Pesticide residue detection in agriculture crops is a challenging issue and is even more difficult to quantify pesticide residue resident in agriculture produces and fruits. This paper conducts a series of base-line experiments which are particularly designed for three specific pesticides commonly used in Taiwan. The materials used for experiments are single leaves of vegetable produces which are being contaminated by various amount of concentration of pesticides. Two sensors are used to collected data. One is Fourier Transform Infrared (FTIR) spectroscopy. The other is a hyperspectral sensor, called Geophysical and Environmental Research (GER) 2600 spectroradiometer which is a batteryoperated field portable spectroradiometer with full real-time data acquisition from 350 nm to 2500 nm. In order to quantify data with different levels of pesticide residue concentration, several measures for spectral discrimination are developed. Mores specifically, new measures for calculating relative power between two sensors are particularly designed to be able to evaluate effectiveness of each of sensors in quantifying the used pesticide residues. The experimental results show that the GER is a better sensor than FTIR in the sense of pesticide residue quantification.
KEYWORDS: Data communications, Spectral data processing, Data processing, Thallium, Data compression, Satellite communications, Remote sensing, Image processing, Data analysis, Filtering (signal processing)
Band selection (BS) has advantages over data dimensionality in satellite communication and data
transmission in the sense that the spectral bands can be tuned by users at their discretion for data analysis
while keeping data integrity. However, to materialize BS in such practical applications several issues need
to be addressed. One is how many bands required for BS. Another is how to select appropriate bands. A third one is how to take advantage of previously selected bands without re-implementing BS. Finally and most importantly is how to tune bands to be selected in real time as number of bands varies. This paper presents an application in spectral unmixing, progressive band selection in linear spectral unmixing to address the above-mentioned issues where data unmixing can be carried out in a real time and progressive fashion with data updated recursively band by band in the same way that data is processed by a Kalman filter.
Anomaly detection generally requires real time processing to find targets on a timely basis. However, for an algorithm to be a real time processing it can only use data samples up to the sample currently being visited and no future data samples can be used for data processing. Such a property is generally called “causality”, which has unfortunately received little interest in the past. Recently, a causal anomaly detector derived from a well-known anomaly detector, called RX detector, referred to as causal RXD (C-RXD) was developed for this purpose where the sample covariance matrix, K used in RXD was replaced by the sample correlation matrix, R(n) which can be updated up to the currently being visited data sample, rn. However, such proposed C-RXD is not a real processing algorithm since the inverse of the matrix R(n), R-1(n) is recalculated by entire data samples up to rn. In order to implement C-RXD the matrix R(n) must be
carried out in such a fashion that the matrix R-1(n) can be updated only through previously calculated R-1(n-1) as well as the currently being processed data sample rn. This paper develops a real time processing of CRXD, called real time causal anomaly detector (RT-C-RXD) which is derived from the concept of Kalman filtering via a causal update equation using only innovations information provided by the pixel currently being processed without re-processing previous pixels.
KEYWORDS: Field programmable gate arrays, Computer simulations, Hyperspectral imaging, Signal processing, Clocks, Algorithm development, Principal component analysis, MATLAB, Computer architecture, Digital signal processing
N-FINDR has been widely used for endmember extraction in hyperspectral imagery. Due to its high computational
complexity developing fast computing N-FINDR has become interest. One approach is to design field programmable
gate array (FPGA) architecture for N-FINDR to reduce computing time. However, two major issues still need to be
addressed. One is that the number of endmembers must be fixed regardless of applications. The other is computation of
simplex volumes. This paper investigates a progressive version of N-FINDR, previously known as simplex growing
algorithm (SGA) for its FPGA implementation which basically resolves these two issues.
Endmember extraction has recently received considerable attention in hyperspectral data exploitation since they
represent crucial and vital information for hyperspectral data analysis. So far, no work has been reported on how to
implement endmember extraction algorithms in real-time. An endmember is defined as an idealized signature and may
or may not exist as a data sample or an image pixel. The interest of endmember extraction arises in the use of hundreds
of contiguous spectral channels that allows a hyperspectral imaging sensor to uncover many subtle substances in
diagnostic bands. However, finding such substances also presents a great challenge to hyperspectral data analysts and
becomes imperative when it comes to satellite communication if a hyperspectral imaging sensor is operated in space
platform where bandwidths used by satellite links may be very limited and downloading all the data may not be realistic
in many practical applications. In order to address this need many endmember extraction algorithms have been
developed and designed in the past, but no work has been reported on how to implement endmember extraction
algorithms in real-time. This paper investigates this issue in designing algorithms for real time processing of endmember
extraction and developed several endmember extraction algorithms derived from the widely used N-finder algorithm (NFINDR)
that can be implemented in real time.
Many hyperspectral measures such as Spectral Angle Mapper (SAM), Euclidean Distance (ED), Spectral Information
Divergence (SID) calculate values that can be used to measure the closeness between two hyperspectral signatures in
terms of spectral similarity. When a hyperspectral measure is used for target discrimination or classification, a
commonly used approach assigns a data sample to a spectral signature with the maximum spectral similarity or a class
whose mean is most similar to its spectral signature. As a result, such a hyperspectral measure is called a hard decision
hyperspectral measure and its performance is evaluated by its confusion matrix. This paper develops a new class of
hyperspectral measures, called soft-decision hyperspectral measures which use the similarity value between a data
sample and a target signature or a class as an indicator of the likelihood of the data sample assigned to this particular
signature or class instead of signature or class map resulting from hard decisions. In order for a soft-decision to perform
target discrimination or classification, the soft-decision hyperspectral measure-generated likelihood values are
normalized to probabilities so that a threshold can be used to make hard decisions via a recently developed 3D ROC
analysis. Experimental study demonstrates that these two approaches yield different results.
Pixel Purity Index (PPI) has been widely used in endmember extraction. While it is available in ENVI software there are
several interesting issues arising in its implementation. This paper re-invents the wheel by re-visiting the design rationale
of the PPI and re-designing algorithms to implement PPI. More specifically, it develops the so-called Causal PPI (CPPI)
which implements the PPI in a causal manner in the sense that the information used for data processing is only up to the
data sample currently being visited. If the time required for computer processing is negligible, the CPPI actually
becomes a real time PPI. The proposed CPPI can be implemented automatically and resolves the main issue of requiring
human intervention to determine parameters.
N-finder algorithm (N-FINDR) is probably one of most popular and widely used algorithms used for endmember
extraction. Three major obstacles need to be overcome in its practical implementation. One is that the number of
endmembers must be known a priori. A second one is the use of random initial endmembers to initialize N-FINDR,
which results in inconsistent final results of extracted endmembers. A third one is its very expensive computational cost
caused by an exhaustive search. While the first two issues can be resolved by a recently developed concept, virtual
dimensionality (VD) and custom-designed initialization algorithms respectively, the third issue seems to remain
challenging. This paper addresses the latter issue by re-designing N-FINDR which can generate one endmember at a
time sequentially in a successive fashion to ease computational complexity. Such resulting algorithm is called
SeQuential N-FINDR (SQ N-FINDR) as opposed to the original N-FINDR referred to as SiMultaneous N-FINDR (SM
N-FINDR) which generates all endmembers simultaneously at once. Two variants of SQ N-FINDR can be further
derived to reduce computational complexity. Interestingly, experimental results show that SQ N-FINDR can perform as
well as SM-N-FINDR if initial endmembers are appropriately selected.
Endmember extraction has received considerable interest in recent years. Many algorithms have been developed for this
purpose and most of them are designed based on convexity geometry such as vertex or endpoint projection and
maximization of simplex volume. This paper develops statistics-based approaches to endmember extraction in the sense
that different orders of statistics are used as criteria to extract endmembers. The idea behind the proposed statistics-based
endmember extraction algorithms (EEAs) is to assume that a set of endmmembers constitute the most un-correlated
sample pool among all the same number of signatures with correlation measured by statistics which include variance
specified by 2nd order statistics, least squares error (LSE) also specified by 2nd order statistics (variance), 3rd order
statistics (skewness), 4th order statistics (kurtosis), kth moment, entropy specified by infinite order of statistics and
statistical independency measured by mutual information. Of particular interest are Independent Component Analysis-based
EEAs which use statistics of various orders such as variance, skewness, kurtosis the kth moment and infinite orders
including entropy and divergence. In order to substantiate proposed statistics-based EEAs, experiments using synthetic
and real images are conducted in comparison with several popular and well-known EEAs such as Pixel Purity Index
(PPI), N-finder algorithm (N-FINDR).
KEYWORDS: Detection and tracking algorithms, Minerals, Interference (communication), Error analysis, Data modeling, Signal processing, Signal detection, Signal to noise ratio, Independent component analysis, Array processing
A recently introduced concept, virtual dimensionality (VD) has been shown promise in many applications of hyperspectral data exploitation. It was originally developed for estimating number of spectrally distinct signal sources. This paper explores utility of the VD from various signal processing perspectives and further investigates four techniques, Gershgorin radius (GR), orthogonal projection subspace (OSP), signal subspace estimation (SSE), Neyman-Pearson detection (NPD), to be used to estimate the VD. In particular, the OSP-based VD estimation technique is new and has several advantages over other methods. In order to evaluate their performance, a comparative study and analysis is conducted via synthetic and real image experiments.
Endmember extraction has received considerable interest in recent years. Of particular interest is the Pixel Purity Index (PPI) because of its publicity and availability in ENVI software. There are also many variants of the PPI have been developed. Among them is an interesting endmember extraction algorithm (EEA), called vertex component analysis (VCA) developed by Dias and Nascimento who extend the PPI to a simplex-based EEA while using orthogonal subspace projection (OSP) as a projection criterion rather than simplex volume used by another well-known EEA, N-finder algorithm (N-FINDR) developed by Winter. Interestingly, this paper will show that the VCA is essentially the same algorithm, referred to as Automatic Target Generation Process (ATGP) recently developed for automatic target detection and classification by Ren and Chang except the use of the initial condition to initialize the algorithm. In order to substantiate our findings, experiments using synthetic and real images are conducted for a comparative study and analysis.
An endmember is an idealized, pure signature for a class and provides crucial information for hyperspectral image
analysis. Recently, endmember extraction has received considerable attention in hyperspectral imaging due to
significantly improved spectral resolution where the likelihood of a hyperspectral image pixel uncovered by a
hyperspectral image sensor as an endmember is substantially increased. Many algorithms have been proposed for this
purpose. One great challenge in endmember extraction is the determination of number of endmembers, p required for an
endmember extraction algorithm (EEA) to generate. Unfortunately, this issue has been overlooked and avoided by
making an empirical assumption without justification. However, it has been shown that an appropriate selection of p is
critical to success in extracting desired endmembers from image data. This paper explores methods available in the
literature that can be used to estimate the value, p. These include the commonly used eigenvalue-based energy method,
An Information criterion (AIC), Minimum Description Length (MDL), Gershgorin radii-based method, Signal Subspace
Estimation (SSE) and Neyman-Pearson detection method in detection theory. In order to evaluate the effectiveness of
these methods, two sets of experiments are conducted for performance analysis. The first set consists of synthetic imagebased
simulations which allow us to evaluate their performance with apriori knowledge, while the second set
comprising of real hyperspectral image experiments which demonstrate utility of these methods in real applications.
Endmenber extraction has received increasing interests in hyperspectral image analysis. Two major issues are of interest. One is determination of endmembers, p, required to be generated and the other is generation of initial endmembers. Since most endmember extraction algorithms (EEAs) use randomly generated vectors as their initial endmembers to initialize their algorithms, their final generated endmembers are generally determined by these random initial endmembers. As a result, a different set of random initial endmembers may well likely produce a different final set of desired endmembers. This paper converts this disadvantage to an advantage and further resolves the above-mentioned two issues. Due to the random nature of initial endmembers, the proposed idea is to implement an EEA as a random algorithm so that a single run using a random set of initial endmembers is considered as a realization of a random algorithm. As a result, if an EEA is implemented several times with different sets of random initial endmembers, the intersection of their final generated endmembers in all runs should contain the desired endmembers. An EEA is then terminated when their produced intersections converge to the same set of desired endmembers. In this case, there is no need to determine the p. An EEA implemented in such a manner is called automatic EEA (AEEA). Two commonly used EEAs, pixel purity index (PPI) and N-finder algorithm (N-FINDR), are extended to AEEAs along with a new automatic ICA-based EEA. Experimental results demonstrate that the AEEA performs at least as well as their counterparts.
The US Army Joint Service Agent Water Monitor (JSAWM) program is currently interested in an approach that can implement a hardware- designed device in ticket-based hand-held assay (currently being developed) used for chemical/biological agent detection. This paper presents a preliminary investigation of the proof of concept. Three components are envisioned to accomplish the task. One is the ticket development which has been undertaken by the ANP, Inc. Another component is the software development which has been carried out by the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County (UMBC). A third component is an embedded system development which can be used to drive the UMBC-developed software to analyze the ANP-developed HHA tickets on a small pocket-size device like a PDA. The main focus of this paper is to investigate the third component that is viable and is yet to be explored. In order to facilitate to prove the concept, a flatbed scanner is used to replace a ticket reader to serve as an input device. The Stargate processor board is used as the embedded System with Embedded Linux installed. It is connected to an input device such as scanner as well as output devices such as LCD display or laptop etc. It executes the C-Coded processing program developed for this embedded system and outputs its findings on a display device. The embedded system to be developed and investigated in this paper is the core of a future hardware device. Several issues arising in such an embedded system will be addressed. Finally, the proof-of-concept pilot embedded system will be demonstrated.
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