Showing 1-25 of 51 resultsfor "Index Terms":kernel spectral angle mapper algorithm
"Index Terms":kernel spectral angle mapper algorithm
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A Kernel Spectral Angle Mapper algorithm for remote sensing image classification
2013 6th International Congress on Image and Signal Processing (CISP)
Year: 2013 | Conference Paper |
Cited by: Papers (32)
A Kernel Spectral Angle Mapper (KSAM) algorithm is proposed to deal better with the nonlinear classification problem of the remote sensing image. The so-called KSAM algorithm is achieved by introducing the kernel method into the standard Spectral Angle Mapper (SAM) algorithm. Experimental results indicate that the classification accuracy of the KSAM algorithm is superior to one of the SAM algorith...Show More
Spectral Angle Mapper classification of fluorescence hyperspectral image for aflatoxin contaminated corn
Haibo Yao;Zuzana Hruska;Russell Kincaid;Ambrose Ononye;Robert L. Brown;Thomas E. Cleveland
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
Year: 2010 | Conference Paper |
Cited by: Papers (8)
Aflatoxin contamination in corn is a serious problem for both producers and consumers. The present study applied the Spectral Angle Mapper classification technique to classify single corn kernels into contaminated and healthy groups. Fluorescence hyperspectral images were used in the classification. Actual corn aflatoxin concentration was chemically determined using the VICAM analytical method for...Show More
A family of kernel anomaly change detectors
2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
Year: 2014 | Conference Paper |
Cited by: Papers (3)
This paper introduces the nonlinear extension of the anomaly change detection algorithms in [1] based on the theory of reproducing kernels. The presented methods generalize their linear counterparts, under both the Gaussian and elliptically-contoured assumptions, and produce both improved detection accuracies and reduced false alarm rates. We study the Gaussianity of the data in Hilbert spaces wit...Show More
Advances in Spectral-Spatial Classification of Hyperspectral Images
Mathieu Fauvel;Yuliya Tarabalka;Jón Atli Benediktsson;Jocelyn Chanussot;James C. Tilton
Proceedings of the IEEE
Cited by: Papers (1085)
Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which...Show More
Hyperspectral Image Analysis--A Robust Algorithm Using Support Vectors and Principal Components
2007 International Conference on Computing: Theory and Applications (ICCTA'07)
Year: 2007 | Conference Paper |
Cited by: Papers (6)
This paper presents a new algorithm for hyperspectral image analysis using spectral-angle based support vector clustering (SVC) and principal component analysis (PCA). In the classical approach to hyper-spectral dimensionality reduction based on principal component analysis (PCA), no meaning or behavior of the spectrum is considered and results are influenced by majority components in the scene. A...Show More
A new framework of target detection in hyperspectral images
Yanshan Li;Jianjie Xu;Yayuan Chen;Zhoufan Kong;Qinghua Huang
2017 2nd International Conference on Advanced Robotics and Mechatronics (ICARM)
Year: 2017 | Conference Paper |
Cited by: Papers (1)
Hyperspectral Image (HSI) is used widely in many areas, especially in the remote sensing field. Compared with the traditional remote sensing HSI, the large-scale and high-resolution HSI (LHHSI) which has big data and large size is high-resolution both in spatial domain and spectral domain. However, traditional methods of automatic target detection do not apply to LHHSI. Therefore, this paper propo...Show More
Spectral Modulation for Fusion of Hyperspectral and Multispectral Images
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
Year: 2019 | Conference Paper |
Hyperspectral (HS) and multispectral (MS) image fusion has attracted great attention during the past decades. Numerous of fusion methods have been developed and shown their effectiveness particularly on simulated data. Nonetheless, for real remote sensing data, the different acquisition times or conditions result in a serious spectral distortion and severely affect the fusion quality. Yet very few...Show More
Empirical Mode Decomposition Based Morphological Profile For Hyperspectral Image Classification
2023 6th International Conference on Pattern Recognition and Image Analysis (IPRIA)
Year: 2023 | Conference Paper |
The empirical mode decomposition (EMD) based morphological profile (MP), called as EMDMP, is proposed for hyperspectral image classification in this work. The EMD algorithm can well decompose the nonlinear spectral feature vector to intrinsic components and the residual term. To extract the main spatial characteristics and shape structures, the closing operators are applied to the intrinsic compon...Show More
Feature selection with PSO and kernel methods for hyperspectral classification
2011 IEEE Congress of Evolutionary Computation (CEC)
Year: 2011 | Conference Paper |
Cited by: Papers (6)
Hyperspectral image data has great potential to identify and classify the chemical composition of materials remotely. Factors limiting the use of hyperspectral sensors in practical land-based applications, such as robotics and mining, are the complexity and cost of data acquisition, and the processing time required for the subsequent analysis. This is mainly due to the high dimensional and high vo...Show More
Precise classification of coastal benthic habitats using high resolution Worldview-2 imagery
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Year: 2015 | Conference Paper |
Cited by: Papers (1)
The analysis of the seafloor in shallow waters using remote sensing imagery at very high spatial resolution is a very challenging topic due to the minimum signal level received; the presence of noisy contributions from the atmosphere, solar reflection, foam, turbidity and water column; and the limited spectral information available for the classification at such depths that impedes, for example, t...Show More
Hyperspectral imaging technique for plant leaf identification
2015 International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)
Year: 2015 | Conference Paper |
Cited by: Papers (3)
Hyperspectral imaging is the procedure to gather and handle information across the electromagnetic spectrum. The fundamental objective of hyperspectral imaging is to achieve the spectrum for every pixel in the picture. The spectrum helps in computer vision, i.e., locating items, material detection or process discovery. This approach is constantly developing in the field of remote sensing applicati...Show More
In this paper, an improved least squares support vector machines algorithm for solving remote sensing classification problems is presented. Support Vector Machines (SVM) is a potential remote sensing classification method because it is advantageous to deal with problems with high dimensions, small samples and uncertainty. The general idea of the proposed algorithm is that spectral angle mapping (S...Show More
Kernel Anomalous Change Detection for Remote Sensing Imagery
José A. Padrón-Hidalgo;Valero Laparra;Nathan Longbotham;Gustau Camps-Valls
Anomalous change detection (ACD) is an important problem in remote sensing image processing. Detecting not only pervasive but also anomalous or extreme changes has many applications for which methodologies are available. This paper introduces a nonlinear extension of a full family of anomalous change detectors. In particular, we focus on algorithms that utilize Gaussian and elliptically contoured ...Show More
A combined hyperspectral image restoration and fusion approach
2009 IEEE International Geoscience and Remote Sensing Symposium
Year: 2009 | Conference Paper |
Cited by: Papers (2)
In this paper, we present a combined image restoration and fusion approach to enhance the spatial resolution of hyper-spectral (HS) images, using a low spatial resolution HS observation and a high spatial resolution multispectral (MS) observation of the same scene. The proposed approach is based on an iterative Expectation-Maximization restoration algorithm, improving the spatial resolution of the...Show More
Correcting Bidirectional Effect for Multiple-Flightline Aerial Images Using a Semiempirical Kernel-Based Model
Due to the intrinsic observing characteristics of airborne sensors, the bidirectional effect is inevitable and happens regardless of the number of flightlines being considered. This affects the quantitative use of aerial data over large regions. In this paper, a simple “two-step” bidirectional effect correction scheme based on Ross-Li model (RLM) is developed for multiple-flightline aerial images....Show More
MLKAF-Net: Multiscale Large Kernel Attention Network for Hyperspectral and Multispectral Image Fusion
The fusion of a low spatial resolution hyperspectral image (LR-HSI) with a high spatial resolution multispectral image (HR-MSI) aims to synthesize a high-resolution hyperspectral image (HR-HSI), enabling a broader range of applications for hyperspectral images (HSIs). However, existing fusion methods struggle to capture both long-range dependencies and fine-grained spatial features, resulting in b...Show More
MPSoC FPGA Implementation of Algorithms of Machine Learning for Clinical Applications Using High-Level Design Methodology
2023 26th Euromicro Conference on Digital System Design (DSD)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
This paper presents the design of an FPGA-accelerated application for skin cancer detection which uses both hyperspectral imaging and a k-means algorithm. The accelerator is designed employing 3 FPGA kernels. The first 2 kernels filter and normalize the hyperspectral image. Then, the last kernel runs k-means to segment the image into three different regions according to the distribution of the les...Show More
Assessment of different classification algorithms for burnt land discrimination
2007 IEEE International Geoscience and Remote Sensing Symposium
Year: 2007 | Conference Paper |
Cited by: Papers (2)
In this paper, satellite-based remote sensing techniques are used for assessing the damage after a forest fire. Here, burnt land mapping is based on a single after-fire satellite image (SPOT 5). Both Support Vector Machines (SVM) and traditional classification algorithms such as the K-nearest neighbours or the Kmeans are used to discriminate burnt from unburnt areas. An automatic method combining ...Show More
Supervised classification for hyperspectral imaging in UAV maritime target detection
Sara Freitas;Carlos Almeida;Hugo Silva;José Almeida;Eduardo Silva
2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)
Year: 2018 | Conference Paper |
Cited by: Papers (2)
This paper addresses the use of a hyperspectral image system to detect vessels in maritime operational scenarios. The developed hyperspectral imaging classification methods are based on supervised approaches and allow to detect the presence of vessels using real hyperspectral data. We implemented two different methods for comparison purposes: SVM and SAM. The SVM method, which can be considered on...Show More
A hyperspectral image restoration technique
2009 16th IEEE International Conference on Image Processing (ICIP)
Year: 2009 | Conference Paper |
Cited by: Papers (1)
In this paper, a restoration technique for hyperspectral images is presented. The technique requires a low spatial resolution hyperspectral image and a high spatial resolution multispectral image of the same scene. The proposed approach applies a restoration on the hyperspectral image, while accounting for the joint statistics with the multispectral image. The restoration is based on an Expectatio...Show More
Laplacian support vector machine for hyperspectral image classification by using manifold learning algorithms
2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS
Year: 2013 | Conference Paper |
Cited by: Papers (5)
For hyperspectral image classification, manifold learning based graph Laplacian is proposed in the Laplacian support vector machine (LapSVM) classifier. The manifold regularization term in LapSVM constrains the smoothness of classification function on the data manifold. Since manifold learning approach is capable of exploring the manifold geometry of data, it is suitable for calculating the graph ...Show More
Kernel-based invariant subspace method for hyperspectral target detection
2004 IEEE International Conference on Acoustics, Speech, and Signal Processing
Year: 2004 | Conference Paper |
In this paper, a kernel-based invariant subspace detection method is proposed for small target detection of hyperspectral images. The method combines kernel principal component analysis (KPCA) and the linear mixture model (LMM). The LMM is used to describe each pixel in the hyper-spectral image as a mixture of target, background and noise. The KPCA is used to build subspaces of the target and back...Show More
A Region Based Pan Sharpening Method Using Match Measure and Fuzzy Logic
Tanish Zaveri;Mukesh Zaveri;Ishit MaKwana;Mitul Panchal;Vitrag Sheth
The proposed method provides novel tradeoff solution to preserve spectral as well as spatial quality using fuzzy logic. It combines match measure, region based approach and fuzzy logic to produce quality pan sharp image. Standard pan-sharpening methods do not allow control of the spatial and spectral quality of the pan sharp image. Sugeno integral fuzzy logic model allows us to blend the benefits ...Show More
FPGA High Level Synthesis for the classification of skin tumors with hyperspectral images
Elisa Marenzi;Emanuele Torti;Giovanni Danese;Francesco Leporati
2022 11th Mediterranean Conference on Embedded Computing (MECO)
Year: 2022 | Conference Paper |
Cited by: Papers (6)
Cancer is the main cause of premature death in the world, with 18 million diagnoses in 2018, 3.9 million of which in Europe. In particular, according to studies conducted by the American Academy of Dermatology, skin cancer is the most prevalent type in the US. Diagnostic tools are generally invasive, hence research focuses on emerging technologies, like hyperspectral images, since they are non-inv...Show More
High-Accuracy Image Formation Model for Coded Aperture Snapshot Spectral Imaging
Coded aperture snapshot spectral imaging (CASSI) is based on the binary modulation of the spatial-spectral scene, which allows for hyperspectral image reconstruction from 2D compressive measurement. However, the actual optical modulation does not match the current image formation model due to the extra optical phenomena, such as diffraction, distortion, optical misalignment, and dispersion, inside...Show More
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