Noisy Bands Research Articles

COMPARING INDEPENDENT COMPONENT ANALYSIS WITH PRINCIPLE COMPONENT ANALYSIS IN DETECTING ALTERATIONS OF PORPHYRY COPPER DEPOSIT (CASE STUDY: ARDESTAN AREA, CENTRAL IRAN)

Abstract. The image processing techniques in transform domain are employed as analysis tools for enhancing the detection of mineral deposits. The process of decomposing the image into important components increases the probability of mineral extraction. In this study, the performance of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) has been evaluated for the visible and near-infrared (VNIR) and Shortwave infrared (SWIR) subsystems of ASTER data. Ardestan is located in part of Central Iranian Volcanic Belt that hosts many well-known porphyry copper deposits. This research investigated the propylitic and argillic alteration zones and outer mineralogy zone in part of Ardestan region. The two mentioned approaches were applied to discriminate alteration zones from igneous bedrock using the major absorption of indicator minerals from alteration and mineralogy zones in spectral rang of ASTER bands. Specialized PC components (PC2, PC3 and PC6) were used to identify pyrite and argillic and propylitic zones that distinguish from igneous bedrock in RGB color composite image. Due to the eigenvalues, the components 2, 3 and 6 account for 4.26% ,0.9% and 0.09% of the total variance of the data for Ardestan scene, respectively. For the purpose of discriminating the alteration and mineralogy zones of porphyry copper deposit from bedrocks, those mentioned percentages of data in ICA independent components of IC2, IC3 and IC6 are more accurately separated than noisy bands of PCA. The results of ICA method conform to location of lithological units of Ardestan region, as well.

Remote detection of rotating machinery with a portable atomic magnetometer.

We demonstrate remote detection of rotating machinery, using an atomic magnetometer at room temperature and in an unshielded environment. The system relies on the coupling of the AC magnetic signature of the target with the spin-polarized, precessing atomic vapor of a radio-frequency optical atomic magnetometer. The AC magnetic signatures of rotating equipment or electric motors appear as sidebands in the power spectrum of the atomic sensor, which can be tuned to avoid noisy bands that would otherwise hamper detection. A portable apparatus is implemented and experimentally tested. Proof-of-concept investigations are performed with test targets mimicking possible applications, and the operational conditions for optimum detection are determined. Our instrument provides comparable or better performance than a commercial fluxgate and allows detection of rotating machinery behind a wall. These results demonstrate the potential for ultrasensitive devices for remote industrial and usage monitoring, security, and surveillance.

Effect of Denoising on Dimensionally Reduced Sparse Hyperspectral Unmixing

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.

A robust and efficient band selection method using graph representation for hyperspectral imagery

ABSTRACTIn the field of unsupervised band selection, both robustness and efficiency are of great importance. In this article, we propose a new unsupervised band selection method termed graph representation based band selection (GRBS), which is expected to be insensitive to noisy bands and computationally inexpensive. In GRBS, bands are treated as the nodes of graph in high-dimensional space and centres of the band clusters are considered as the ideal choice. Interestingly, different from other clustering-based band selection methods, GRBS does not involve band clustering. Instead, it employs an easily computed criterion function to select the desired bands, which greatly improves the efficiency. The experiments demonstrate that GRBS has a promising performance and outperforms the compared methods in terms of both accuracy and efficiency.

Restoration of Simulated EnMAP Data through Sparse Spectral Unmixing

This paper proposes the use of spectral unmixing and sparse reconstruction methods to restore a simulated dataset for the Environmental Mapping and Analysis Program (EnMAP), the forthcoming German spaceborne hyperspectral mission. The described method independently decomposes each image element into a set of representative spectra, which come directly from the image and have previously undergone a low-pass filtering in noisy bands. The residual vector from the unmixing process is considered as mostly composed of noise and ignored in the reconstruction process. The first assessment of the results is encouraging, as the original bands taken into account are reconstructed with a high signal-to-noise ratio and low overall distortions. Furthermore, the same method could be applied for the inpainting of dead pixels, which could affect EnMAP data, especially at the end of the satellite’s life cycle.

Exploiting Algebraic and Structural Complexity for Single Snapshot Computed Tomography Hyperspectral Imaging Systems

This paper presents strategies for spectral de- noising of hyperspectral images and 3-D data cube reconstruction from a limited number of tomographic measurements, arising in single snapshot imaging systems. For de-noising the main idea is to exploit the incoherency between the algebraic complexity measure, namely the low rank of the noise-free hyperspectral data cube, and the sparsity structure of the spectral noise. In particular, the non-noisy spectral data, when stacked across the spectral dimension, exhibits low-rank due to a small number of species. On the other hand, under the same representation, the spectral noise exhibits a banded structure. Motivated by this we show that the de-noised spectral data and the unknown spectral noise and the respective bands can be simultaneously estimated through the use of a low-rank and simultaneous sparse minimization operation without prior knowledge of the noisy bands. This result is novel for hyperspectral imaging applications and we compare our results with several existing methods for noisy band recovery. For recovery under limited tomographic projections we exploit both the low algebraic and structural complexity of the data cube via joint rank penalization plus Total Variation/wavelet domain sparsity, which is novel for single snapshot hyperspectral imaging systems. We combine these two approaches for simultaneous spectral de-noising and data cube recovery under limited measurements. We perform extensive simulations and our main result indicates that exploiting both low algebraic and structural complexity has a superior performance compared to exploiting only the structural complexity. To address the computational challenges associated with the resulting optimization problem we adapt several recent developments in the area of convex optimization, specifically employing splitting and proximal point based methods.

Efficient Unsupervised Band Selection Through Spectral Rhythms

The main goal of remote sensing image classification is to associate land cover classes to each pixel in the monitored area. In this sense, hyperspectral images play a key role by providing detailed spectral information per pixel. On the other hand, although the huge amount of spectral bands enables the creation of more accurate thematic maps, they can compromise the quality of results due to data redundancy, high-dimensionality problems and noisy bands. Many dimensionality reduction techniques have been proposed in order to better use the available information. An effective strategy is to perform a band selection, which aims at selecting the best bands for classification. This process decreases the dimensionality without degrading information, i.e., it keeps the physical properties acquired by the sensors. As a drawback, the dimensionality reduction process can take a lot of time to be performed. In this paper, we propose a new unsupervised band selection method based on the dissimilarity among neighboring bands by exploiting an intermediary representation called spectral rhythm. Our approach can take advantage of a pixel sampling strategy to improve its efficiency without significant reduction on the quality of selected bands. Experimental results reveal that our method can efficiently select suitable bands to represent the whole data by producing accuracy results as good as the baselines in the classification problem.

Robust Hyperspectral Unmixing With Correntropy-Based Metric.

Hyperspectral unmixing is one of the crucial steps for many hyperspectral applications. The problem of hyperspectral unmixing has proved to be a difficult task in unsupervised work settings where the endmembers and abundances are both unknown. In addition, this task becomes more challenging in the case that the spectral bands are degraded by noise. This paper presents a robust model for unsupervised hyperspectral unmixing. Specifically, our model is developed with the correntropy-based metric where the nonnegative constraints on both endmembers and abundances are imposed to keep physical significance. Besides, a sparsity prior is explicitly formulated to constrain the distribution of the abundances of each endmember. To solve our model, a half-quadratic optimization technique is developed to convert the original complex optimization problem into an iteratively reweighted nonnegative matrix factorization with sparsity constraints. As a result, the optimization of our model can adaptively assign small weights to noisy bands and put more emphasis on noise-free bands. In addition, with sparsity constraints, our model can naturally generate sparse abundances. Experiments on synthetic and real data demonstrate the effectiveness of our model in comparison to the related state-of-the-art unmixing models.

Unsupervised Hyperspectral Image Band Selection via Column Subset Selection

In this letter, we proposed a novel band selection algorithm for hyperspectral images (HSIs) based on column subset selection. The main idea of the proposed algorithm comes from the column subset selection problem in numerical linear algebra. It selects a group of bands, which maximizes the volume of the selected subset of columns. Since the high dimensionality decreases the contrast between bands, we use Manhattan distance to obtain a higher selection quality. Experimental results on real HSIs show that the proposed algorithm obtains competitively good results, in terms of classification accuracy, and is robust to noisy bands.

Semisupervised Affinity Propagation Based on Normalized Trivariable Mutual Information for Hyperspectral Band Selection

The high dimensionality of hyperspectral images brings a heavy burden for image processing. Band selection is a common technique for dimensionality reduction. Since the labels of hyperspectral images are difficult to collect, a new semisupervised band selection method based on affinity propagation (AP) is proposed. AP, an exemplar-based clustering method, is famous due to fast execution time and low reconstruction error. For band selection, AP involves two key issues: band correlation and band preference. In this paper, a new normalized trivariable mutual information (normalized TMI, NTMI) is devised to measure band correlation for classification. NTMI considers not only band redundancy but also band synergy, and overcomes the sensitivity of TMI to the discriminative abilities of bands. Band preference is defined by the discriminative ability and informative amount of each band. Since the clustering methods are easily disturbed by noisy bands, a new statistical-based method for band correlation and band preference is devised. It can automatically remove noisy bands beforehand by exploiting the continuity property of bands. Finally, the proposed method can select highly discriminative and informative bands, and remove highly redundant bands. Experimental results on hyperspectral images demonstrate the effectiveness of the proposed semisupervised band selection method.

Design of a feature-tuned ANN model based on bulk rock-derived mineral spectra for endmember classification of a hyperspectral image from an iron ore deposit

In this article, a new feature-tuned artificial neural network (ANN) model has been developed for endmember classification of a hyperspectral image. This model is developed on the basis of using only the essential absorption bands of mineral spectra as opposed to using all the spectral bands of the hyperspectral image. This approach has the added advantages of reducing the dimensionality of input features to the ANN as well as inhibiting the influences of noisy bands for classification of endmembers. The proposed ANN model is trained using input features extracted from laboratory spectra of in situ bulk ore materials collected from an existing iron ore deposit. The input features are basically the constituent absorption bands of mineral spectra where each absorption band is mathematically characterized by the centre, width, and strength parameters of a Gaussian curve. For extracting absorption bands from a mineral spectrum, a modified Gaussian model has been used. The application of this model also necessitates the design of a special template for the input layer ANN model. After training the model, its generalization property is assessed through a testing data set. The model has achieved nearly 97% of classification accuracy in a training set, and 71% of accuracy in a testing set. The trained model is then applied on Hyperion imagery collected over an iron ore deposit. All the endmember spectra of this deposit are classified into either vegetation or any of the ores or rock present in the deposit. None of the endmembers is classified into non-iron ore minerals.

CEM: More Bands, Better Performance

Target detection has recently drawn considerable interest in hyperspectral image processing. People tend to exclude corrupted or badly damaged bands before applying the target detection algorithm to the data for better detection results. In this letter, it is proved that adding any band independent of the original image, even a noisy band, would be always beneficial to the performance of constrained energy minimization in terms of output energy. Finally, several tests are conducted to further justify our viewpoint.

High Speed Data Transmission Using Coding

The relible and fast data transmission over noisy band limitted channels is the basic requirment of digital communication and transmission system. Reliability considerations require that forward error correction techniques be used. This techniques find and correct limitted erros caused by a transport or storage systems. Such coding technique is therefore used to help compensate for signal degradation and provide increase in system capacity and reliability. In this paper, we analyze the performance of three codes which are the Reed Soloman Code, the convolutional code, and turbo code for the same signal that is corrupted by transmission channel. The type of channel considered in this work is the AWGN. For the same message length, the turbo code gives the best performance and the Reed Soloman Code gave the poorest performance when the signal is corrupted by AWGN channel.

Junk band recovery for hyperspectral image based on curvelet transform

Under consideration that the profiles of bands at close wavelengths are quite similar and the curvelets are good at capturing profiles, a junk band recovery algorithm for hyperspectral data based on curvelet transform is proposed. Both the noisy bands and the noise-free bands are transformed via curvelet band by band. The high frequency coefficients in junk bands are replaced with linear interpolation of the high frequency coefficients in noise-free bands, and the low frequency coefficients remain the same to keep the main spectral characteristics from being distorted. Junk bands then are recovered after the inverse curvelet transform. The performance of this method is tested on the hyperspectral data cube obtained by airborne visible/infrared imaging spectrometer (AVIRIS). The experimental results show that the proposed method is superior to the traditional denoising method BayesShrink and the art-of-state Curvelet Shrinkage in both roots of mean square error (RMSE) and peak-signal-to-noise ratio (PSNR) of recovered bands.

Noise-signal index threshold: a new noise-reduction technique for generation of reference spectra and efficient hyperspectral image classification

Reference spectra of terrestrial targets are usually collected using field spectro-radiometers for mineral abundance mapping and target detection. These spectra often have noise that masks characteristic absorption and reflection features and affects the efficiency of material mapping. This work aims at obtaining an empirical technique for reduction of high-frequency noise from field spectra. The proposed noise correction technique uses a ‘normalized’ measure Rn , where Rn = (Ln − Fn )/Ln for each band (n) calculated from field and laboratory spectra of test material, with Fn and Ln being the depth of the absorption feature in field and laboratory spectra, respectively. On the basis of the assumption of the constancy of this ratio in neighbouring bands, an empirical algorithm that approximates the ratio Rn of a noisy band to the corrected ratio of an adjacent band is used to obtain the noise-corrected field spectra. The classification accuracy increases significantly when noise reduced field spectra are used as reference spectra.

シリコーン含有ポリイミドから調製したSi/C/Oコンポジットのリチウムイオン挿入脱離特性

Si/C/O composites were formed by carbonization of Kapton type polyimide films containing 0–10 at% Si. About 24 mass% of Si was present in the composite derived from the polyimide containing 7 at% Si at 1350°C and, by 29Si MAS NMR, they were mainly Si(O)4 type components around −110 ppm with unidentifiable components in a range of −100 to −20 ppm. This composite showed the best performance of Li+ ion insertion/extraction: the coulomb efficiency was nearly 100% except for the 1st cycle and the extraction capacities were 648 mA h g−1 after 10 cycles at 50 mA g−1 and 393 mA h g−1 after 50 cycles at 500 mA g−1, and the latter was increased to ca. 470 mA h g−1 by preliminary cycles at low current densities. After 10 cycles at 50 mA g−1 the components around −110 ppm almost disappeared from the NMR spectrum and a broad and noisy band was observed in a range of −100 to −50 ppm. Since sub-micrometer fused silica particles did not react as the composites performed, the components around −110 ppm are not necessarily silica. The mechanism of reversible insertion/extraction of Li+ ion for the composites was not completely clarified, but the results by cyclic voltammetry and MAS NMR left room for doubt about an alloying/dealloying mechanism between Si and Li.

Canonical Correlation Feature Selection for Sensors With Overlapping Bands: Theory and Application

The main focus of this paper is a rigorous development and validation of a novel canonical correlation feature- selection (CCFS) algorithm that is particularly well suited for spectral sensors with overlapping and noisy bands. The proposed approach combines a generalized canonical correlation analysis framework and a minimum mean-square-error criterion for the selection of feature subspaces. The latter induces ranking of the best linear combinations of the noisy overlapping bands and, in doing so, guarantees a minimal generalized distance between the centers of classes and their respective reconstructions in the space spanned by sensor bands. To demonstrate the efficacy and the scope of the proposed approach, two different applications are considered. The first one is separability and classification analysis of rock species using laboratory spectral data and a quantum-dot infrared photodetector (QDIP) sensor. The second application deals with supervised classification and spectral unmixing, and abundance estimation of hyperspectral imagery obtained from the Airborne Hyperspectral Imager sensor. Since QDIP bands exhibit significant spectral overlap, the first study validates the new algorithm in this important application context. The results demonstrate that proper postprocessing can facilitate the emergence of QDIP-based sensors as a promising technology for midwave- and longwave-infrared remote sensing and spectral imaging. In particular, the proposed CCFS algorithm makes it possible to exploit the unique advantage offered by QDIPs with a dot-in-a-well configuration, comprising their bias-dependent spectral response, which is attributable to the quantum Stark effect. The main objective of the second study is to assert that the scope of the new CCFS approach also extends to more traditional spectral sensors.

Noise estimation in a noise-adjusted principal component transformation and hyperspectral image restoration

We apply a noise-adjusted principal component transformation (NAPCT) to an Earth Observing 1 (EO-1) Hyperion image whose noise structure is typically unknown. In this paper, we propose to simulate and estimate the noise covariance structure of either a body of water, such as an ocean or lake, or a horizontal piece-wise delineation along a spatially homogeneous area. The effect is compared to that of the near-neighbor difference method utilized in some of the literature. A strategy is proposed of efficiently and accurately locating the noisy bands, particularly the striping bands and the striping columns. It automates the task of manual examination of each band and is particularly useful for hyperspectral data. We illustrate algorithmically that the implementation of NAPCT can be achieved by application of the procedure in linear discriminant analysis (LDA). The resultant images of NAPCT are compared to those from standard principal component transformation (PCT). By using the first 10 NAPCT bands (almost striping and noise free), which explain 99.8% of total data variability, we can reproject the NAPCT image back onto the original spectral space for visualization and image enhancement. The quality of the restored hyperspectral image is greatly improved.

Advances in video coding for hand-held device implementation in networked electronic media

This survey addresses a number of challenges and research areas identified in real time image processing for state-of-the-art hand-held device implementation in networked electronic media. The challenges appear when having to develop and map processing algorithms not only on fading, noisy, and multi-path band limited transmission channels, but more specifically here on the limited resources available for decoding and scalable rendering on battery-limited hand-held mobile devices. Networked electronic media requires scalable video coding which in turn introduces additional degradation. These problems raise some complex issues discussed in the paper. A need to extend, modify and even create new algorithms and tools, targeting architectures, technology platforms, and design techniques as well as scalability, computational load and energy efficiency considerations has established itself as a key research area. A multidisciplinary approach is advocated.

Robust support vector method for hyperspectral data classification and knowledge discovery

We propose the use of support vector machines (SVMs) for automatic hyperspectral data classification and knowledge discovery. In the first stage of the study, we use SVMs for crop classification and analyze their performance in terms of efficiency and robustness, as compared to extensively used neural and fuzzy methods. Efficiency is assessed by evaluating accuracy and statistical differences in several scenes. Robustness is analyzed in terms of: (1) suitability to working conditions when a feature selection stage is not possible and (2) performance when different levels of Gaussian noise are introduced at their inputs. In the second stage of this work, we analyze the distribution of the support vectors (SVs) and perform sensitivity analysis on the best classifier in order to analyze the significance of the input spectral bands. For classification purposes, six hyperspectral images acquired with the 128-band HyMAP spectrometer during the DAISEX-1999 campaign are used. Six crop classes were labeled for each image. A reduced set of labeled samples is used to train the models, and the entire images are used to assess their performance. Several conclusions are drawn: (1) SVMs yield better outcomes than neural networks regarding accuracy, simplicity, and robustness; (2) training neural and neurofuzzy models is unfeasible when working with high-dimensional input spaces and great amounts of training data; (3) SVMs perform similarly for different training subsets with varying input dimension, which indicates that noisy bands are successfully detected; and (4) a valuable ranking of bands through sensitivity analysis is achieved.