Hyperspectral Pushbroom Research Articles

Investigation of fungal development in maize kernels using NIR hyperspectral imaging and multivariate data analysis

Near infrared (NIR) hyperspectral imaging and hyperspectral image analysis were evaluated for their potential to track changes in fungal contamination on and fungal activity immediately under the surface of whole maize kernels (Zea mays L.) infected with Fusarium verticillioides. Hyperspectral images of clean and infected kernels were acquired using a SisuChema hyperspectral pushbroom imaging system with a spectral range of 1000–2498 nm at predetermined time intervals after infection. Background, bad pixels and shading of acquired absorbance images were removed using exploratory principal component analysis (PCA). When plotting PC4 against PC5, with percentage sum of squares (%SS) 0.49% and 0.34%, three distinct clusters were apparent in the score plot and this was associated with degree of infection. Loading line plots, with prominent peaks at 1900 nm and 2136 nm, confirmed that the source of variation was due to changes in starch and protein. Partial least squares (PLS) regression models, with time as the Y variable, were calculated and also indicated that changes over time were apparent. Variable importance plots (VIP) confirmed the peaks observed in the PCA loading line plots. More systematic future experiments are needed to confirm this, but it can already be concluded that early detection of fungal contamination and activity is possible.

THE APPLICATION OF HYPERSPECTRAL DATA TO THE DETECTION AND IDENTIFICATION OF RED TIDES

In recent years, repeated red tides have caused great damage to the coastal areas of China. As a much more important manner for red tide monitoring than any other remote sensing method, Pushbroom Hyperspectral Imager (PHI) can obtain abundant high resolution and continuous hyperspectral data. In this paper, through the analysis of the hyperspectral data acquired above the North China Sea by airplane from June to August of 2001, the methods for detecting and identifying red tide water are discussed on the basis of creating the fault color image, extracting the reflectance curve of the abnormal district and constructing the correlative function.

Operational Atmospheric Correction for Imaging Spectrometers Accounting for the Smile Effect

Hyperspectral pushbroom imagers are affected by a number of artifacts, such as pixel nonuniformity, spectral smile, and keystone. These have to be taken into account during system correction, orthorectification, or atmospheric correction, as performed in processing and archiving facilities (PAFs). This contribution is presenting an efficient and accurate smile correction method integrated in the atmospheric correction. The proposed technique will be used in the PAF of the German hyperspectral Environmental Mapping and Analysis Program mission. The spectral smile shift across the detector array is parametrized with a fourth-order polynomial function for each channel based on the instrument optical design model or measured laboratory data. Alternatively, spectral smile shifts can be calculated from image data using channels in atmospheric absorption regions. The concept for the time-optimized processor is outlined, and the results are presented for simulated EnMAP data and existing pushbroom imagery [HYPERION, AISA (Airborne Imaging Spectrometer for Applications), and HYSPEX (Hyperspectral Camera)].

Indirect Detection of Fusarium Verticillioides in Maize (Zea mays L.) Kernels by near Infrared Hyperspectral Imaging

Near infrared (NIR) hyperspectral imaging and hyperspectral image analysis were evaluated for their potential to distinguish between Fusarium verticillioides infected and sound whole maize ( Zea mays L.) kernels. Hyperspectral images of infected and sound kernels were acquired using a Spectral Dimensions MatrixNIR camera with a spectral range of 960–1662nm and a sisuChema hyperspectral pushbroom imaging system with a spectral range of 1000–2498 nm. Background, bad pixels and shading were removed using exploratory principal component analysis (PCA) on absorbance images. PCA could be used effectively on the cleaned images to identify classes including infected and non-infected regions on individual kernels. A distinct difference between infected and sound kernels along principal component (PC) one with two distinguishable clusters was found. The loading line plot of the first PC of the sisuChema hypercube showed important absorbance peaks for the two classes, i.e. 1960nm and 2100nm for the infected class and U50 nm, 2300nm and 2350nm for the non-infected class. Partial least squares discriminant analysis (PLS-DA) was applied. The coefficient of determination was 0.73 for the MatrixNIR image and 0.86 for the sisuChema image, both after three PLS components. These PLS-DA models could be used to calculate predictions on a test set image. The predictions could be shown as prediction images and an acceptable root mean square error of prediction was obtained (0.23). NIR hyperspectral imaging has the potential to be used as a rapid, objective means of indentifying fungal infected maize kernels and infected regions.

Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis

The use of near infrared (NIR) hyperspectral imaging and hyperspectral image analysis for distinguishing between hard, intermediate and soft maize kernels from inbred lines was evaluated. NIR hyperspectral images of two sets (12 and 24 kernels) of whole maize kernels were acquired using a Spectral Dimensions MatrixNIR camera with a spectral range of 960–1662 nm and a sisuChema SWIR (short wave infrared) hyperspectral pushbroom imaging system with a spectral range of 1000–2498 nm. Exploratory principal component analysis (PCA) was used on absorbance images to remove background, bad pixels and shading. On the cleaned images, PCA could be used effectively to find histological classes including glassy (hard) and floury (soft) endosperm. PCA illustrated a distinct difference between glassy and floury endosperm along principal component (PC) three on the MatrixNIR and PC two on the sisuChema with two distinguishable clusters. Subsequently partial least squares discriminant analysis (PLS-DA) was applied to build a classification model. The PLS-DA model from the MatrixNIR image (12 kernels) resulted in root mean square error of prediction (RMSEP) value of 0.18. This was repeated on the MatrixNIR image of the 24 kernels which resulted in RMSEP of 0.18. The sisuChema image yielded RMSEP value of 0.29. The reproducible results obtained with the different data sets indicate that the method proposed in this paper has a real potential for future classification uses.

Dimensionality Reduction Based on Clonal Selection for Hyperspectral Imagery

A new stochastic search strategy inspired by the clonal selection theory in an artificial immune system is proposed for dimensionality reduction of hyperspectral remote-sensing imagery. The clonal selection theory is employed to describe the basic features of an immune response to an antigenic stimulus in order to meet the requirement of diversity in the antibody population. In our proposed strategy, dimensionality reduction is formulated as an optimization problem that searches an optimum with less number of features in a feature space. In line with this novel strategy, a feature subset search algorithm, clonal selection Feature-Selection (CSFS) algorithm, and a feature-weighting algorithm, Clonal-Selection Feature-Weighting (CSFW) algorithm, have been developed. In the CSFS, each solution is evolved in binary space, and the value of each bit is either 0 or 1, which indicates that the corresponding feature is either removed or selected, respectively. In CSFW, each antibody is directly represented by a string consisting of integer numbers and their corresponding weights. These algorithms are compared with the following four well-known algorithms: sequential forward selection, sequential forward floating selection, genetic-algorithm-based feature selection, and decision-boundary feature extraction using the hyperspectral remote-sensing imagery acquired by the Pushbroom Hyperspectral Imager and the Airborne Visible/Infrared Imaging Spectrometer, respectively. Experimental results demonstrate that CSFS and CSFW outperform other algorithms and hence provide effective new options for dimensionality reduction of hyperspectral remote-sensing imagery.