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.
Proc. SPIE. 8398, Optical Pattern Recognition XXIII
KEYWORDS: Optical filters, Image processing, Digital filtering, Signal processing, Color image processing, Image filtering, Image enhancement, Filtering (signal processing), Gemini Planet Imager, RGB color model
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.
Proc. SPIE. 7696, Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI
KEYWORDS: Statistical analysis, Image processing, Digital filtering, Feature extraction, Lung, Signal processing, Color image processing, Image enhancement, Filtering (signal processing), RGB color model
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.
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.
This paper describes an algorithm for the detection of low- and high-contrast targets in forward-looking infrared imagery while rejecting the effects of clutter and other associated detrimental factors. The proposed automatic target recognition algorithm involves two modules—a detector module and a clutter rejection module. The detection algorithm, based on morphology-based preprocessing, acts as a prescreener that selects possible candidate target regions for further analysis and places target-size markers in those preselected regions. The application of simple nonlinear grayscale operations in the proposed detection algorithm has been found to be especially suitable for real-time implementation. The clutter rejection module uses target- and background-specific information, extracted from training sample, to reduce false alarms often generated in the detection step. The application of two Mahalonobis distances, derived from target and background features of the training image, improves false-alarm rejection. Preliminary results indicate that the developed detection and clutter rejection modules exhibit excellent detection performance for both low- and high-contrast targets in complex backgrounds while ensuring a low false-alarm rate.
In this paper, we investigate the detection and classification of targets in forward-looking infrared (FLIR) imagery under various challenging scenarios. At first, morphological preprocessing is applied for the preliminary selection of all possible candidate target regions. Morphological operations decompose the given input image into a filtered image. Clutter rejection, i.e. the classification between desired target and background, is done by means of Probabilistic neural network (PNN). For most cases, only the samples of the desired target images are used for the training purposes, which are not adequate for cases, where the target is almost blended with the background. For instance, target like objects may be present in the region of interest (ROI) and there is very low contrast difference between target and background. Horizontal and vertical convolution with wavelet low pass filter coefficients serves to extract features for training the PNN. In this paper, an improved clutter rejecter is presented to overcome the inferior classification performance of alternate techniques for poorly centered targets by moving the marked candidate target window in suitable directions with respect to the center of the potential target patch to extract ROIs from each detected target region. Results are shown for introductory detection-classification, and on improved performance of the clutter rejecter, by considering several shifted ROIs to accurately classify the true target from the clutter. Test results confirm the excellent performance of the detector and the clutter rejecter when both target and background features are used for training, and several shifted ROIs are considered for precise classification of each ROI marked by the detector.
In this paper, we investigated automatic target detection and classification of low and high contrast targets present in unknown forward looking infrared (FLIR) image sequence. The detection algorithm, based on morphology based preprocessing, acts as a prescreener that selects possible candidate target regions, comprising both true targets and false alarms and places expected target-sized marker to those preselected regions. The application of simple non-linear grayscale operations in the proposed detection algorithm leads to real-time implementations. By considering the known target and background specific attributes, extracted from the training samples, the clutter rejection module discriminates between true target and false alarms previously identified by the detection algorithm. Two approaches are employed for object classification where one uses local features of the image and the other uses template matching technique such as image correlation. For the first approach, to extract features, we employed two methods - nonlinear filtering for texture energy measurement and wavelet decomposition by expending Daubechies high and low pass filter coefficients. Then for classification, a neural network based classifier is used. In the second approach minimax distance transform correlation filter (MDTCF) is applied that minimizes the average squared distance from the filtered true-class training images to a filtered reference image while maximizing the mean squared distance (MSD) of the filtered false-class training images to this filtered reference image. Then classification is performed using the squared distance of a filtered test image to the chosen filtered reference image. The performance of the proposed technique is analyzed for i) neural network with nonlinear texture filtering, ii) neural network with wavelet decomposition and iii) correlation filtering. Preliminary results indicate that the proposed detection algorithms can locate both hot and cold targets from cluttered background. In addition, the clutter rejecters are capable of maintaining a low false alarm rate and excellent discrimination competence. The performance of the proposed techniques has been tested with real life FLIR imagery supplied by the Army Missile Command (AMCOM).
A technique has been formulated based on hetero-associative target detection strategy that recognizes and tracks multiple dissimilar or hetero-associative targets from gray-scale image sequences taken from a moving aircraft in real time. Fringe-adjusted joint transform correlation combined with the proposed hetero-associative filter is used to enhance the correlation performance and thus ensures strong and equal cross-correlation peak for each element of the selected class. Tracking is accomplished by combining the analysis of single image frame with the determination of the motion from consecutive image frames. For efficient performance, the desired targets are identified prior to be tracked by correlating successive frames using the proposed filter which is an enhanced version of the fringe-adjusted filter. The optimality of the tracking performance is tested by MATLAB software.
The joint transform correlator (JTC) technique has been found to be suitable for real time pattern recognition applications. Among the various JTC techniques proposed in the literature, the fringe-adjusted JTC has been found to yield better correlation output for target detection. We propose a generalized fringe-adjusted JTC (GFJTC) based algorithm for efficient detection and tracking of a target in a video sequence. The proposed algorithm has been found to be suitable for near real-time recognition and tracking of a static or moving target, while accommodating the detrimental effects of background variations as well as other artifacts. The performance of the proposed technique has been verified with real life forward looking infrared (FLIR) image sequences.
A modified phase-encoded fringe-adjusted joint transform correlation technique is proposed for multiple target detection, where two joint power spectrums (JPS) are formulated utilizing a random phase mask and phase shifted random phase mask to the reference image separately. The final JPS is the difference between the phase encoded and shifted phase encoded JPS which is multiplied by the phase mask before applying the inverse Fourier transform to yield the correlation output. This technique ensures better utilization of the input/output plane space bandwidth product by yielding one delta function like correlation peak for each desired target object and no peak for non-target objects. The proposed technique can effectively detect any number of targets from noise free or noisy input scenes without changing the system parameters and without any degradation of performance. Computer simulation results verify the performance of the proposed technique.
The joint transform correlator (JTC) technique has been found to be suitable for real-time matching and tracking operations. Among the various JTC techniques proposed in the literature, the fringe-adjusted JTC has been found to yield better correlation output for target detection. In this paper, we propose a generalized fringe-adjusted JTC based algorithm for detecting and tracking a target in a video image sequence. The proposed JTC based algorithm has been found to be suitable for near real time recognition and tracking of a static or moving target. The performance of the proposed technique has been verified with real life image sequences.