Relevant bibliographies by topics / Hyperspectral imaging, Landmine detection, Remote sensing

Academic literature on the topic 'Hyperspectral imaging, Landmine detection, Remote sensing'

Author: Grafiati
Published: 14 March 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Hyperspectral imaging, Landmine detection, Remote sensing.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Hyperspectral imaging, Landmine detection, Remote sensing"

1

Manley, Paul V., Vasit Sagan, Felix B. Fritschi, and Joel G. Burken. "Remote Sensing of Explosives-Induced Stress in Plants: Hyperspectral Imaging Analysis for Remote Detection of Unexploded Threats." Remote Sensing 11, no. 15 (August 5, 2019): 1827. http://dx.doi.org/10.3390/rs11151827.

Full text
Abstract:
Explosives contaminate millions of hectares from various sources (partial detonations, improper storage, and release from production and transport) that can be life-threatening, e.g., landmines and unexploded ordnance. Exposure to and uptake of explosives can also negatively impact plant health, and these factors can be can be remotely sensed. Stress induction was remotely sensed via a whole-plant hyperspectral imaging system as two genotypes of Zea mays, a drought-susceptible hybrid and a drought-tolerant hybrid, and a forage Sorghum bicolor were grown in a greenhouse with one control group, one group maintained at 60% soil field capacity, and a third exposed to 250 mg kg−1 Royal Demolition Explosive (RDX). Green-Red Vegetation Index (GRVI), Photochemical Reflectance Index (PRI), Modified Red Edge Simple Ratio (MRESR), and Vogelmann Red Edge Index 1 (VREI1) were reduced due to presence of explosives. Principal component analyses of reflectance indices separated plants exposed to RDX from control and drought plants. Reflectance of Z. mays hybrids was increased from RDX in green and red wavelengths, while reduced in near-infrared wavelengths. Drought Z. mays reflectance was lower in green, red, and NIR regions. S. bicolor grown with RDX reflected more in green, red, and NIR wavelengths. The spectra and their derivatives will be beneficial for developing explosive-specific indices to accurately identify plants in contaminated soil. This study is the first to demonstrate potential to delineate subsurface explosives over large areas using remote sensing of vegetation with aerial-based hyperspectral systems.
APA, Harvard, Vancouver, ISO, and other styles
2

Shukla, Alpana, and Rajsi Kot. "An Overview of Hyperspectral Remote Sensing and its applications in various Disciplines." IRA-International Journal of Applied Sciences (ISSN 2455-4499) 5, no. 2 (December 12, 2016): 85. http://dx.doi.org/10.21013/jas.v5.n2.p4.

Full text
Abstract:
<div><p><em>Recent advances in remote sensing and geographic information has opened new directions for the development of hyperspectral sensors. Hyperspectral remote sensing, also known as imaging spectroscopy is a new technology. Hyperspectral imaging is currently being investigated by researchers and scientists for the detection and identification of vegetation, minerals, different objects and background.</em><em> Hyperspectral remote sensing combines imaging and spectroscopy in a single system which often includes large data sets and requires new processing methods. Hyperspectral data sets are generally made of about 100 to 200 spectral bands of relatively narrow bandwidths (5-10 nm), whereas, multispectral data sets are usually composed of about 5 to 10 bands of relatively large bandwidths (70-400 nm). Hyperspectral imagery is collected as a data cube with spatial information collected in the X-Y plane, and spectral information represented in the Z-direction. </em><em>Hyperspectral remote sensing is applicable in many different disciplines. It was originally developed for mining and geology; it has now spread into fields such as agriculture and forestry, ecology, coastal zone management, geology and mineral exploration. This paper presents an overview of hyperspectral imaging, data exploration and analysis, applications in various disciplines, advantages and disadvantages and future aspects of the technique.</em></p></div>
APA, Harvard, Vancouver, ISO, and other styles
3

Liu, Hong, Tao Yu, Bingliang Hu, Xingsong Hou, Zhoufeng Zhang, Xiao Liu, Jiacheng Liu, et al. "UAV-Borne Hyperspectral Imaging Remote Sensing System Based on Acousto-Optic Tunable Filter for Water Quality Monitoring." Remote Sensing 13, no. 20 (October 12, 2021): 4069. http://dx.doi.org/10.3390/rs13204069.

Full text
Abstract:
Unmanned aerial vehicle (UAV) hyperspectral remote sensing technologies have unique advantages in high-precision quantitative analysis of non-contact water surface source concentration. Improving the accuracy of non-point source detection is a difficult engineering problem. To facilitate water surface remote sensing, imaging, and spectral analysis activities, a UAV-based hyperspectral imaging remote sensing system was designed. Its prototype was built, and laboratory calibration and a joint air–ground water quality monitoring activity were performed. The hyperspectral imaging remote sensing system of UAV comprised a light and small UAV platform, spectral scanning hyperspectral imager, and data acquisition and control unit. The spectral principle of the hyperspectral imager is based on the new high-performance acousto-optic tunable (AOTF) technology. During laboratory calibration, the spectral calibration of the imaging spectrometer and image preprocessing in data acquisition were completed. In the UAV air–ground joint experiment, combined with the typical water bodies of the Yangtze River mainstream, the Three Gorges demonstration area, and the Poyang Lake demonstration area, the hyperspectral data cubes of the corresponding water areas were obtained, and geometric registration was completed. Thus, a large field-of-view mosaic and water radiation calibration were realized. A chlorophyl-a (Chl-a) sensor was used to test the actual water control points, and 11 traditional Chl-a sensitive spectrum selection algorithms were analyzed and compared. A random forest algorithm was used to establish a prediction model of water surface spectral reflectance and water quality parameter concentration. Compared with the back propagation neural network, partial least squares, and PSO-LSSVM algorithms, the accuracy of the RF algorithm in predicting Chl-a was significantly improved. The determination coefficient of the training samples was 0.84; root mean square error, 3.19 μg/L; and mean absolute percentage error, 5.46%. The established Chl-a inversion model was applied to UAV hyperspectral remote sensing images. The predicted Chl-a distribution agreed with the field observation results, indicating that the UAV-borne hyperspectral remote sensing water quality monitoring system based on AOTF is a promising remote sensing imaging spectral analysis tool for water.
APA, Harvard, Vancouver, ISO, and other styles
4

Li, Yanyi, Jian Wang, Tong Gao, Qiwen Sun, Liguo Zhang, and Mingxiu Tang. "Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images." Computational Intelligence and Neuroscience 2020 (September 1, 2020): 1–13. http://dx.doi.org/10.1155/2020/8886932.

Full text
Abstract:
To overcome the difficulty of automating and intelligently classifying the ground features in remote-sensing hyperspectral images, machine learning methods are gradually introduced into the process of remote-sensing imaging. First, the PaviaU, Botswana, and Cuprite hyperspectral datasets are selected as research subjects in this study, and the objective is to process remote-sensing hyperspectral images via machine learning to realize the automatic and intelligent classification of features. Then, the basic principles of the support vector machine (SVM) and extreme learning machine (ELM) classification algorithms are introduced, and they are applied to the datasets. Next, by adjusting the parameter estimates using a restricted Boltzmann machine (RBM), a new terrain classification model of hyperspectral images that is based on a deep belief network (DBN) is constructed. Next, the SVM, ELM, and DBN classification algorithms for hyperspectral image terrain classification are analysed and compared in terms of accuracy and consistency. The results demonstrate that the average detection accuracies of ELM on the three datasets are 89.54%, 96.14%, and 96.28%, and the Kappa coefficient values are 0.832, 0.963, and 0.924; the average detection accuracies of SVM are 88.90%, 92.11%, and 91.68%, and the Kappa coefficient values are 0.768, 0.913, and 0.944; the average detection accuracies of the DBN classification model are 92.36%, 97.31%, and 98.84%, and the Kappa coefficient values are 0.883, 0.944, and 0.972. The results also demonstrate that the classification accuracy of the DBN algorithm exceeds those of the previous two methods because it fully utilizes the spatial and spectral information of hyperspectral remote-sensing images. In summary, the DBN algorithm that is proposed in this study has high application value in object classification for remote-sensing hyperspectral images.
APA, Harvard, Vancouver, ISO, and other styles
5

Chang, Chein-I., Meiping Song, Junping Zhang, and Chao-Cheng Wu. "Editorial for Special Issue “Hyperspectral Imaging and Applications”." Remote Sensing 11, no. 17 (August 27, 2019): 2012. http://dx.doi.org/10.3390/rs11172012.

Full text
Abstract:
Due to advent of sensor technology, hyperspectral imaging has become an emerging technology in remote sensing. Many problems, which cannot be resolved by multispectral imaging, can now be solved by hyperspectral imaging. The aim of this Special Issue “Hyperspectral Imaging and Applications” is to publish new ideas and technologies to facilitate the utility of hyperspectral imaging in data exploitation and to further explore its potential in different applications. This Special Issue has accepted and published 25 papers in various areas, which can be organized into 7 categories, Data Unmixing, Spectral variability, Target Detection, Hyperspectral Image Classification, Band Selection, Data Fusion, Applications.
APA, Harvard, Vancouver, ISO, and other styles
6

Qian, Liyong, Decheng Wu, Dong Liu, Shalei Song, Shuo Shi, Wei Gong, and Le Wang. "Parameter Simulation and Design of an Airborne Hyperspectral Imaging LiDAR System." Remote Sensing 13, no. 24 (December 17, 2021): 5123. http://dx.doi.org/10.3390/rs13245123.

Full text
Abstract:
With continuous technological development, the future development trend of LiDAR in the field of remote sensing and mapping is to obtain the elevation and spectral information of ground targets simultaneously. Airborne hyperspectral imaging LiDAR inherits the advantages of active and passive remote sensing detection. This paper presents a simulation method to determine the design parameters of an airborne hyperspectral imaging LiDAR system. In accordance with the hyperspectral imaging LiDAR equation and optical design principles, the atmospheric transmission model and the reflectance spectrum of specific ground targets are utilized. The design parameters and laser emission spectrum of the hyperspectral LiDAR system are considered, and the signal-to-noise ratio of the system is obtained through simulation. Without considering the effect of detector gain and electronic amplification on the signal-to-noise ratio, three optical fibers are coupled into a detection channel, and the power spectral density emitted by the supercontinuum laser is simulated by assuming that the signal-to-noise ratio is equal to 1. The power spectral density emitted by the laser must not be less than 15 mW/nm in the shortwave direction. During the simulation process, the design parameters of the hyperspectral LiDAR system are preliminarily demonstrated, and the feasibility of the hyperspectral imaging LiDAR system design is theoretically guaranteed in combination with the design requirements of the supercontinuum laser. The spectral resolution of a single optical fiber of the hyperspectral LiDAR system is set to 2.5 nm. In the actual prototype system, multiple optical fibers can be coupled into a detection channel in accordance with application needs to further improve the signal-to-noise ratio of hyperspectral LiDAR system detection.
APA, Harvard, Vancouver, ISO, and other styles
7

Jha, Sudhanshu Shekhar, and Rama Rao Nidamanuri. "Gudalur Spectral Target Detection (GST-D): A New Benchmark Dataset and Engineered Material Target Detection in Multi-Platform Remote Sensing Data." Remote Sensing 12, no. 13 (July 3, 2020): 2145. http://dx.doi.org/10.3390/rs12132145.

Full text
Abstract:
Target detection in remote sensing imagery, mapping of sparsely distributed materials, has vital applications in defense security and surveillance, mineral exploration, agriculture, environmental monitoring, etc. The detection probability and the quality of retrievals are functions of various parameters of the sensor, platform, target–background dynamics, targets’ spectral contrast, and atmospheric influence. Generally, target detection in remote sensing imagery has been approached using various statistical detection algorithms with an assumption of linearity in the image formation process. Knowledge on the image acquisition geometry, and spectral features and their stability across different imaging platforms is vital for designing a spectral target detection system. We carried out an integrated target detection experiment for the detection of various artificial target materials. As part of this work, we acquired a benchmark multi-platform hyperspectral and multispectral remote sensing dataset named as ‘Gudalur Spectral Target Detection (GST-D)’ dataset. Positioning artificial targets on different surface backgrounds, we acquired remote sensing data by terrestrial, airborne, and space-borne sensors on 20th March 2018. Various statistical and subspace detection algorithms were applied on the benchmark dataset for the detection of targets, considering the different sources of reference target spectra, background, and the spectral continuity across the platforms. We validated the detection results using the receiver operation curve (ROC) for different cases of detection algorithms and imaging platforms. Results indicate, for some combinations of algorithms and imaging platforms, consistent detection of specific material targets with a detection rate of about 80% at a false alarm rate between 10−2 to 10−3. Target detection in satellite imagery using reference target spectra from airborne hyperspectral imagery match closely with the satellite imagery derived reference spectra. The ground-based in-situ reference spectra offer a quantifiable detection in airborne or satellite imagery. However, ground-based hyperspectral imagery has also provided an equivalent target detection in the airborne and satellite imagery paving the way for rapid acquisition of reference target spectra. The benchmark dataset generated in this work is a valuable resourcefor addressing intriguing questions in target detection using hyperspectral imagery from a realistic landscape perspective.
APA, Harvard, Vancouver, ISO, and other styles
8

Huang, Zuo Wei, Shu Guang Wu, and Tao Xin Zhang. "A Approach to Change Detection for HR Image." Advanced Materials Research 971-973 (June 2014): 1449–53. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1449.

Full text
Abstract:
Hyperspectral remote sensing is the multi-dimensional information obtaining technology,which combines target detection and spectral imaging technology together, In order to accord with the condition of hyperspectral imagery，the paper developed an optimized ICA algorithm for change detection to describe the statistical distribution of the data. By processing these abundance maps, change of different classes of objects can be obtained..A approach is capable of self-adaptation, and can be applied to hyperspectral images with different characteristics. Experiment results demonstrate that the ICA-based hyperspectral change detection performs better than other traditional methods with a high detection rate and a low false detection rate.
APA, Harvard, Vancouver, ISO, and other styles
9

Wei, Jiaojiao, and Xiaofei Wang. "An Overview on Linear Unmixing of Hyperspectral Data." Mathematical Problems in Engineering 2020 (August 25, 2020): 1–12. http://dx.doi.org/10.1155/2020/3735403.

Full text
Abstract:
Hyperspectral remote sensing technology has a strong capability for ground object detection due to the low spatial resolution of hyperspectral imaging spectrometers. A single pixel that leads to a hyperspectral remote sensing image usually contains more than one feature coverage type, resulting in a mixed pixel. The existence of a mixed pixel affects the accuracy of the ground object identification and classification and hinders the application and development of hyperspectral technology. For the problem of unmixing of mixed pixels in hyperspectral images (HSIs), the linear mixing model can model the mixed pixels well. Through the collation of nearly five years of the literature, this paper introduces the development status and problems of linear unmixing models from four aspects: geometric method, nonnegative matrix factorization (NMF), Bayesian method, and sparse unmixing.
APA, Harvard, Vancouver, ISO, and other styles
10

Sulaiman, Nursyazyla, Nik Norasma Che’Ya, Muhammad Huzaifah Mohd Roslim, Abdul Shukor Juraimi, Nisfariza Mohd Noor, and Wan Fazilah Fazlil Ilahi. "The Application of Hyperspectral Remote Sensing Imagery (HRSI) for Weed Detection Analysis in Rice Fields: A Review." Applied Sciences 12, no. 5 (March 1, 2022): 2570. http://dx.doi.org/10.3390/app12052570.

Full text
Abstract:
Weeds are found on every cropland across the world. Weeds compete for light, water, and nutrients with attractive plants, introduce illnesses or viruses, and attract harmful insects and pests, resulting in yield loss. New weed detection technologies have been developed in recent years to increase weed detection speed and accuracy, resolving the contradiction between the goals of enhancing soil health and achieving sufficient weed control for profitable farming. In recent years, a variety of platforms, such as satellites, airplanes, unmanned aerial vehicles (UAVs), and close-range platforms, have become more commonly available for gathering hyperspectral images with varying spatial, temporal, and spectral resolutions. Plants must be divided into crops and weeds based on their species for successful weed detection. Therefore, hyperspectral image categorization also has become popular since the development of hyperspectral image technology. Unmanned aerial vehicle (UAV) hyperspectral imaging techniques have recently emerged as a valuable tool in agricultural remote sensing, with tremendous promise for weed detection and species separation. Hence, this paper will review the weeds problem in rice fields in Malaysia and focus on the application of hyperspectral remote sensing imagery (HRSI) for weed detection with algorithms and modelling employed for weeds discrimination analysis.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Hyperspectral imaging, Landmine detection, Remote sensing"

1

Farrell, Michael D. Jr. "Analysis of Modeling, Training, and Dimension Reduction Approaches for Target Detection in Hyperspectral Imagery." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7505.

Full text
Abstract:
Whenever a new sensor or system comes online, engineers and analysts responsible for processing the measured data turn first to methods that are tried and true on existing systems. This is a natural, if not wholly logical approach, and is exactly what has happened in the advent of hyperspectral imagery (HSI) exploitation. However, a closer look at the assumptions made by the approaches published in the literature has not been undertaken. This thesis analyzes three key aspects of HSI exploitation: statistical data modeling, covariance estimation from training data, and dimension reduction. These items are part of standard processing schemes, and it is worthwhile to understand and quantify the impact that various assumptions for these items have on target detectability and detection statistics. First, the accuracy and applicability of the standard Gaussian (i.e., Normal) model is evaluated, and it is shown that the elliptically contoured t-distribution (EC-t) sometimes offers a better statistical model for HSI data. A finite mixture approach for EC-t is developed in which all parameters are estimated simultaneously without a priori information. Then the effects of making a poor covariance estimate are shown by including target samples in the training data. Multiple test cases with ground targets are explored. They show that the magnitude of the deleterious effect of covariance contamination on detection statistics depends on algorithm type and target signal characteristics. Next, the two most widely used dimension reduction approaches are tested. It is demonstrated that, in many cases, significant dimension reduction can be achieved with only a minor loss in detection performance. In addition, a concise development of key HSI detection algorithms is presented, and the state-of-the-art in adaptive detectors is benchmarked for land mine targets. Methods for detection and identification of airborne gases using hyperspectral imagery are discussed, and this application is highlighted as an excellent opportunity for future work.
APA, Harvard, Vancouver, ISO, and other styles
2

Hislop, Gregory Francis. "Diffraction Tomographic Imaging of Shallowly Buried Targets using Ground Penetrating Radar." Thesis, Queensland University of Technology, 2005. https://eprints.qut.edu.au/16125/1/Gregory_Hislop_Thesis.pdf.

Full text
Abstract:
The problem of subsurface imaging with Ground Penetrating Radar (GPR) is a challenging one. Due to the low-pass nature of soil sensors must utilise wave-lengths that are of the same order of magnitude as the object being imaged. This makes imaging difficult as straight ray approximations commonly used in higher frequency applications cannot be used. The problem becomes even more challenging when the target is shallowly buried as in this case the ground surface reflection and the near-field parameters of the radar need to be considered. This thesis has investigated the problem of imaging shallowly buried targets with GPR. Two distinct problems exist in this field radar design and the design of inverse scattering techniques. This thesis focuses on the design of inverse scattering techniques capable of taking the electric field measurements from the receiver and providing accurate images of the scatterer in real time. The thesis commences with a brief introduction to GPR theory. It then provides an extensive review of linear inverse scattering techniques applied to raw GPR data. As a result of this review the thesis draws the conclusion that, due to its strong foundations in Maxwell's equations, diffraction tomography is the most appropriate approach for imaging shallowly buried targets with GPR. A three-dimensional diffraction tomographic technique is then developed. This algorithm forms the primary contribution of the thesis. The novel diffraction tomography technique improves on its predecessors by catering for shallowly buried targets, significant antenna heights and evanescent waves. This is also the first diffraction tomography technique to be derived for a range of antenna structures. The advantages of the novel technique are demonstrated first mathematically then on synthetic and finally practical data. The algorithm is shown to be of high practical value by producing accurate images of buried targets in real time.
APA, Harvard, Vancouver, ISO, and other styles
3

Hislop, Gregory Francis. "Diffraction Tomographic Imaging of Shallowly Buried Targets using Ground Penetrating Radar." Queensland University of Technology, 2005. http://eprints.qut.edu.au/16125/.

Full text
Abstract:
The problem of subsurface imaging with Ground Penetrating Radar (GPR) is a challenging one. Due to the low-pass nature of soil sensors must utilise wave-lengths that are of the same order of magnitude as the object being imaged. This makes imaging difficult as straight ray approximations commonly used in higher frequency applications cannot be used. The problem becomes even more challenging when the target is shallowly buried as in this case the ground surface reflection and the near-field parameters of the radar need to be considered. This thesis has investigated the problem of imaging shallowly buried targets with GPR. Two distinct problems exist in this field radar design and the design of inverse scattering techniques. This thesis focuses on the design of inverse scattering techniques capable of taking the electric field measurements from the receiver and providing accurate images of the scatterer in real time. The thesis commences with a brief introduction to GPR theory. It then provides an extensive review of linear inverse scattering techniques applied to raw GPR data. As a result of this review the thesis draws the conclusion that, due to its strong foundations in Maxwell's equations, diffraction tomography is the most appropriate approach for imaging shallowly buried targets with GPR. A three-dimensional diffraction tomographic technique is then developed. This algorithm forms the primary contribution of the thesis. The novel diffraction tomography technique improves on its predecessors by catering for shallowly buried targets, significant antenna heights and evanescent waves. This is also the first diffraction tomography technique to be derived for a range of antenna structures. The advantages of the novel technique are demonstrated first mathematically then on synthetic and finally practical data. The algorithm is shown to be of high practical value by producing accurate images of buried targets in real time.
APA, Harvard, Vancouver, ISO, and other styles
4

Hamid, Muhammed Hamed. "Hyperspectral Image Generation, Processing and Analysis." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-5905.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "Hyperspectral imaging, Landmine detection, Remote sensing"

1

Hyperspectral imaging: Techniques for spectral detection and classification. New York: Kluwer Academic/Plenum Publishers, 2003.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Chang, Chein-I. Hyperspectral Imaging: Techniques for Spectral Detection and Classification. Springer, 2003.

Find full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Hyperspectral imaging, Landmine detection, Remote sensing"

1

Bhagat, Amol Prakash, and Sandip Kendre. "Quantum Discrete Transform for Real-Time Object Detection in Today's Smart Era." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 178–90. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6697-1.ch010.

Full text
Abstract:
The application of quantum technology for remote sensing has been considered for at least the last 20 years. An active imaging information transmission technology for satellite-borne quantum remote sensing is proposed, providing solutions and a technical basis for realizing active imaging technology relying on quantum mechanics principles. Quantum technology is also used in interferometric synthetic aperture radars. A residue connection problem in the phase unwrapping procedure as quadratic unconstrained binary optimization problem can be solved by using the D-Wave quantum annealer. A quantum annealer application has been explored in the past for subset feature selection and the classification of hyperspectral images. In this chapter, quantum discrete transform is proposed and analyzed, which can be used for real-time object detection in distinct fields.
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Hyperspectral imaging, Landmine detection, Remote sensing"

1

Ozturk, Safak, Yunus Emre Esin, and Yusuf Artan. "Object detection in rural areas using hyperspectral imaging." In SPIE Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2015. http://dx.doi.org/10.1117/12.2195326.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Bolton, Jeremy, and Paul Gader. "Application of random set-based clustering to landmine detection with hyperspectral imagery." In 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2007. http://dx.doi.org/10.1109/igarss.2007.4423227.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Rotman, Stanley, and Hanoch Shalev. "Evaluating hyperspectral imaging change detection methods." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8127360.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Frontera-Pons, J., M. A. Veganzones, S. Velasco-Forero, F. Pascal, J. P. Ovarlez, and J. Chanussot. "Robust anomaly detection in Hyperspectral Imaging." In IGARSS 2014 - 2014 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2014. http://dx.doi.org/10.1109/igarss.2014.6947518.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Kerekes, John P. "Hyperspectral remote sensing subpixel object detection performance." In 2011 IEEE Applied Imagery Pattern Recognition Workshop: Imaging for Decision Making (AIPR 2011). IEEE, 2011. http://dx.doi.org/10.1109/aipr.2011.6176366.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Ozturk, Safak, Yusuf Artan, Yunus Emre Esin, Mustafa Yaman, and Ahmet Erdem. "Semi-supervised gas detection in hyperspectral imaging." In IGARSS 2015 - 2015 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2015. http://dx.doi.org/10.1109/igarss.2015.7325802.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Tang, Shaofan. "A high spectral remote sensing method for hyperspectral imaging." In Fifth Symposium on Novel Optoelectronic Detection Technology and Application, edited by Qifeng Yu, Wei Huang, and You He. SPIE, 2019. http://dx.doi.org/10.1117/12.2521531.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Liu, Yangyang, Qunbo Lv, Jianwei Wang, Linlin Pei, and Weiyan Li. "UAV-based hyperspectral imaging detection for explosives and contaminants." In Image and Signal Processing for Remote Sensing XXV, edited by Lorenzo Bruzzone, Francesca Bovolo, and Jon Atli Benediktsson. SPIE, 2019. http://dx.doi.org/10.1117/12.2532716.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Nakaya, Daiki, Hiroki Yanagida, Shin Satori, Tomonori Ito, and Yusuke Takeuchi. "Small real time detection satellites for MDA using hyperspectral imaging." In Image and Signal Processing for Remote Sensing, edited by Lorenzo Bruzzone, Francesca Bovolo, and Jon Atli Benediktsson. SPIE, 2017. http://dx.doi.org/10.1117/12.2278242.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Song, Mei-ping, Ming Chang, Ju-bai An, Jian Huang, and Bin Lin. "Active contour segmentation for hyperspectral oil spill remote sensing." In ISPDI 2013 - Fifth International Symposium on Photoelectronic Detection and Imaging, edited by Lifu Zhang and Jianfeng Yang. SPIE, 2013. http://dx.doi.org/10.1117/12.2035052.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Hyperspectral imaging, Landmine detection, Remote sensing' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography