Hyperspectral image classification (HSIC) has received considerable interest in recent years where most techniques are developed to classify images with background (BKG) removed by ground truth (GT). Unfortunately, in real scenarios, obtaining complete BKG knowledge is generally infeasible. Accordingly, HSIC performed with no BKG (HSIC-NB) is not realistic. Most importantly, many techniques claim to work well for HSIC-NB but perform poorly with BKG included. This article investigates issues arising from BKG in HSIC and further presents a new approach to HSIC with BKG (HSIC-B), called one class detection (OCD), which is based on the well-known hyperspectral subpixel detection technique, constrained energy minimization (CEM). In order for OCD to perform multiclass classification, OCD is further extended to hierarchical OCD (HOC) which is particularly designed to classify multiple classes in a hierarchical tree where each layer uses an iterative kernel CEM (IKCEM) or an iterative kernel target-constrained interference-minimized filter (IKTCIMF) to detect one class at a time for classification. Since M classes are classified by OCD in${M} -1$layers in a hierarchical tree, a new concept of class classification priority (CCP) derived from CEM is specifically designed to rank all the classes along the tree in a prioritized order according to their CCP scores. The experimental results demonstrate that hierarchical OCD (HOCD) works well and performs significantly better than many existing HSIC-NB methods at the expense of slightly reduced classification accuracy compared to HSIC-N methods.

This article investigates four issues, background (BKG) suppression (BS), anomaly detectability, noise effect, and interband correlation reduction (IBCR), which have significant impacts on its performance. Despite that a recently developed effective anomaly space (EAS) was designed to use data sphering (DS) to remove the second-order data statistics characterized by BKG, enhance anomaly detectability, and reduce noise effect, it does not address the IBCR issue. To cope with this issue, this article introduces band sampling (BSam) into EAS to reduce IBCR and further suppress BKG more effectively. By implementing EAS in conjunction with BSam (EAS-BSam), these four issues can be resolved altogether for any arbitrary anomaly detector. It first modifies iterative spectral–spatial hyperspectral anomaly detection (ISSHAD) to develop a new variant of ISSHAD, called iterative spectral–spatial maximal map (ISSMax), and then generalizes ISSMax to EAS-ISSMax, which further enhances anomaly detectability and noise removal. Finally, EAS-BSam is implemented to reduce IBCR. As a result, combining EAS, BSam, and ISSMax yields four versions: EAS-BSam, EAS-ISSMax, BSam-SSMAX, and EAS-BSam-SSMax. Such integration presents a great challenge because all these four versions are derived from different aspects, iterative spectral–spatial feedback process, compressive sensing, and low-rank and sparse matrix decomposition. Experiments demonstrate that EAS-BSam and EAS-BSam-SSMax show their superiority to ISSHAD and many current existing hyperspectral anomaly detection (HAD) methods.

Gaussian pyramid (GP) is a commonly used image coding technique which encodes an image as a pyramid which is stacked by a set of images with Gaussian window-reduced sizes and multiple spatial resolutions. Associated with GP a Laplacian pyramid (LP) can be also constructed to represent differential images between images in two consecutive layers of GP. Such resulting Gaussian-Laplacian pyramid (GLP) performs data compression in a lossless and lossy manner. A convolutional neural network (CNN) consists of a series of layers concatenated in a feedforward manner where each layer has a convolutional sublayer (CL) and a pooling sublayer (PL). Interestingly, each layer implemented by CL and PL in a CNN can be realized by a single layer in GP in the sense that CL and PL can be carried out by a low-pass Gaussian filter operated as a Gaussian kernel in a single layer of GP. This paper develops a new approach to hyperspectral image classification (HSIC), called Gaussian-Laplacian pyramid network (GLPN) which uses not only GP to realize CNN, but also LP to capture differential information between two consecutive layers that CNN cannot. Furthermore, by incorporating an iterative process into GLPN we can derive an iterative GLPN (IGLPN) that can be considered as a companion of a recently developed iterative random training sampling CNN (IRTS-CNN) by replacing CNN with GLPN. Since GLPN can realize CNN in a better way, it is expected that IGLPN will perform better than IRTS-CNN and also significantly reduce computational efficiency compared to IRTS-CNN.

Due to significant inter-band correlation resulting from use of hundreds of contiguous spectral bands, band selection (BS) is one of most widely used methods to reduce data dimensionality for band redundancy removal. A challenge for BS is how to design an effective criterion which can select bands with preserving crucial spectral information, while also avoiding selecting highly correlated bands. Information theory turns out to be one of best means to address such issue in terms of information redundancy, specifically, the rate distortion function (RDF) of Shannon’s 3rdnoisy source coding (or joint source and channel coding) theorem, which has been widely used in image compression/coding. This paper presents a novel unsupervised RDF-based band subset selection (RDFBSS) for hyperspectral image classification (HSIC). To accomplish this goal, a new concept of the area under an RDF curve, ARDFsimilar to the area under a receiver operating characteristic (ROC), Azdefined in hyperspectral target detection is coined and defined as a criterion for BSS. Since BSS generally requires an exhaustive search for an optimal band subset, two iterative algorithms similar to sequential (SQ) N-FINDR and successive (SC) N-FINDR for finding endmembers, called sequential (SQ) RDFBSS and successive (SC) RDFBSS, can be derived and coupled with Ardf as a criterion to find optimal band subsets. The experimental results demonstrate that RDFBSS is indeed a very effective BS method to find best possible band subsets and also performs better than most recent BS methods.

Anomaly detection (AD) requires spectral and spatial information to differentiate anomalies from their surrounding data samples. To capture spatial information, a general approach is to utilize local windows in various forms to adapt local characteristics of the background (BKG) from which unknown anomalies can be detected. This article develops a new approach, called iterative spectral–spatial hyperspectral AD (ISSHAD), which can improve an anomaly detector in its performance via an iterative process. Its key idea is to include an iterative process that captures spectral and spatial information from AD maps (ADMaps) obtained in previous iterations and feeds these anomaly maps back to the current data cube to create a new data cube for the next iteration. To terminate the iterative process, a Tanimoto index (TI)-based automatic stopping rule is particularly designed. Three types of spectral and spatial information, ADMaps, foreground map (FGMap), and spatial filtered map (SFMap), are introduced to develop seven various versions of ISSHAD. To demonstrate its full utilization in improving AD performance, a large number of extensive experiments are performed for ISSHAD along with its detailed comprehensive analysis among several most recently developed anomaly detectors, including classic, dual-window-based, low-rank representation model-based, and tensor-based AD methods for validation.

Convolution neural network (CNN) has received considerable interest in hyperspectral image classification (HSIC) lately due to its excellent spectral-spatial feature extraction capability. To improve CNN, many approaches have been directed to exploring the infrastructure of its network by introducing different paradigms. This paper takes a rather different approach by developing an iterative CNN which extends a CNN by including a feedback system to repeatedly process the same CNN in an iterative manner. Its idea is to take advantage of a recently developed iterative training sampling spectral-spatial classification (IRTS-SSC) that allows CNN to update its spatial information of classification maps through a feedback spatial filtering system via IRTS. The resulting CNN is called iterative random training sampling CNN (IRTS-CNN) with several unique features. First, IRTS-CNN combines CNN and IRTS-SSC into one paradigm, an architecture which has never investigated in the past. Second, it implements a series of spatial filters to capture spatial information of classified data samples and further feeds this information back via an iterative process to expand the current input data cube for the next iteration. Third, it utilizes the expanded data cube to randomly re-select training samples and then to re-implement CNN iteratively. Last but not least, IRTS-CNN provides a general framework which can implement any arbitrary CNN as an initial classifier to improve its performance through an iterative process. Extensive experiments are conducted to demonstrate that IRTS-CNN indeed significantly improves CNN, specifically, when only a small size of limited training samples is used.

Due to significant inter-band correlation resulting from the use of hundreds of contiguous spectral bands, band selection (BS) is commonly used to reduce data dimensionality for band redundancy removal. A challenge for BS is how to design an effective criterion which can select bands with crucial self-retained spectral information, while also avoiding highly correlated bands to be selected. This article presents a novel approach, referred to as self-mutual information-based band selection (SMI-BS) for hyperspectral image classification (HSIC) to address these two issues. It first constructs a hyperspectral band channel from a hyperspectral image and then takes advantage of such a band channel to coin a new concept of SMI, which is defined as the mutual information (MI) between a selected band, b, and the set of full bands, Ω, I(b; Ω). As a result, a curve plotted as a function of I(b; Ω) versus individual band b, called SMI curve, can be used as a BS criterion which selects those bands with large I(b; Ω) values as desired bands. Since such selected bands may be highly correlated, another new concept, called prominent band (PB), which is defined as a band corresponding to a prominent peak of an SMI curve, is further introduced to avoid selecting highly inter-correlated spectral bands. To validate the utility of SMI-BS in HSIC, experiments are conducted to compare existing state-of-the-art BS methods for performance evaluation. The results demonstrate that SMI-BS is indeed a very effective BS method and also performs better than other test BS methods.

Hyperspectral image classification (HSIC) has received considerable interest in recent years. In particular, spectral-spatial classification methods are proposed to jointly consider spectral and spatial together. However, one of challenging issues in hyperspectral image classifications is the random training sample selection which produces inconsistent results. A general approach to resolving this problem is so-called k-fold method which implements randomly selected training samples k times and takes their average with respect to the standard deviation to be used describe a confidence interval. This paper develops an approach to mitigating such a random issue by introducing an iterative process to remove uncertainty caused by randomness. Its idea is to repeatedly feedback the classification results in an iterative manner that the randomness caused by the randomly selected samples can be largely reduced. The iterative process is terminated as long as the classification results obtained by two consecutive iterations agree with a prescribed tolerance. Experimental results demonstrate that our proposed method works very effectively not only to reduce result inconsistency but also to improve classification results.

Urban impervious surface area (ISA) is a key index toward urban eco-system and sustainable urban planning strategy. In this paper, a subpixel approach is proposed to estimate urban ISA values using linear spectral mixture analysis (LSMA)-based hyperspectral imaging techniques. In doing so the concept of virtual dimensionality (VD) is used to first estimate the number of endmembers, then an endmember finding approach is implemented to find VD-determined number of endmembers in a hyperspectral image. Finally, nonnegativity constrained least squares (NCLS) is performed for endmember unmixing. The hyperspectral image used in our approach provides a larger number of spectral dimensions than a multispectral image does so that a sufficient number of endmembers can be found from a hyperspectral image for ISA estimation. What is more, a relationship between ISA values and fractional endmember abundances can be further constructed by linear regression.

Monitoring of non-invasive, continuous, high-resolution patient vital signs (VS) such as heart rate and oxygen saturation is becoming increasingly common in hospital settings. These data are a potential boon for health informatics as a source of predictive information about a variety of patient outcomes. Yet the volume, noisiness, and per-patient idiosyncrasies of these data make their use extremely challenging. This paper explores the utility of representing VS data as unordered collections (bags) of local discrete patterns for the purpose of training classifiers to predict outcomes for traumatic brain injury patients, including mortality and level of cognitive function months after hospital discharge. The Symbolic Aggregate approXimation (SAX) algorithm is used for discretization, producing a bag of SAX "words" (local patterns) for each time series. Experiments with a dataset of sixty traumatic brain injury patients demonstrate that this approach is promising both in terms of predictive accuracy and patterns that it can reveal in the underlying VS data.

More than a decade of research has produced numerous representations and similarity measures to support time series classification and clustering. Yet most of the work in the field is so focused on the representation or similarity measure that it ignores the possibility of improving performance using ensembles of representations or classifiers. This paper explores ways of exploiting representational diversity for time series classification via ensembles of representations. We focus on the Symbolic Aggregate approXimation (SAX) discretization method coupled with the bag-of-patterns (BoP) representation because of their state-of-the-art performance in the single representation/classifier case. Experiments with a number of standard benchmark time series datasets and a new dataset of vital signs collected from patients suffering from traumatic brain injury demonstrate the power of the ensemble approaches. The result is a single method that is often significantly better than vanilla SAX/BoP and compares favorably on a per dataset basis with the best methods reported in the literature for each dataset.

Continuous, automated, electronic patient vital signs data are important to physicians in evaluating traumatic brain injury (TBI) patients' physiological status and reaching timely decisions for therapeutic interventions. However, missing values in the medical data streams hinder applying many standard statistical or machine learning algorithms and result in losing some episodes of clinical importance. In this paper, we present a novel approach to filling missing values in streams of vital signs data. We construct sequences of Hankel matrices from vital signs data streams, find that these matrices exhibit low-rank, and utilize low-rank matrix completion methods from compressible sensing to fill in the missing data. We demonstrate that our approach always substantially outperforms other popular fill-in methods, like k-nearest-neighbors and expectation maximization. Further, we show that our approach recovers thousands of simulated missing data for intracranial pressure, a critical stream of measurements for guiding clinical interventions and monitoring traumatic brain injuries.

Distributed planning and monitoring relies on wide, lateral information access that may promote anticipatory behaviour, opportunistic planning and redundant checking and monitoring. The reliability of the resulting system performance is thus enhanced. However, information access control is often critical given the wide adoption of information technology. After presenting findings of several field studies related to the strategies used by distributed team members in managing information access control, we highlight how inefficiencies in information flows are exploited to achieve information access control. We then present implementation strategies for a video-based coordination platform to resolve the trade-off between information access and privacy. In particular, a role-based assignment of information access, along with mechanisms of controlling levels of information access was used to balance the potential loss of privacy with the gain in coordination efficiency.

Healthcare environment is a prime example of collaborative work, in which work is organized both at the macro-level over days and weeks as well as micro-level over hours and minutes. Coordination is carried out jointly by those who often share the ultimate goal of providing best care to the patient while at the same time have different perspectives. Additionally, uncertainty and contingencies often arise to disrupt the best plans. Based on the phenomenology observed in coordination for day of surgery management, we illustrate strategies employed by healthcare workers to enhance operational robustness and reliability. Based on the insight learned, a video-based toolset was developed and deployed in a Level-I trauma center to enhance distributed monitoring. Initial trials showed that the toolset was highly received.