Noise-adjusted Principal Component Research Articles

Thin cloud removal in remote sensing images based on noise-adjusted principal component transform

Thin cloud removal in remote sensing images based on noise-adjusted principal component transform

Joint Local Block Grouping with Noise-Adjusted Principal Component Analysis for Hyperspectral Remote-Sensing Imagery Sparse Unmixing

Spatial regularized sparse unmixing has been proved as an effective spectral unmixing technique, combining spatial information and standard spectral signatures known in advance into the traditional spectral unmixing model in the form of sparse regression. In a spatial regularized sparse unmixing model, spatial consideration acts as an important role and develops from local neighborhood pixels to global structures. However, incorporating spatial relationships will increase the computational complexity, and it is inevitable that some negative influences obtained by inaccurate estimated abundances’ spatial correlations will reduce the accuracy of the algorithms. To obtain a more reliable and efficient spatial regularized sparse unmixing results, a joint local block grouping with noise-adjusted principal component analysis for hyperspectral remote-sensing imagery sparse unmixing is proposed in this paper. In this work, local block grouping is first utilized to gather and classify abundant spatial information in local blocks, and noise-adjusted principal component analysis is used to compress these series of classified local blocks and select the most significant ones. Then the representative spatial correlations are drawn and replace the traditional spatial regularization in the spatial regularized sparse unmixing method. Compared with total variation-based and non-local means-based sparse unmixing algorithms, the proposed approach can yield comparable experimental results with three simulated hyperspectral data cubes and two real hyperspectral remote-sensing images.

Thin cloud removal from optical remote sensing images using the noise-adjusted principal components transform

Cloud cover is a common problem in optical remote sensing images due to its contamination of the reflectance from the Earth’s surface. Cloud removal has become a necessary preprocessing step for most applications. In this paper, we develop an effective thin cloud removal method with the help of a noise-adjusted principal components transform model (CR-NAPCT). We discover that cloud-contaminated data has the highest signal-to-noise ratio (S/N) due to its high spatial correlation, and are shown in the first NAPCT component (NAPC1) in the NAPCT data. Image restoration is achieved by performing an inverse NAPCT after removing the cloud component in NAPC1. In the experiments, both simulated and real images from Landsat 8 were used to assess the performance of the proposed method quantitatively and qualitatively. The results show that the CR-NAPCT method is effective in eliminating thin cloud effects and performs better than the existing similar approaches tested.

Noise-robust detection and tracking of salt domes in postmigrated volumes using texture, tensors, and subspace learning

The identification of salt-dome boundaries in migrated seismic data volumes is important for locating petroleum reservoirs. The presence of noise in the data makes computer-aided salt-dome interpretation even more challenging. We have developed noise-robust algorithms that could label boundaries of salt domes effectively and efficiently. Our research is twofold. First, we used a texture-based gradient to accomplish salt-dome detection. We found that by using a dissimilarity measure based on the 2D discrete Fourier transform, the algorithm was capable of efficiently detecting salt-dome boundaries with accuracy. At the same time, our analysis determined that the proposed algorithm was robust to noise. Once the detection is performed for an initial 2D seismic section, we track the initial boundaries through the data volume to accomplish an efficient labeling process by avoiding the parameter tuning that would have been necessary if detection had been performed for every seismic section. The tracking process involves a tensor-based subspace learning process, in which we built texture tensors using patches from different seismic sections. To accommodate noise components with various levels in a texture tensor, we used noise-adjusted principal component analysis, so that principal components corresponding to greater signal-to-noise-ratio values might be selected for tracking. We validated our detection and tracking algorithms through experiments using seismic data sets acquired from the Netherlands offshore F3 block in the North Sea with very encouraging results.

An Improved Nonlocal Sparse Unmixing Algorithm for Hyperspectral Imagery

As a result of the spatial consideration of the imagery, spatial sparse unmixing (SU) can improve the unmixing accuracy for hyperspectral imagery, based on the application of a spectral library and sparse representation. To better utilize the spatial information, spatial SU methods such as SU via variable splitting augmented Lagrangian and total variation (SUnSAL-TV) and nonlocal SU (NLSU) have been proposed. However, the spatial information considered in these algorithms comes from the estimated abundance maps, which will change along with the iterations. As the spatial correlations of the imagery are fixed and certain, the spatial relationships obtained from the variable abundances are not reliable during the process of optimization. To obtain more precise and fixed spatial relationships, an improved weight calculation NLSU (I-NLSU) algorithm is proposed in this letter by changing the spatial information acquisition source from the variable estimated abundances to the original hyperspectral imagery. A noise-adjusted principal component analysis strategy is also applied for the feature extraction in the proposed algorithm, and the obtained principal components are the foundation of the spatial relationships. The experimental results of both simulated and real hyperspectral data sets indicate that the proposed I-NLSU algorithm outperforms the previous spatial SU methods.

A New Band Selection Method for Hyperspectral Image Based on Data Quality

Most unsupervised band selection methods take the information of bands into account, but few of them pay attention to the quality of bands. In this paper, by combining idea of noise- adjusted principal components (NAPCs) with a state-of-art band selection method (maximum determinant of covariance matrix (MDCM)), we define a new index to quantitatively measure the quality of the hyperspectral data cube. Both signal-to-noise ratios (SNRs) and correlation of bands are simultaneously considered in . Based on the new index defined in this article, we propose an unsupervised band selection method called minimum noise band selection (MNBS). Taking the quality ( ) of the data cube as selection criterion, MNBS tries to find the bands with both high SNRs and low correlation (high ). The subset selection method, sequential backward selection (SBS), is used in MNBS to improve the search efficiency. Some comparative experiments based on simulated as well as real hyperspectral data are conducted to evaluate the performance of MNBS in this study. The experimental results show that the bands selected by MNBS are always more effective than those selected by other methods in terms of classification.

Probability Density Components Analysis: A New Approach to Treatment and Classification of SAR Images

Speckle noise (salt and pepper) is inherent to synthetic aperture radar (SAR), which causes a usual noise-like granular aspect and complicates the image classification. In SAR image analysis, the spatial information might be a particular benefit for denoising and mapping classes characterized by a statistical distribution of the pixel intensities from a complex and heterogeneous spectral response. This paper proposes the Probability Density Components Analysis (PDCA), a new alternative that combines filtering and frequency histogram to improve the classification procedure for the single-channel synthetic aperture radar (SAR) images. This method was tested on L-band SAR data from the Advanced Land Observation System (ALOS) Phased-Array Synthetic-Aperture Radar (PALSAR) sensor. The study area is localized in the Brazilian Amazon rainforest, northern Rondônia State (municipality of Candeias do Jamari), containing forest and land use patterns. The proposed algorithm uses a moving window over the image, estimating the probability density curve in different image components. Therefore, a single input image generates an output with multi-components. Initially the multi-components should be treated by noise-reduction methods, such as maximum noise fraction (MNF) or noise-adjusted principal components (NAPCs). Both methods enable reducing noise as well as the ordering of multi-component data in terms of the image quality. In this paper, the NAPC applied to multi-components provided large reductions in the noise levels, and the color composites considering the first NAPC enhance the classification of different surface features. In the spectral classification, the Spectral Correlation Mapper and Minimum Distance were used. The results obtained presented as similar to the visual interpretation of optical images from TM-Landsat and Google Maps.

Combining noise-adjusted principal components transform and median filter techniques for denoising modis temporal signatures

Consistent multi-temporal images are necessary for accurate landscape change detection and temporal signatures analysis. Orbital images have a difficultyto maintain a temporal information precision due to several interferences that generate missing data. In this paper is developed a program in C++ languagefor denoising MODIS temporal signatures considering two-phase scheme for removing impulse and white noise. In the first phase, the median filter is used to identifyimpulse noise. In the second phase, the Noise-Adjusted Principal Components (NAPC) transform is applied to eliminate white noise. Because they are two complementarymethods, there is high performance in removing noise. The restored NDVI (Normalized Difference Vegetation Index) signatures showed a significant improvementproviding a time series dataset that can be used to identify and classify the vegetation physiognomic types.

Noise-Adjusted Principal Component Analysis for Buried Radioactive Target Detection and Classification

We present a noise-adjusted principal component analysis (NAPCA)-based approach to the detection and classification of buried radioactive targets with short sensor dwell time. The data used in the experiments is the gamma spectroscopy collected by a Sodium Iodide (NAI) scintillation detector. Spectral transformation methods are first applied to the data, followed by NAPCA. Then k-nearest neighbor (kNN) clustering is applied to the NAPCA-transformed feature subspace to achieve detection or classification. This method is evaluated using a database of 240 spectral measurements consisting of background (construction sand), benign material measurements (uranium ore), and target measurements (depleted uranium) at various depths. Compared to other widely used algorithms for depleted uranium, the proposed technique can provide better performance.

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.

Independent-component analysis for hyperspectral remote sensing imagery classification

We investigate the application of independent-component analysis (ICA) to remotely sensed hyperspectral image classification. We focus on the performance of two well-known and frequently used ICA algorithms: joint approximate diagonalization of eigenmatrices (JADE) and FastICA; but the proposed method is applicable to other ICA algorithms. The major advantage of using ICA is its ability to classify objects with unknown spectral signatures in an unknown image scene, i.e., unsupervised classification. However, ICA suffers from computational expensiveness, which limits its application to high-dimensional data analysis. In order to make it applicable or reduce the computation time in hyperspectral image classification, a data-preprocessing procedure is employed to reduce the data dimensionality. Instead of using principal-component analysis (PCA), a noise-adjusted principal-components (NAPC) transform is employed for this purpose, which can reorganize the original data with respect to the signal-to-noise ratio, a more appropriate image-ranking criterion than variance in PCA. The experimental results demonstrate that the major principal components from the NAPC transform can better maintain the object information in the original data than those from PCA. As a result, an ICA algorithm can provide better object classification.

Blind separation of spectral signatures in hyperspectral imagery

For the purpose of material identification, methods for exploring hyperspectral images with minimal human intervention have been investigated. Without any prior knowledge, it is extremely difficult to identify or determine how many endmembers in a scene. To tackle this problem, a new spectral unmixing technique, the spectral data explorer (SDE), is presented in the paper. SDE is a hybrid approach combining the optimal parts of fast independent component analysis (FastICA) and noise-adjusted principal components analysis (NAPCA). Experimental results show that SDE is highly efficient for separating significant signatures of hyperspectral images in a blind environment.

Determination of data dimensionality in hyperspectral imagery -- A noise-adjusted transformed Derschgorin disk approach

In hyperspectral image analysis, determining a distinct material number is an important task for subsequent classification processes. Identifying the number of distinct materials is essentially the same task as determining the intrinsic dimensionality of the imaging spectrometer data. Minimum noise fraction (MNF) transformation or noise-adjusted principal component analysis (NAPCA) is a highly effective means of determining the inherent dimensionality of image data. However, inaccuracy in the noise estimation degrades the validity of this estimation. To effectively resolve this problem, this work presents a Noise-Adjusted Transformed Gerschgorin Disk approach (NATGD) which incorporates the NAPCA method into a transformed Gerschgorin disk (TGD) approach. By noise-adjusted, Gerschgorin disks in NATGD can be formed into two distinct, non-overlapping collections; one for signals and the other for noises. Hence, the number of distinct materials can be visually determined by counting the number of Gerschgorin disks for signals. Experimental results demonstrate that the method proposed herein can effectively solve the intrinsic dimensionality problem.

Interference and noise-adjusted principal components analysis

The goal of principal components analysis (PCA) is to find principal components in accordance with maximum variance of a data matrix. However, it has been shown recently that such variance-based principal components may not adequately represent image quality. As a result, a modified PCA approach based on maximization of SNR was proposed. Called maximum noise fraction (MNF) transformation or noise-adjusted principal components (NAPC) transform, it arranges principal components in decreasing order of image quality rather than variance. One of the major disadvantages of this approach is that the noise covariance matrix must be estimated accurately from the data a priori. Another is that the factor of interference is not taken into account in MNF or NAPC in which the interfering effect tends to be more serious than noise in hyperspectral images. In this paper, these two problems are addressed by considering the interference as a separate, unknown signal source, from which an interference and noise-adjusted principal components analysis (INAPCA) can be developed in a manner similar to the one from which the NAPC was derived. Two approaches are proposed for the INAPCA, referred to as signal to interference plus noise ratio-based principal components analysis (SINR-PCA) and interference-annihilated noise-whitened principal components analysis (IANW-PCA). It is shown that if interference is taken care of properly, SINR-PCA and IANW-PCA significantly improve NAPC. In addition, interference annihilation also improves the estimation of the noise covariance matrix. All of these results are compared with NAPC and PCA and are demonstrated by HYDICE data.

Determination of Data Dimensionality in Hyperspectral Imagery—PNAPCA

Minimum noise fraction (MNF) transformation or noise-adjusted principal component analysis (NAPCA) is frequently used to determine the inherent dimensionality for remote sensing images. However, these approaches are limited primarily in that the noise must be accurately estimated from the data or a priori. Inaccurately estimating the noise seriously degrades the validity of the calculated dimensionality. In this work, we apply NAPCA to a partitioned data space to resolve the inaccuracy of the noise estimation and properly estimate the data dimensionality. This approach is referred to herein as PNAPCA. In contrast to the PCA-based approaches which consider interrelationships within a set of variables, PNAPCA focuses on the relationship between two distinct subspaces which are partitioned from the data space of the original image by a simultaneous transformation. This partitioning causes the gap between the group of eigenvalues for signal plus noise and noise only to become larger than all other PCA-based approaches. The number of endmembers can then be determined by a designed union-intersection margin testing (UIMT). In addition, the performance of PNAPCA is assessed by two experiments using simulated and real imaging spectrometer data sets collected by the Airborne Visible Infrared Imaging Spectrometer (AVIRIS). Experimental results demonstrate that the proposed method can effectively determine the intrinsic dimensionality of remote sensing images.

A faster way to compute the noise-adjusted principal components transform matrix

The matrix for the noise-adjusted principal components (NAPC) transform is the solution of a generalized symmetric eigenvalue problem. Applied to remote sensing imagery, this entails the simultaneous diagonalization of data and noise covariance matrices. One of the two PC transforms of the original NAPC transform is replaced by several short, fast procedures. The total operation count for the computation of the NAPC transform matrix is halved. >

Enhancement of high spectral resolution remote-sensing data by a noise-adjusted principal components transform

High-spectral-resolution remote-sensing data are first transformed so that the noise covariance matrix becomes the identity matrix. Then the principal components transform is applied. This transform is equivalent to the maximum noise fraction transform and is optimal in the sense that it maximizes the signal-to-noise ratio (SNR) in each successive transform component, just as the principal component transform maximizes the data variance in successive components. Application of this transform requires knowledge or an estimate of the noise covariance matrix of the data. The effectiveness of this transform for noise removal is demonstrated in both the spatial and spectral domains. Results that demonstrate the enhancement of geological mapping and detection of alteration mineralogy in data from the Pilbara region of Western Australia, including mapping of the occurrence of pyrophyllite over an extended area, are presented. >