KEYWORDS: Algorithm development, Data communications, Hyperspectral imaging, Detection and tracking algorithms, Data processing, Image processing, Signal to noise ratio, Minerals, Remote sensing, Data acquisition
Fast Iterative Pixel Purity Index (FIPPI) was previously developed to address two major issues arising in PPI which are the use of skewers whose number must be determined by a priori and inconsistent final results which cannot be reproduced. Recently, a new concept has been developed for hyperspectral data communication according to Band SeQuential (BSQ) acquisition format in such a way that bands can be collected band by band. By virtue of BSQ users are able to develop Progressive Band Processing (PBP) for hyperspectral imaging algorithms so that data analysts can observe progressive profiles of inter-band changes among bands. Its advantages have been justified in several applications, anomaly detection, constrained energy minimization, automatic target generation process, orthogonal subspace projection, PPI, etc. This paper further extends PBP to FIPPI. The idea to implement PBP-FIPPI is to use two loops specified by skewers and bands to process FIPPI. Depending upon which one is implemented in the outer loop two different versions of PBP-FIPPI can be designed. When the outer loop is iterated band by band, it is called to be called Progressive Band Processing of FIPPI (PBP-FIPPI). When the outer loop is iterated by growing skewers, it is called Progressive Skewer Processing of FIPPI (PSP-FIPPI). Interestingly, both versions provide different insights into the design of FIPPI but produce close results.
KEYWORDS: Vital signs, Data analysis, Hyperspectral imaging, Signal detection, Data modeling, Data centers, Lawrencium, Injuries, Blood, Blood pressure
Vital Sign Signals (VSSs) have been widely used for medical data analysis. One classic approach is to use Logistic Regression Model (LRM) to describe data to be analyzed. There are two challenging issues from this approach. One is how many VSSs needed to be used in the model since there are many VSSs can be used for this purpose. Another is that once the number of VSSs is determined, the follow-up issue what these VSSs are. Up to date these two issues are resolved by empirical selection. This paper addresses these two issues from a hyperspectral imaging perspective. If we view a patient with collected different vital sign signals as a pixel vector in hyperspectral image, then each vital sign signal can be considered as a particular band. In light of this interpretation each VSS can be ranked by band prioritization commonly used by band selection in hyperspectral imaging. In order to resolve the issue of how many VSSs should be used for data analysis we further develop a Progressive Band Processing of Anomaly Detection (PBPAD) which allows users to detect anomalies in medical data using prioritized VSSs one after another so that data changes between bands can be dictated by profiles provided by PBPAD. As a result, there is no need of determining the number of VSSs as well as which VSS should be used because all VSSs are used in their prioritized orders. To demonstrate the utility of PBPAD in medical data analysis anomaly detection is implemented as PBP to find anomalies which correspond to abnormal patients. The data to be used for experiments are data collected in University of Maryland, School of Medicine, Shock Trauma Center (STC). The results will be evaluated by the results obtained by Logistic Regression Model (LRM).
KEYWORDS: Image processing, Data processing, Thallium, Hyperspectral imaging, Signal processing, Image compression, Signal detection, Data communications, Data modeling, Data acquisition
Progressive band processing (PBP) processes data band by band according to the Band SeQuential (BSQ) format acquired by a hyperspectral imaging sensor. It can be implemented in real time in the sense that data processing can be performed whenever bands are available without waiting for data completely collected. This is particularly important for satellite communication when data download is limited by bandwidth and transmission. This paper presents a new concept of processing a well-known technique, Orthogonal Subspace Projection (OSP) band by band, to be called PBPOSP. Several benefits can be gained by PBP-OSP. One is band processing capability which allows different receiving ends to process data whenever bands are available. Second, it enables users to identify significant bands during data processing. Third, unlike band selection which requires knowing the number of bands needed to be selected or band prioritization PBP-OSP can process arbitrary bands in real time with no need of such prior knowledge. Most importantly, PBP can locate and identify which bands are significant for data processing in a progressive manner. Such progressive profile resulting from PBP-OSP is the best advantage that PBP-OSP can offer and cannot be accomplished by any other OSP-like operators.
KEYWORDS: Lawrencium, Vital signs, Data modeling, Signal analysis, Hyperspectral imaging, Blood, Injuries, Data processing, Data analysis, Performance modeling
This paper develops a completely new technology,) from a hyperspectral imaging perspective, called Hyperspectral Vital Sign Signal Analysis (HyVSSA. A hyperspectral image is generally acquired by hundreds of contiguous spectral bands, each of which is an optical sensor specified by a particular wavelength. In medical application, we can consider a patient with different vital sign signals as a pixel vector in hyperspectral image and each vital sign signal as a particular band. In light of this interpretation, a revolutionary concept is developed, which translates medical data to hyperspectral data in such a way that hyperspectral technology can be readily applied to medical data analysis. One of most useful techniques in hyperspectral data processing is, Anomaly Detection (AD) which in this medical application is used to predict outcomes such as transfusion, length of stay (LOS) and mortality using various vital signs. This study compared transfusion prediction performance of Anomaly Detection (AD) and Logistic Regression (LR).
OSP has been used widely in detection and abundance estimation for about twenty years. But it can’t
apply nonnegative and sum-to-one constraints when being used as an abundance estimator. Fully
constrained least square algorithm does this well, but its time cost increases greatly as the number of
endmembers grows. There are some tries for unmixing spectral under fully constraints from different
aspects recently. Here in this paper, a new fully constrained unmixing algorithm is prompted based on
orthogonal projection process, where a nearest projected point is defined onto the simplex constructed
by endmembers. It is much easier, and it is faster than FCLS with the mostly same unmixing results. It
is also compared with other two constrained unmixing algorithms, which shows its effectiveness too.
Pixel Purity Index (PPI) is a very popular endmember finding algorithm due to its availability in ENVI software. According to the band sequential (BSQ) format of data acquisition this paper introduces a new concept of executing PPI band-by-band in a progressive manner. It is called progressive band processing of PPI (PBP-PPI) which allows users to process PPI band by band without waiting for full bands of data information acquired. To accomplish this goal PPI must be capable of calculating and updating PPI counts of data samples band by band. Furthermore, progressive-band-processing progressive PPI (PBP-P-PPI) and progressive-band-processing causal PPI (PBP-C-PPI) are proposed to address the issues that the number of skewers is undefined and only partial pixels are available correspondingly. Many benefits can be gained from PBP-PPI, for example, providing progressive profiles of PPI counts of data samples as more bands are included for data processing, finding crucial bands according to progressive changes in PPI counts.
KEYWORDS: Algorithm development, Hyperspectral imaging, Signal to noise ratio, Minerals, Interference (communication), Digital imaging, Lithium, Data modeling, Distance measurement, Detection and tracking algorithms
Endmember finding has received considerable interest in hyperspectral imaging. In reality an endmember finding
algorithm (EFA) suffers from endmember variability which causes inaccuracy, inconsistency and instability. In this case
a real endmember may not exist but rather appears as its variant, referred to as virtual signature (VS). This paper
presents a new approach to finding VSs by taking endmember variability into account. It first determines a required
number of endmember classes by virtual dimensionality (VD), then designs an unsupervised method to find endmember
classes and finally develops an iterative algorithm to find VSs. Comprehensive experiments including synthetic and real
image scenes are conducted to demonstrate effectiveness of the proposed approach.
Endmember variability presents a great challenge in endmember finding since a true endmember may be contaminated by many unknown factors. This paper develops a pixel purity index (PPI) based approach to resolving this issue. It is known that endmember candidates must have their PPI counts greater than 0. Using this fact we can start with all data samples with PPI counts greater than 0 and cluster them into p endmember classes where the value of p can be determined by virtual dimensionality (VD). We further develop an endmember identification algorithm to select true endmembers from these p endmembers. So, in our proposed technique three state processes are developed. It first uses PPI to produce a set of endmember candidates and then develops a clustering algorithm to group PPI-generated endmember candidates into p endmember classes and finally concludes by designing an algorithm to extract true endmembers from the p endmember classes.
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