Spectral unmixing is a popular tool for remotely sensed hyperspectral data interpretation and classification. It
aims at identifying the spectra of all endmembers in the scene to find the fractional abundances of pure spectral
signatures in each mixed pixel collected by an imaging spectrometer. Complete spectral unmixing exploits
the theory that the reflectance spectrum of any pixel is the result of linear combinations of the spectra of all
endmembers inside that pixel and simply solves a set of l linear equations for each pixel, where l is the number
of bands in the image. But often the estimation of all the endmember signatures may be difficult due to the
unavailability of pure spectral signatures in the original data, or inadequacy of spatial resolution. For such cases,
partial unmixing can be used where only the user chosen targets need to be mapped and the unmixing equations
are partially solved. Like complete unmixing, a pixel value in the output image of partial unmixing is proportional
to the fraction of the pixel that contains the target material. In this paper, we study the partial spectral unmixing
problem under the light of recent theoretical results published in those areas. Our experimental results, which
are conducted using real hyperspectral data sets collected by the NASA Jet Propulsion Laboratory’s Airborne
Visible Infrared Imaging Spectrometer (AVIRIS) and spectral libraries publicly available; indicate the potential
of partial unmixing techniques in the task of accurately characterizing the mixed pixels using the library spectra.
Furthermore, we provide a comparison of complete spectral unmixing and partial spectral unmixing for the oil
spill detection in the sea.
Bidimensional empirical mode decomposition (BEMD) technique decomposes an image into several bidimensional intrinsic mode functions (BIMFs) and a bidimensional residue (BR). Classical BEMD methods require some form of surface interpolation to estimate envelope surfaces, which causes various problems. On the other hand, existing surface interpolation-based BEMD methods are proposed and/or are suitable for gray-scale images only. This paper presents a novel BEMD approach for color images known as color BEMD (CBEMD), which employs order-statistics filter (OSF)-based envelope estimation to avoid some of the difficulties, otherwise encountered in classical BEMD approaches. The CBEMD can decompose a color image into several color BIMFs and a color BR based on hierarchical local spatial variation of image intensity and color. Since the color BR represents the trend of a color image in terms of global intensity and color distribution, it is utilized for trend adjustment of color images. Both formulation of the CBEMD and finding and adjusting the color image trend are known to be the first approaches in corresponding issues. Experimental results with several real images demonstrate the potential of the proposed CBEMD method for color image processing, which include trend adjustment of color images.
Image sharpening is an image processing technique that highlights transitions in intensity and/or enhances the
darker regions. This paper formulates a bidimensional empirical mode decomposition (BEMD) based spatial
domain color image sharpening. In this approach, color image is first decomposed into several hierarchical
components using BEMD, which is a multi-scale/multi-resolution technique. The hierarchical color image components
are known as color bidimensional empirical mode functions (CBEMFs), where the first CBEMF contains
the highest/finest local spatial variations, and the final CBEMF contains the color trend of an image. The final
CBEMF is also known as color bidimensional residue (CBR), whereas the other CBEMFs are known as color
bidimensional intrinsic mode functions (CBIMFs). However, instead of using classical BEMD, a modified BEMD,
known as fast and adaptive BEMD (FABEMD) is utilized, which uses order-statics filters for envelope estimation
in the process instead of surface interpolation. The BEMD developed for color images employing FABEMD is
known as color BEMD (CBEMD). Since the first CBEMF contains the finest spatial variations in the image and
the CBR contains the color trend information, manipulation of these two elements can provide useful sharpening
of a color image. In one simple approach, suitable weighting of the first CBEMF and CBR is accomplished,
where weighting is done to all three color components of these two elements. Finally, the image is reconstructed
from the addition of all the CBEMFs to obtain the primary sharpening. An additional level of sharpening is
achieved when the primarily sharpened image, as mentioned above, is added to the original image. By varying
the weights, desired color image sharpening can be achieved, which is inherently data driven.
Electrocardiography is a diagnostic procedure for the detection and diagnosis of heart abnormalities. The electrocardiogram
(ECG) signal contains important information that is utilized by physicians for the diagnosis and
analysis of heart diseases. So good quality ECG signal plays a vital role for the interpretation and identification
of pathological, anatomical and physiological aspects of the whole cardiac muscle. However, the ECG signals
are corrupted by noise which severely limit the utility of the recorded ECG signal for medical evaluation. The
most common noise presents in the ECG signal is the high frequency noise caused by the forces acting on the
electrodes. In this paper, we propose a new ECG denoising method based on the empirical mode decomposition
(EMD). The proposed method is able to enhance the ECG signal upon removing the noise with minimum
signal distortion. Simulation is done on the MIT-BIH database to verify the efficacy of the proposed algorithm.
Experiments show that the presented method offers very good results to remove noise from the ECG signal.
Bidimensional empirical mode decomposition (BEMD) decomposes an image into several bidimensional intrinsic
mode components, which is useful for various image enhancement and/or feature extraction applications. However,
because of the requirement of scattered data interpolation and associated difficulties, the classical BEMD
methods appear unsuitable for many applications. Recently, a fast and adaptive BEMD (FABEMD) method
is proposed, which alleviates some of the difficulties, otherwise encountered in classical BEMD approaches. On
the other hand, existing BEMD methods are proposed for gray scale images only. This paper first presents a
novel BEMD approach for color images known as color BEMD (CBEMD), which employs FABEMD principle
and decomposes a color image into color bidimensional intrinsic mode components based on hierarchical local
spatial variation of image intensity and color. In fact, FABEMD facilitates the extension of the BEMD process
for color images in a convenient and useful way, whereas the other interpolation based BEMD techniques appear
unsuitable for this purpose. In FABEMD, order statistics filters are employed to estimate the envelope surfaces
from the data instead of surface interpolation, which enables fast decomposition and well characterized bidimensional
intrinsic mode components. Second, the CBEMD is utilized in this paper for adjusting and/or modifying
the trend of color images. In this process, the image is reconstructed by adding the color bidimensional intrinsic
mode components after applying suitably selected weights. Test results with real images demonstrate the
potential of the proposed CBEMD method for color image processing, which include color trend adjustment.
A novel approach is proposed to recognize and track multiple identical and/or dissimilar targets in forward-looking infrared (FLIR) image sequences using a combination of an extended maximum average correlation height (EMACH) filter and polynomial distance classifier correlation filter (PDCCF). The EMACH filter and PDCCF are trained a priori using representative training images of targets with expected size and orientation variations. In the first step, the input scene is correlated with all EMACH filters (one for each desired or expected target class). Based on the regions with higher correlation peak values in the combined correlation output, a sufficient number of regions of interest (ROIs) are selected from the input scene. In the second step, a PDCCF is applied to these ROIs to identify target types and reject clutter and background. Moving-target detection and tracking is accomplished by applying this technique independently to all incoming image frames. Independent tracking of target(s) from one frame to the other allows the system to handle complicated situations such as a target disappearing in a few frames and then reappearing in later frames. This method yields robust performance for challenging FLIR imagery in terms of accurate detection and classification as well as tracking of the targets.
Simultaneous detection and classification of single/multiple identical and dissimilar targets is very important in automatic target recognition applications. A new approach is proposed for this purpose using a combination of maximum average correlation height (MACH) filter and polynomial distance classifier correlation filter (PDCCF). In this technique, full-resolution MACH filters are applied to the input scene, and the regions of interest (ROIs) containing the probable targets are selected from the input scene based on the ROIs with higher-correlation peak values in the correlation output. Then a multiclass PDCCF is applied to these ROIs to identify target types and reject clutters and/or backgrounds. To increase the robustness of the proposed technique, multiple filters are formulated for multiple ranges of target size and/or orientation variations. The simulation results using real-life imagery indicate the effectiveness of the proposed technique for target detection and classification in the presence of distortion, clutter, and other artifacts.
This paper proposes a method to detect objects of arbitrary poses and sizes from a complex forward looking infrared (FLIR) image scene exploiting image correlation technique along with the preprocessing of the scene using a class of morphological operators. This presented automatic target recognition (ATR) algorithm consists of two steps. In the first step, the image is preprocessed, by employing morphological reconstruction operators, to remove the background as well as clutter and to intensify the presence of both low or high contrast targets. This step also involves in finding the possible candidate target regions or region of interests (ROIs) and passing those ROIs to the second step for classification. The second step exploits template-matching technique such as minimax distance transform correlation filter (MDTCF) to identify the true target from the false alarms in the pre-selected ROIs after classification. The MDTCF minimizes the average squared distance from the filtered true-class training images to a filtered reference image while maximizing the mean squared distance of the filtered false-class training images to this filtered reference image. This approach increases the separation between the false-class correlation outputs and the true-class correlation outputs. Classification is performed using the squared distance of a filtered test image to the chosen filtered reference image. The proposed technique has been tested with real life FLIR image sequences supplied by the Army Missile Command (AMCOM). Experimental results, obtained with these real FLIR image sequences, illustrating a wide variety of target and clutter variability, demonstrate the effectiveness and robustness of the proposed method.
A distortion-invariant class-associative pattern recognition technique is proposed, where a class of objects may be defined as a group of objects with similarity and dissimilarity among them. The fractional power fringe-adjusted joint transform correlation technique as well as the synthetic discriminant function concept has been effectively utilized to achieve the distortion-invariant detection of multiple dissimilar targets simultaneously present in the input scene. Simulation results prove that the proposed scheme is an effective tool for the detection of multiple dissimilar targets in both binary and gray-level input scenes corrupted by distortion and noise.
Over the last two decades, researchers investigated various approaches for detection and classification of targets in forward looking infrared (FLIR) imagery using correlation based techniques. In this paper, a novel technique is proposed to recognize and track single as well as multiple identical and/or dissimilar targets in real life FLIR sequences using a combination of extended maximum average correlation height (EMACH) and polynomial distance classifier correlation filter (PDCCF). The EMACH filters are used for the detection stage and PDCCF filter is used for the classification stage for improving the detection and discrimination capability. The EMACH and PDCCF filters are trained a priori using target images with expected size and orientation variations. In the first step, the input scene is correlated with all the detection filters (one for each desired or expected target class) and the resulting correlation outputs are combined. The regions of interest (ROI) are selected from the input scene based on the regions with higher correlation peak values in the combined correlation output. In the second step, PDCCF filter is applied to these ROIs to identify target types and reject clutters/backgrounds based on a distance measure and a threshold. Moving target detection and tracking is accomplished by applying this technique independently to all incoming image frames. Independent tracking of target(s) from one frame to the other allows the system to handle complicated situations such as a target disappearing in few frames and then reappearing in later frames. This method has been found to yield robust performance for challenging FLIR imagery in terms of faster and accurate detection and classification as well as tracking of the targets.
A new technique using a combination of maximum average correlation height (MACH) filter and polynomial distance classifier correlation filter (PDCCF) for simultaneous detection and classification of single/multiple identical and dissimilar targets is proposed in this paper. In this technique, a MACH filter is formulated for each desired target class from the training images of the corresponding target with expected size and orientation variations such that the size of the filter is the same as the input scene. Then a multi-class PDCCF is formulated from the training images of all target classes such that the size of the filter is the same as the expected targets. For real time applications, the input scene is first correlated with all MACH filters and the correlation outputs are combined. The regions of interest (ROI) containing the probable targets are selected from the input scene based on the ROIs with higher correlation peak values in the combined correlation output. The PDCCF filter is then applied to these ROIs to identify target types and reject clutters and/or backgrounds. To increase the robustness of the proposed technique, multiple filters are formulated for multiple ranges of target size and/or orientation variations. This two-stage system is faster and yields more accurate results compared to the existing three-stage system, which involves wide area prescreening, detection using MACH filters, and classification using distance classifier correlation filter. The simulation results using real life imagery show that the proposed technique can detect and classify the desired targets with higher efficiency irrespective of their distortion or the number of targets present in the input scene, when compared to the alternate techniques.
Class-associative detection involves recognition of multiple dissimilar targets simultaneously present in the input scene. In this paper, synthetic discriminant function (SDF) has been incorporated in the fringe-adjusted joint transform correlation based class-associative target detection technique to make it distortion invariant. The concept of fractional power fringe-adjusted joint transform correlation (FPFJTC) has been utilized both to generate the SDF based reference images and to detect the class-associative targets using multi-target detection algorithm. FPFJTC provides mainly three different types of filters, may be termed as generalized fringe-adjusted filters (GFAF), to modify the joint power spectrum and thus facilitates the selection of appropriate filter/filters. Here we have proposed the phase-only filter variation from the GFAF at all steps for successful detection. Simulation results verify that the proposed scheme performs satisfactorily in detecting both binary and gray level images of a class irrespective of distortion.
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