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Hyperspectral images (HSIs) have attracted worldwide attention as they make it possible to remotely distinguish between spectrally similar materials. The data of hyperspectral image with high dimensionality, redundant features, unbalanced distribution and so on, however, impose great difficulties for HSI classification. An ensemble of classifiers are usually considered to provide more accurate predictions than a single classifier. In this paper, a heterogeneous ensemble consisting of a support vector machine, a kernel extreme learning machine and a multinomial logistic regression classifier is constructed to increase classification accuracies of HSIs. Furthermore, the feature extraction method is employed to transform the spectral features to promote the diversity of the ensemble, and a voting mechanism is introduced to predict the label on unseen data. Our experimental results with two widely used real hyperspectral datasets demonstrate that the proposed heterogeneous ensemble classifier obtains satisfactory performance with respect to spectral feature sets, even though the number of labelled samples is small. [ABSTRACT FROM AUTHOR]

Powdery mildew is considered to be one of the important diseases that threaten wheat production. It can cause serious economic losses and damage food security. Therefore, early detection of powdery mildew infection is essential to limit its amplification in reproduction. The purpose of this study is to use the hyperspectral image (HSI) to detect wheat powdery mildew (WPM) in an objective and accurate way. A method is proposed to classify the infection degree of WPM by combining cascade interval selection sensitive band with support vector machine (SVM). Firstly, HSI of leaf and canopy scale are obtained, and the image was segmented based on HSV (hue, saturation, value) color space and OTSU algorithm (HSV + OTSU). The spectral reflectance data were extracted after removing the background region of interest (ROI), and then the multiple scattering correction (MSC) was performed. The spectral data after MSC was called RAW, and then the first derivative (D1) and second derivative (D2) operations were performed respectively. Subsequently, the method of backward interval partial least squares (BiPLS) was used to screen out the characteristic bands of the sensitive interval. Finally, the classification models of WPM disease degree at the leaf scale and the canopy scale (RAW-BiPLS-SVM, D1-BiPLS-SVM and D2-BiPLS-SVM) were established respectively. The results showed that the spectral data after derivative operation was enhanced, and the classification effect of D1-BiPLS-SVM model had advantages at both leaf and canopy scales, in which the overall accuracy (OA) at the leaf scale was 95.2 %, and the OA at canopy scale was 74.4 %. In order to increase the difference between diseases, the canopy-scale disease severity levels were integrated. The correct rates of health and disease were 96.9 % and 99.2 %, respectively, and the OA of disease grade recombination was 87.9 %. Therefore, the method proposed in this paper can not only accurately identified healthy and infected wheat, but also realized the classification of different degrees of WPM, and provide technical support for the prevention and control of WPM. • Wheat powdery mildew (WPM) was detected based on the hyperspectral data. • A method for WPM infection classification based on interval selection sensitivity bands combined with SVM is proposed. • The proposed method can effectively classify WPM from leaf to canopy levels. • The results of the proposed method suggested its potential in field applications. [ABSTRACT FROM AUTHOR]

Abstract Nowadays, the hyperspectral remote sensing imagery HSI becomes an important tool to observe the Earth's surface, detect the climatic changes and many other applications. The classification of HSI is one of the most challenging tasks due to the large amount of spectral information and the presence of redundant and irrelevant bands. Although great progresses have been made on classification techniques, few studies have been done to provide practical guidelines to determine the appropriate classifier for HSI. In this paper, we investigate the performance of four supervised learning algorithms, namely, Support Vector Machines SVM, Random Forest RF, K-Nearest Neighbors KNN and Linear Discriminant Analysis LDA with different kernels in terms of classification accuracies. The experiments have been performed on three real hyperspectral datasets taken from the NASA's Airborne Visible/Infrared Imaging Spectrometer Sensor AVIRIS and the Reflective Optics System Imaging Spectrometer ROSIS sensors. The mutual information had been used to reduce the dimensionality of the used datasets for better classification efficiency. The extensive experiments demonstrate that the SVM classifier with RBF kernel and RF produced statistically better results and seems to be respectively the more suitable as supervised classifiers for the hyperspectral remote sensing images. [ABSTRACT FROM AUTHOR]

Abstract Recently, the hyperspectral sensors has improved our ability to monitor the earth surface with high spectral resolution. However, the high dimensionality of spectral data brings challenges for the image processing. Consequently, the dimensionality reduction is a necessary step in order to reduce the computational complexity and increase the classification accuracy. In this paper, we propose a new filter approach based on information gain for dimensionality reduction and classification of hyperspectral images. A special strategy based on hyperspectral bands selection is adopted to pick the most informative bands and discard the irrelevant and noisy ones. The algorithm evaluates the relevancy of the bands based on the information gain function with the support vector machine classifier. The proposed method is compared using two benchmark hyperspectral datasets (Indiana, Pavia) with three competing methods. The comparison results showed that the information gain filter approach outperforms the other methods on the tested datasets and could significantly reduce the computation cost while improving the classification accuracy. [ABSTRACT FROM AUTHOR]

• Unlike previous studies that used optical technology to irradiate the skin surface, our findings can help pathologists complete rapid pathological diagnoses, improve diagnostic accuracy, and provide new research ideas for distinguishing melanoma and nevus. • In this paper, we innovatively demonstrate the potential to extract a large amount of fluorescence fingerprint information using a small number of hyperspectral pathological images to identify the pigmented nevus and melanoma. • We discovered substantial variances in spectral features in the 500-675nm band range and extracted spectral information in this range of bands for the classification procedure, which achieves 98% accuracy. Cutaneous melanoma, an exceedingly aggressive form of skin cancer, holds the top rank in both malignancy and mortality among skin cancers. In early stages, distinguishing malignant melanomas from benign pigmented nevi pathologically becomes a significant challenge due to their indistinguishable traits. Traditional skin histological examination techniques, largely reliant on light microscopic imagery, offer constrained information and yield low-contrast results, underscoring the necessity for swift and effective early diagnostic methodologies. As a non-contact, non-ionizing, and label-free imaging tool, hyperspectral imaging offers potential in assisting pathologists with identification procedures sans contrast agents. This investigation leverages hyperspectral cameras to ascertain the optical properties and to capture the spectral features of malignant melanoma and pigmented nevus tissues, intending to facilitate early pathological diagnostic applications. We further enhance the diagnostic process by integrating transfer learning with deep convolutional networks to classify melanomas and pigmented nevi in hyperspectral pathology images. The study encompasses pathological sections from 50 melanoma and 50 pigmented nevus patients. To accurately represent the spectral variances between different tissues, we employed reflectance calibration, highlighting that the most distinctive spectral differences emerged within the 500–675 nm band range. The classification accuracy of pigmented tumors and pigmented nevi was 89% for one-dimensional sample data and 98% for two-dimensional sample data. Our findings have the potential to expedite pathological diagnoses, enhance diagnostic precision, and offer novel research perspectives in differentiating melanoma and nevus. [ABSTRACT FROM AUTHOR]

In hyperspectral images, spectral mixing occurs when objects lying beside each cannot be distinguished as different entities due to its low spatial resolution. Other hurdles in hyperspectral imaging are its huge dimension and noisy bands. In this paper, a new approach for spectral unmixing is presented where, the data is reduced dimensionally and, the bands eliminated during this are denoised using the existing denoising methods. Then, dataset with these bands is dimensionally reduced and their presence after reduction is validated using spectral unmixing methods. The effectiveness of this method is evaluated using parametric measures such as RMSE and classification accuracy. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging sensors measure the radiance of the materials within each pixel area at a very large number of contiguous spectral wavelength bands. The spectral information available in a hyperspectral image (cube) may serve to classify the nature of the target object because every material had a unique fixed spectrum. In this paper, the similarity can be described through various spectral distances between pixels and can be controlled by a threshold value. In the context of hyperspectral imagery, the “distance” is a spectral distance not a spatial distance. This measure is used in many classification techniques to estimate the distance between the spectrum to be classified and the representatives of the class („target”). The spectral screening is a process that can produce a representative subset in hyperspectral data with similar spectral signature. We have investigated the proposed method to find subsets of similar pixels on the AVIRIS data. [ABSTRACT FROM AUTHOR]

• Individual models between spectral indices and emissivity of urban surfaces are proposed. • The best wavelengths of low-altitude hyperspectral data for calculating spectral indices are determined. • DVI is the best for higher-emissivity surfaces, and OSAVI is the fittest for lower-emissivity surfaces. • The largest temperature error caused by the emissivity deviation reaches up to 3.7 K. The estimation of land surface energy budgets and land surface temperature (LST) require accurate information on land surface emissivity (LSE). Despite the use of remote sensing or low-altitude sensing data in numerous models for the computation of LSE, it is still highly challenging to precisely predict the LSE of high-heterogeneous surfaces on a micro-scale. This paper thus proposes several individual models to retrieve each surface's emissivity on the pixel level. Various emissivity and reflectance values were obtained upon spectral resampling to found individual models between emissivity and spectral indices (NDVI, DVI, MSAVI, OSAVI, RRI) for classified surfaces. Based on the smallest root mean square error (RMSE), DVI models accurately indicated the emissivity of concrete floors (0.0012), asphalt roads (0.0012), grasses (0.0062), parking lots (0.0035), pavements (0.0057), and red manhole covers (0.0089). RRI models better predicted the emissivity of tiles (0.0039), grey manhole covers (0.0092), and shrubs (0.0005). OSAVI was also the ideal index for retrieving the emissivity of rooftops (0.0009). Validation results showed their effectiveness, with accuracy within 0.001. Finally, temperature errors caused by the deviation between traditional or default emissivity and measured emissivity fluctuated from 0.0 K to 2.9 K or 0.2 K to 3.7 K, respectively. [ABSTRACT FROM AUTHOR]

Hyperspectral sensors capture images in hundreds of narrow spectral channels. The spectral signatures for each spatial location provide rich information about an image scene, leading to better “separation” between physical materials and objects. Hyperspectral data are spectrally overestimated and the useful signals usually occupy lower dimensional subspace which needs to be inferred. The signal information is usually concentrated in lower dimensional subspaces. For estimating the number of spectrally distinct signatures present in a hyperspectral data the concept of Virtual Dimensionality (VD), give us the minimum number of spectrally distinct signal sources that characterize this data from a perspective view of target detection and classification. Considering these facts it is important to reduce the volume of data with minimum loss of information and this is the main idea of our algorithm. In this paper, a new VD modified method for estimating the number of spectral distinct signatures has been proposed. To demonstrate the applicability of the developed software tools, we used a few well-known hyperspectral image-data sets. [ABSTRACT FROM AUTHOR]

Abstract: Hyperspectral data acquired over hundreds of narrow contiguous wavelength bands are extremely suitable for target detection due to their high spectral resolution. Though spectral response of every material is expected to be unique, but in practice, it exhibits variations, which is known as spectral variability. Most target detection algorithms depend on spectral modelling using a priori available target spectra In practice, target spectra is, however, seldom available a priori. Independent component analysis (ICA) is a new evolving technique that aims at finding out components which are statistically independent or as independent as possible. The technique therefore has the potential of being used for target detection applications. A assessment of target detection from hyperspectral images using ICA and other algorithms based on spectral modelling may be of immense interest, since ICA does not require a priori target information. The aim of this paper is, thus, to assess the potential of ICA based algorithm vis a vis other prevailing algorithms for military target detection. Four spectral matching algorithms namely Orthogonal Subspace Projection (OSP), Constrained Energy Minimisation (CEM), Spectral Angle Mapper (SAM) and Spectral Correlation Mapper (SCM), and four anomaly detection algorithms namely OSP anomaly detector (OSPAD), Reed–Xiaoli anomaly detector (RXD), Uniform Target Detector (UTD) and a combination of Reed–Xiaoli anomaly detector and Uniform Target Detector (RXD–UTD) were considered. The experiments were conducted using a set of synthetic and AVIRIS hyperspectral images containing aircrafts as military targets. A comparison of true positive and false positive rates of target detections obtained from ICA and other algorithms plotted on a receiver operating curves (ROC) space indicates the superior performance of the ICA over other algorithms. [Copyright &y& Elsevier]

In this paper, an integrated framework for the classification of hyperspectral images is presented. Firstly, two convolutional neural networks (CNNs) were developed for the extraction of representative features. In particular, a pixel-wise CNN and a patch-based CNN were designed to extract spectral features and spectral–spatial features, respectively. The two neural networks consist of several convolutional, pooling and activation layers, and are able to predict the class membership probabilities of test pixels. Secondly, two probabilistic relaxation methods, namely Markov random fields and discontinuity preserving relaxation were integrated into the framework in order to refine the probabilistic results from a Bayesian perspective. This framework enhances the classification performance by exploiting the contextual information available from neighboring pixels. This is particularly advantageous when only limited training samples are available. The proposed framework was tested on both simulated and real-world data sets. The experimental results suggest that the proposed methods outperform several state-of-the-art methods. • We developed and applied a new framework for HSI classification. • The new method integrates a CNN and a probabilistic relaxation method. • The CNN is designed to be pixel-wise and patch-based, respectively. • CNN is applied to obtain the probabilistic distribution for test pixels. • Probabilistic method is used to refine the probabilistic results. • The proposed method performs very well with limited training samples. [ABSTRACT FROM AUTHOR]