriate strategy for arrangement and checking the accuracy. From most recent four decades different specialists have been taking a shot at these issues up to some degree yet at the same time a few difficulties are there like numerous products distinguishing proof, separation of harvests of the same sort this paper gives a general survey of the work done in this vital zone. Multispectral and hyper-spectral images contain spectral data about the crops. Good delicate registering and examination aptitudes are required to order and distinguish the class of enthusiasm from that datasets. Various specialists have worked with supervised and unsupervised arrangement alongside hard classifiers and also delicate processing strategies like fuzzy C mean, support vector machine and they have been discovered distinctive outcomes with various datasets.

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Aghighi, H., J. Trinder, S. Lim, and Y. Tarabalka. "Improved adaptive Markov random field based super-resolution mapping for mangrove tree identification." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-8 (November 27, 2014): 61–68. http://dx.doi.org/10.5194/isprsannals-ii-8-61-2014.

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Traditionally, forest tree crowns are extracted using airborne or spaceborne hyper-/multi-spectral remotely sensed images or pansharpened images. However, these medium/low spatial resolution images suffer from the mixed pixel problem, and the cost to collect very high resolution image collection is high. Moreover, existing feature extraction techniques cannot extract local patterns from medium/low resolution images. Therefore, super-resolution mapping (SRM) techniques, which generate land-cover maps with finer spatial resolution than the original remotely sensed image, can be beneficial for the extraction of forest trees. The SRM methods can improve the quality of information extraction by combining spectral information and spatial context into image classification problems. In this paper we have improved an adaptive Markov random field approach for super-resolution mapping (MRF-SRM) based on spatially adaptive MRF-SPM to overcome the limitation of equal covariance matrices assumption for all classes. We applied the developed method for mangrove tree identification from multispectral image recorded by QuickBird satellite, where we generated a super-resolution map with the panchromatic image spatial resolution of 0.6 m. Moreover, the performance of the proposed technique is evaluated by employing the simulated image with different covariance matrices for each class. Our experimental results have demonstrated that the new adaptive MRF-SRM method has increased the overall accuracy by 5.1% and the termination conditions of this method were satisfied three times faster when compared to the state-of-the-art methods.

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Huang, Yuancheng, Liangpei Zhang, Pingxiang Li, and Yanfei Zhong. "High-resolution hyper-spectral image classification with parts-based feature and morphology profile in urban area." Geo-spatial Information Science 13, no. 2 (January 2010): 111–22. http://dx.doi.org/10.1007/s11806-010-0004-8.

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Wan, Lei, Ma, and Cheng. "The Analysis on Similarity of Spectrum Analysis of Landslide and Bareland through Hyper-Spectrum Image Bands." Water 11, no. 11 (November 17, 2019): 2414. http://dx.doi.org/10.3390/w11112414.

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Landslides of Taiwan occur frequently in high mountain areas. Soil disturbance causes by the earthquake and heavy rainfall of the typhoon seasons often produced the earth and rock to landslide in the upper reaches of the catchment area. Therefore, the landslide near the hillside has an influence on the catchment area. The hyperspectral images are effectively used to monitor the landslide area with the spectral analysis. However, it is rarely studied how to interpret it in the image of the landslide. If there are no elevation data on the slope disaster, it is quite difficult to identify the landslide zone and the bareland area. More specifically, this study used a series of spectrum analysis to identify the difference between them. Therefore, this study conducted a spectrum analysis for the classification of the landslide, bareland, and vegetation area in the mountain area of NanXi District, Tainan City. On the other hand, this study used the following parallel study on Support Vector Machine (SVM) for error matrix and thematic map for comparison. The study simultaneously compared the differences between them. The spectral similarity analysis reaches 85% for testing data, and the SVM approach has 98.3%.

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Li, Huo-Yuan, and Yong-Feng Qi. "A Hyper spectral Images Classification Method Based on Maximum Scatter Discriminant Analysis." ITM Web of Conferences 7 (2016): 02007. http://dx.doi.org/10.1051/itmconf/20160702007.

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Voulodimos, A., K. Fokeas, N. Doulamis, A. Doulamis, and K. Makantasis. "NOISE-TOLERANT HYPERSPECTRAL IMAGE CLASSIFICATION USING DISCRETE COSINE TRANSFORM AND CONVOLUTIONAL NEURAL NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 14, 2020): 1281–87. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-1281-2020.

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Abstract. Hyperspectral image classification has drawn significant attention in the recent years driven by the increasing abundance of sensor-generated hyper- and multi-spectral data, combined with the rapid advancements in the field of machine learning. A vast range of techniques, especially involving deep learning models, have been proposed attaining high levels of classification accuracy. However, many of these approaches significantly deteriorate in performance in the presence of noise in the hyperspectral data. In this paper, we propose a new model that effectively addresses the challenge of noise residing in hyperspectral images. The proposed model, which will be called DCT-CNN, combines the representational power of Convolutional Neural Networks with the noise elimination capabilities introduced by frequency-domain filtering enabled through the Discrete Cosine Transform. In particular, the proposed method entails the transformation of pixel macroblocks to the frequency domain and the discarding of information that corresponds to the higher frequencies in every patch, in which pixel information of abrupt changes and noise often resides. Experiment results in Indian Pines, Salinas and Pavia University datasets indicate that the proposed DCT-CNN constitutes a promising new model for accurate hyperspectral image classification offering robustness to different types of noise, such as Gaussian and salt and pepper noise.

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Dai, Xiaoai, Xuwei He, Shouheng Guo, Senhao Liu, Fujiang Ji, and Huihua Ruan. "Research on hyper-spectral remote sensing image classification by applying stacked de-noising auto-encoders neural network." Multimedia Tools and Applications 80, no. 14 (March 15, 2021): 21219–39. http://dx.doi.org/10.1007/s11042-021-10735-0.

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Gopal, Narendra, and Sivakumar D. "DIMENSIONALITY REDUCTION BASED CLASSIFICATION USING GENERATIVE ADVERSARIAL NETWORKS DATASET GENERATION." ICTACT Journal on Image and Video Processing 13, no. 01 (August 1, 2022): 2786–90. http://dx.doi.org/10.21917/ijivp.2022.0396.

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The term data augmentation refers to an approach that can be used to prevent overfitting in the training dataset, which is where the issue first manifests itself. This is based on the assumption that extra datasets can be improved by include new information that is of use. It is feasible to create an artificially larger training dataset by utilizing methods such as data warping and oversampling. This will allow for the creation of more accurate models. This idea is demonstrated through the application of a variety of different methods, some of which include neural style transfer, adversarial training, and erasure by random erasure, amongst others. By utilizing oversampling augmentations, it is feasible to create synthetic instances that can be incorporated into the training data. This is made possible by the generation of synthetic instances. There are numerous illustrations of this, including image merging, feature space enhancements, and generative adversarial networks, to name a few (GANs). In this paper, we aim to provide evidence that a Generative Adversarial Network can be used to convert regular images into Hyper Spectral Images (HSI). The purpose of the model is to generate data by including a certain amount of unpredictable noise.

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S, Shitharth, Hariprasath Manoharan, Abdulrhman M. Alshareef, Ayman Yafoz, Hassan Alkhiri, and Olfat M. Mirza. "Hyper spectral image classifications for monitoring harvests in agriculture using fly optimization algorithm." Computers and Electrical Engineering 103 (October 2022): 108400. http://dx.doi.org/10.1016/j.compeleceng.2022.108400.

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Jinglei, Tang, Miao Ronghui, Zhang Zhiyong, Xin Jing, and Wang Dong. "Distance-based separability criterion of ROI in classification of farmland hyper-spectral images." International Journal of Agricultural and Biological Engineering 10, no. 5 (2017): 177–85. http://dx.doi.org/10.25165/j.ijabe.20171005.2264.

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Vinokurov, V. O., I. A. Matveeva, Y. A. Khristoforova, O. O. Myakinin, I. A. Bratchenko, L. A. Bratchenko, A. A. Moryatov, et al. "Neural network classifier of hyperspectral images of skin pathologies." Computer Optics 45, no. 6 (November 2021): 879–86. http://dx.doi.org/10.18287/2412-6179-co-832.

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The paper presents results of using a neural network classifier to analyze images of malignant skin lesions obtained using a hyper-spectral camera. Using a three-block neural network of VGG architecture, we conducted the classification of a set of two-dimensional images of melanoma, papilloma and basal cell carcinoma, obtained in the range of 530 – 570 and 600 – 606 nm, characterized by the highest absorption of melanin and hemoglobin. The sufficiency of the inclusion in the training set of two-dimensional images of a limited spectral range is analyzed. The results obtained show significant prospects of using neural network algorithms for processing hyperspectral data for the classification of skin pathologies. With a relatively small set of training data used in the study, the classification accuracy for the three types of neoplasms was as high as 96 %.

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Jamali, A. "A FIT-FOR-PURPOSE ALGORITHM FOR ENVIRONMENTAL MONITORING BASED ON MAXIMUM LIKELIHOOD, SUPPORT VECTOR MACHINE AND RANDOM FOREST." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W7 (March 1, 2019): 25–32. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w7-25-2019.

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Abstract. Due to concerns of recent earth climate changes such as an increase of earth surface temperature and monitoring its effect on earth surface, environmental monitoring is a necessity. Environmental change monitoring in earth sciences needs land use land cover change (LULCC) modelling as a key factor to investigate impact of climate change phenomena such as droughts and floods on earth surface land cover. There are several free and commercial multi/hyper spectral data sources of Earth Observation (EO) satellites including Landsat, Sentinel and Spot. In this paper, for land use land cover modelling (LULCM), image classification of Landsat 8 using several mathematical and machine learning algorithms including Support Vector Machine (SVM), Random Forest (RF), Maximum Likelihood (ML) and a combination of SVM, ML and RF as a fit-for-purpose algorithm are implemented in R programming language and compared in terms of overall accuracy for image classification.

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Mohammed, Bakhtyar Ahmed, and Muzhir Shaban Al-Ani. "Review Research of Medical Image Analysis Using Deep Learning." UHD Journal of Science and Technology 4, no. 2 (August 27, 2020): 75. http://dx.doi.org/10.21928/uhdjst.v4n2y2020.pp75-90.

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In modern globe, medical image analysis significantly participates in diagnosis process. In general, it involves five processes, such as medical image classification, medical image detection, medical image segmentation, medical image registration, and medical image localization. Medical imaging uses in diagnosis process for most of the human body organs, such as brain tumor, chest, breast, colonoscopy, retinal, and many other cases relate to medical image analysis using various modalities. Multi-modality images include magnetic resonance imaging, single photon emission computed tomography (CT), positron emission tomography, optical coherence tomography, confocal laser endoscopy, magnetic resonance spectroscopy, CT, X-ray, wireless capsule endoscopy, breast cancer, papanicolaou smear, hyper spectral image, and ultrasound use to diagnose different body organs and cases. Medical image analysis is appropriate environment to interact with automate intelligent system technologies. Among the intelligent systems deep learning (DL) is the modern one to manipulate medical image analysis processes and processing an image into fundamental components to extract meaningful information. The best model to establish its systems is deep convolutional neural network. This study relied on reviewing of some of these studies because of these reasons; improvements of medical imaging increase demand on automate systems of medical image analysis using DL, in most tested cases, accuracy of intelligent methods especially DL methods higher than accuracy of hand-crafted works. Furthermore, manually works need a lot of time compare to systematic diagnosis.

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John, C. M., and N. Kavya. "Integration of multispectral satellite and hyperspectral field data for aquatic macrophyte studies." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 581–88. http://dx.doi.org/10.5194/isprsarchives-xl-8-581-2014.

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Aquatic macrophytes (AM) can serve as useful indicators of water pollution along the littoral zones. The spectral signatures of various AM were investigated to determine whether species could be discriminated by remote sensing. In this study the spectral readings of different AM communities identified were done using the ASD Fieldspec® Hand Held spectro-radiometer in the wavelength range of 325–1075 nm. The collected specific reflectance spectra were applied to space borne multi-spectral remote sensing data from Worldview-2, acquired on 26th March 2011. The dimensionality reduction of the spectro-radiometric data was done using the technique principal components analysis (PCA). Out of the different PCA axes generated, 93.472 % variance of the spectra was explained by the first axis. The spectral derivative analysis was done to identify the wavelength where the greatest difference in reflectance is shown. The identified wavelengths are 510, 690, 720, 756, 806, 885, 907 and 923 nm. The output of PCA and derivative analysis were applied to Worldview-2 satellite data for spectral subsetting. The unsupervised classification was used to effectively classify the AM species using the different spectral subsets. The accuracy assessment of the results of the unsupervised classification and their comparison were done. The overall accuracy of the result of unsupervised classification using the band combinations Red-Edge, Green, Coastal blue & Red-edge, Yellow, Blue is 100%. The band combinations NIR-1, Green, Coastal blue & NIR-1, Yellow, Blue yielded an accuracy of 82.35 %. The existing vegetation indices and new hyper-spectral indices for the different type of AM communities were computed. Overall, results of this study suggest that high spectral and spatial resolution images provide useful information for natural resource managers especially with regard to the location identification and distribution mapping of macrophyte species and their communities.

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Wang, Wenxuan, Leiming Liu, Tianxiang Zhang, Jiachen Shen, Jing Wang, and Jiangyun Li. "Hyper-ES2T: Efficient Spatial–Spectral Transformer for the classification of hyperspectral remote sensing images." International Journal of Applied Earth Observation and Geoinformation 113 (September 2022): 103005. http://dx.doi.org/10.1016/j.jag.2022.103005.

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Honkavaara, E., R. Näsi, R. Oliveira, N. Viljanen, J. Suomalainen, E. Khoramshahi, T. Hakala, et al. "USING MULTITEMPORAL HYPER- AND MULTISPECTRAL UAV IMAGING FOR DETECTING BARK BEETLE INFESTATION ON NORWAY SPRUCE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 429–34. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-429-2020.

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Abstract. Various biotic and abiotic stresses are threatening forests. Modern remote sensing technologies provide powerful means for monitoring forest health, and provide a sustainable basis for forest management and protection. The objective of this study was to develop unmanned aerial vehicle (UAV) based spectral remote sensing technologies for tree health assessment, particularly, for detecting the European spruce bark beetle (Ips typographus L.) attacks. Our focus was to study the early detection of bark beetle attack, i.e. the “green attack” phase. This is a difficult remote sensing task as there does not exist distinct symptoms that can be observed by the human eye. A test site in a Norway spruce (Picea abies (L.) Karst.) dominated forest was established in Southern-Finland in summer 2019. It had an emergent bark beetle outbreak and it was also suffering from other stress factors, especially the root and butt rot (Heterobasidion annosum (Fr.) Bref. s. lato). Altogether seven multitemporal hyper- and multispectral UAV remote sensing datasets were captured from the area in August to October 2019. Firstly, we explored deterioration of tree health and development of spectral symptoms using a time series of UAV hyperspectral imagery. Secondly, we trained assessed a machine learning model for classification of spruce health into classes of “bark beetle green attack”, “root-rot”, and “healthy”. Finally, we demonstrated the use of the model in tree health mapping in a test area. Our preliminary results were promising and indicated that the green attack phase could be detected using the accurately calibrated spectral image data.

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Malinee, Rachane, Dimitris Stratoulias, and Narissara Nuthammachot. "Detection of Oil Palm Disease in Plantations in Krabi Province, Thailand with High Spatial Resolution Satellite Imagery." Agriculture 11, no. 3 (March 16, 2021): 251. http://dx.doi.org/10.3390/agriculture11030251.

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Oil palm (Elaeis guineensis) trees are an important contributor of recent economic development in Southeast Asia. The high product yield, and the consequent high profitability, has led to a widespread expansion of plantations in the greater region. However, oil palms are susceptible to diseases that can have a detrimental effect. In this study we use hyper- and multi-spectral remote sensing to detect diseased oil palm trees in Krabi province, Thailand. Proximate spectroscopic measurements were used to identify and discern differences in leaf spectral radiance; the results indicate a relatively higher radiance in visible and near-infrared for the healthy leaves in comparison to the diseased. From a total of 113 samples for which the geolocation and the hyperspectral radiance were recorded, 59 and 54 samples were healthy and diseased oil palm trees, respectively. Moreover, a WorldView-2 satellite image was used to investigate the usability of traditional vegetation indices and subsequently detecting diseased oil palm trees. The results show that the overall maximum likelihood classification accuracy is 85.98%, the Kappa coefficient 0.71 and the producer’s accuracy for healthy and diseased oil palm trees 83.33 and 78.95, respectively. We conclude that high spatial and spectral resolutions can play a vital role in monitoring diseases in oil palm plantations.

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Notesco, Weksler, and Ben-Dor. "Mineral Classification of Soils Using Hyperspectral Longwave Infrared (LWIR) Ground-Based Data." Remote Sensing 11, no. 12 (June 16, 2019): 1429. http://dx.doi.org/10.3390/rs11121429.

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Soil mineralogy is an important factor affecting chemical and physical processes in the soil. Most common minerals in soils—quartz, clay minerals and carbonates—present fundamental spectral features in the longwave infrared (LWIR) region. The current study presents a procedure for determining the soil mineralogy from the surface emissivity spectrum. Ground-based hyperspectral LWIR images of 90 Israeli soil samples were acquired with the Telops Hyper-Cam sensor, and the emissivity spectrum of each sample was calculated. Mineral-related emissivity features were identified and used to create indicants and indices to determine the content of quartz, clay minerals, and carbonates in the soil in a semi-quantitative manner—from more to less abundant minerals. The resultant mineral content was in good agreement with the mineralogy derived from chemical analyses.

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Idoughi, Ramzi, Thomas H. G. Vidal, Pierre-Yves Foucher, Marc-André Gagnon, and Xavier Briottet. "Background Radiance Estimation for Gas Plume Quantification for Airborne Hyperspectral Thermal Imaging." Journal of Spectroscopy 2016 (2016): 1–17. http://dx.doi.org/10.1155/2016/5428762.

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Hyperspectral imaging in the long-wave infrared (LWIR) is a mean that is proving its worth in the characterization of gaseous effluent. Indeed the spectral and spatial resolution of acquisition instruments is steadily decreasing, making the gases characterization increasingly easy in the LWIR domain. The majority of literature algorithms exploit the plume contribution to the radiance corresponding to the difference of radiance between the plume-present and plume-absent pixels. Nevertheless, the off-plume radiance is unobservable using a single image. In this paper, we propose a new method to retrieve trace gas concentration from airborne infrared hyperspectral data. More particularly the outlined method improves the existing background radiance estimation approach to deal with heterogeneous scenes corresponding to industrial scenes. It consists in performing a classification of the scene and then applying a principal components analysis based method to estimate the background radiance on each cluster stemming from the classification. In order to determine the contribution of the classification to the background radiance estimation, we compared the two approaches on synthetic data and Telops Fourier Transform Spectrometer (FTS) Imaging Hyper-Cam LW airborne acquisition above ethylene release. We finally show ethylene retrieved concentration map and estimate flow rate of the ethylene release.

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Görlich, Florian, Elias Marks, Anne-Katrin Mahlein, Kathrin König, Philipp Lottes, and Cyrill Stachniss. "UAV-Based Classification of Cercospora Leaf Spot Using RGB Images." Drones 5, no. 2 (May 5, 2021): 34. http://dx.doi.org/10.3390/drones5020034.

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Plant diseases can impact crop yield. Thus, the detection of plant diseases using sensors that can be mounted on aerial vehicles is in the interest of farmers to support decision-making in integrated pest management and to breeders for selecting tolerant or resistant genotypes. This paper investigated the detection of Cercospora leaf spot (CLS), caused by Cercospora beticola in sugar beet using RGB imagery. We proposed an approach to tackle the CLS detection problem using fully convolutional neural networks, which operate directly on RGB images captured by a UAV. This efficient approach does not require complex multi- or hyper-spectral sensors, but provides reliable results and high sensitivity. We provided a detection pipeline for pixel-wise semantic segmentation of CLS symptoms, healthy vegetation, and background so that our approach can automatically quantify the grade of infestation. We thoroughly evaluated our system using multiple UAV datasets recorded from different sugar beet trial fields. The dataset consisted of a training and a test dataset and originated from different fields. We used it to evaluate our approach under realistic conditions and analyzed its generalization capabilities to unseen environments. The obtained results correlated to visual estimation by human experts significantly. The presented study underlined the potential of high-resolution RGB imaging and convolutional neural networks for plant disease detection under field conditions. The demonstrated procedure is particularly interesting for applications under practical conditions, as no complex and cost-intensive measuring system is required.

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Park, Keunho, Young ki Hong, Gook hwan Kim, and Joonwhoan Lee. "Classification of apple leaf conditions in hyper-spectral images for diagnosis of Marssonina blotch using mRMR and deep neural network." Computers and Electronics in Agriculture 148 (May 2018): 179–87. http://dx.doi.org/10.1016/j.compag.2018.02.025.

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Journal articles on the topic 'HYPER SPECTRAL IMAGE CLASSIFICATION'

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