A Novel Approach to Supervised Hyperspectral Image Classification

A novel technique is proposed for data fusion of earth remote sensing. The method is developed for land cover classification based on fusion of remote sensing images of the same scene collected from multiple sources. It presents a framework for fusion of multisource remote sensing images, which consists of two algorithms, referred to as the greedy modular eigenspace (GME) and the feature scale uniformity transformation (FSUT). The GME method is designed to extract features by a simple and efficient GME feature module, while the FSUT is performed to fuse most correlated features from different data sources. Finally, an optimal positive Boolean function based multiclass classifier is further developed for classification. It utilizes the positive and negative sample learning ability of the minimum classification error criteria to improve classification accuracy. The performance of the proposed method is evaluated by fusing MODIS/ASTER airborne simulator (MASTER) images and the airborne synthetic aperture radar (SAR) images for land cover classification during the PacRim II campaign. Experimental results demonstrate that the proposed fusion approach is an effective method for land cover classification in earth remote sensing, and improves the precision of image classification significantly compared to conventional single source classification.

Greedy modular eigenspaces (GME) has been developed for the band selection of hyperspectral images (HSI). GME attempts to greedily select uncorrelated feature sets from HSI. Unfortunately, GME is hard to find the optimal set by greedy operations except by exhaustive iterations. The long execution time has been the major drawback in practice. Accordingly, finding an optimal (or near-optimal) solution is very expensive. In this study we present a novel parallel mechanism, referred to as parallel particle swarm optimization (PPSO) band selection, to overcome this disadvantage. It makes use of a new particle swarm optimization scheme, a well-known method to solve the optimization problems, to develop an effective parallel feature extraction for HSI. The proposed PPSO improves the computational speed by using parallel computing techniques which include the compute unified device architecture (CUDA) of graphics processor unit (GPU), the message passing interface (MPI) and the open multi-processing (OpenMP) applications. These parallel implementations can fully utilize the significant parallelism of proposed PPSO to create a set of near-optimal GME modules on each parallel node. The experimental results demonstrated that PPSO can significantly improve the computational loads and provide a more reliable quality of solution compared to GME. The effectiveness of the proposed PPSO is evaluated by MODIS/ASTER airborne simulator (MASTER) HSI for band selection during the Pacrim II campaign.

Band selection for hyperspectral images is an effective technique to mitigate the curse of dimensionality. A variety of band selection methods have been suggested in the past. This paper presents a novel band prioritization based on impurity function (IF) for the band selection of hyperspectral images. The proposed IF band selection (IFBS) is incorporated with particle swarm optimization (PSO) band selection which has been developed to effectively group highly correlated bands of hyperspectral images into high corrected modules. It uses a particle swarm optimization scheme, which is a well-known method to solve the optimization problems, to develop an effective feature extraction algorithm for hyperspectral imagery. After PSO method is applied to the band reduction of hyperspectral images, the proposed IFBS is applied to enhance the efficiency of band selection. The propose method is evaluated by MODIS/ASTER airborne simulator (MASTER) for land cover classification during the Pacrim II campaign. The performance of IFBS is validated by the supervised k-nearest neighbor (KNN) classifier. Experimental results demonstrate that the proposed IFBS approach is an effective method for dimensionality reduction and feature extraction. Compared to other band selection methods, IFBS can effectively select the most significant bands for the image classification of hyperspectral images.

In this research, hyperspectral and multispectral images were utilized for vegetation studies in the greenbelt corridor near Denton. EO-1 Hyperion was the hyperspectral image and Landsat Thematic Mapper (TM) was the multispectral image used for this research. In the first part of the research, both the images were classified for land cover mapping (after necessary atmospheric correction and geometric registration) using supervised classification method with maximum likelihood algorithm and accuracy of the classification was also assessed for comparison. Hyperspectral image was preprocessed for classification through principal component analysis (PCA), segmented principal component analysis and minimum noise fraction (MNF) transform. Three different images were achieved after these pre-processing of the hyperspectral image. Therefore, a total of four images were classified and assessed the accuracy. In the second part, a more precise and improved land cover study was done on hyperspectral image using linear spectral unmixing method. Finally, several vegetation constituents like chlorophyll a, chlorophyll b, caroteoids were distinguished from the hyperspectral image using feature-oriented principal component analysis (FOPCA) method and which component dominates which type of land cover particularly vegetation were correlated.

In order to quickly distinguish infertile eggs from fertile eggs, the hyperspectral imaging technology consisting of imaging and spectral information was used for detecting the fertile information of eggs. Before hatching eggs were incubated, a hyperspectral imaging system (wavelength between 400 to 1 000 nm) was used to acquire the images one-by-one manually. The characteristic information of ratios of length to short axis, elongation, roundness and the ratios of the yolk area to the whole area was extracted based on the images. The normalization method was used as the spectral data preprocessing, and then 155 spectral characteristic variables were extracted from 520 nm waveband through the correlation coefficient method. Principal component analysis (PCA) method was adopted to reduce the dimensions of image-spectrum fusion information; the top six principal components were extracted. Support vector machine (SVM) method was used to establish classification of fertile and infertile eggs models, which are based on image, spectrum and image-spectrum fusion information respectively. The accuracy rates of the SVM models were 84.00%, 90.00% and 93.00% respectively. The experimental results show that the model based on image-spectrum fusion information technology is superior to the single information model. Hyperspectral transmission imaging technology is effective and feasible to detect the fertile hatching eggs before incubation. Keywords: hyperspectral image, hatching eggs, information fusion, nondestructive detection DOI: 10.3965/j.ijabe.20150804.1672 Citation: Zhu Z H, Liu T, Xie D J, Wang Q H, Ma M H. Nondestructive detection of infertile hatching eggs based on spectral and imaging information. Int J Agric & Biol Eng, 2015; 8(4): 69－76.

The most standard blind source separation (BSS) methods address the situation when a set of signals are available, e.g. from measurements, and all of them are linear memoryless combinations, with unknown coefficient values, of the same limited set of unknown source signals. BSS methods aim at estimating these unknown source signals and/or coefficients. This generic problem is e.g. faced in the field of Earth observation (where it is also called “unsupervised unmixing”), when considering the commonly used (over)simplified model of hyperspectral images. Each pixel of such an image has an associated reflectance spectrum derived from measurements, which is defined by the fraction of sunlight power reflected by the corresponding Earth surface at each wavelength. Each source signal is then the single reflectance spectrum associated with one of the classes of pure materials which are present in the region of Earth corresponding to the overall considered hyperspectral image. Besides, the associated coefficients define the surfaces on Earth covered with each of these pure materials in each sub-region corresponding to one pixel of the considered image. However, real hyperspectral data e.g. obtained in urban areas have a much more complex structure than the above basic model: each class of pure materials (e.g. roof tiles, trees or asphalt) has so-called spectral or intra-class variability, i.e. it yields a somewhat different spectral component in each pixel of the image. In this complex framework, this chapter shows that Principal Component Analysis (PCA) and its proposed extension are of high interest at three stages of our investigation. First, PCA allows us to analyze the above-mentioned spectral variability of real high-dimensional hyperspectral data and to propose an extended data model which is suited to these complex data. We then develop a versatile extension of BSS methods based on Nonnegative Matrix Factorization, which adds the capability to handle arbitrary forms of intra-class variability by transposing PCA concepts to this original version of the BSS tramework. Finally, PCA again proves to be very well suited to analyzing the high-dimensional data obtained as the result of the proposed BSS method.

Near-infrared hyperspectral reflectance imaging for detection of bruises on pickling cucumbers

Rooibos (Aspalathus linearis) and Honeybush (Cyclopia species) are teas indigenous to South Africa that are popularly consumed locally as well as internationally for its purported health benefits. Studies have confirmed anti-oxidant, antimutagenic and anticancer activity mainly due to the presence of polyphenols. Hyperspectral imaging (HSI) integrates conventional spectroscopy and imaging to obtain spectral and spatial information from a sample. The potential of short wave infrared (SWIR) hyperspectral imaging in combination with chemometric data analysis as a rapid quality control method for commercially important herbal teas was investigated. Images were acquired with the sisuChema short wave infrared (SWIR) hyperspectral pushbroom imaging system at a spectral range of 920 – 2514nm. Principal Component Analysis (PCA) was performed to remove background pixels and to visualize the data. Classes (i.e. Rooibos or Honeybush) were interactively assigned using the score images and plots. Partial Least Squares Discriminant Analysis (PLS-DA) classification models developed were subsequently used to accurately predict the species identity of commercially available herbal tea samples introduced as an external dataset, producing a visually interpretable result. The results showed that the labeling information is correct but that Honeybush tea is in most cases present in very low amounts. It is evident that hyperspectral imaging is an objective and non-destructive quality control method that can be successfully used to determine the botanical species included in herbal teas in addition to giving an indication of the abundance of each species in polyherbal tea mixtures.

Hyperspectral imaging is concerned with the measurement, analysis, and interpretation of spectra acquired from a given scene (or specific object) at a short, medium or long distance by an airbone or satellite sensor. Over the last few years, hyperspectral image data sets have been collected for a great amount of locations over the world, using a variety of instruments for Earth observation. Despite the increasing importance of hyperspectral images in remote sensing applications, there is no common repository of hyperspectral data intended to distribute and share hyperspectral data sets in the community. Quite opposite, the hyperspectral data sets which are available for public use are spread among different storage locations and present significant heterogeneity regarding the storage format, associated meta-data (if any), or ground-truth availability. As a result, the development of a standardized hyperspectral data repository is a highly desired goal in the remote sensing community. In this paper, we take a necessary first step towards the development of a digital repository for remotely sensed hyperspectral data. The proposed system allows uploading new hyperspectral data sets along with meta-data, ground-truth and analysis results, with the ultimate goal of sharing publicly available hyperspectral images within the remote sensing community. The database has been designed in order to allow storing relevant information for the hyperspectral data available through the system, including basic image characteristics (width, height, number of bands, format) and more advanced meta-data (ground-truth information, publications in which the data has been used). The current implementation consists of a front-end to ease the management of images through a web interface, thus containing both synthetic and real hyperspectral images from two highly representative instruments, such as NASAs Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Cuprite Mining District in Nevada. Most importantly, the developed system includes a spectral unmixing-based content based image retrieval (CBIR) functionality which allows searching for images on the spectral unmixing information (spectrally pure components or endmembers and their associated abundances in the scene). This information is stored as meta-data associated to each hyperspectral image instance, and then used to search and retrieve images based on information content. This paper presents the design of the system and a preliminary validation of the unmixing-based retrieval functionality using both synthetic and real hyperspectral images stored in the database.

Abstract. Hyperspectral image enhancement has been a concern for the remote sensing society for detailed end member detection. Hyperspectral remote sensor collects images in hundreds of narrow, continuous spectral channels, whereas multispectral remote sensor collects images in relatively broader wavelength bands. However, the spatial resolution of the hyperspectral sensor image is comparatively lower than that of the multispectral. As a result, spectral signatures from different end members originate within a pixel, known as mixed pixels. This paper presents an approach for obtaining an image which has the spatial resolution of the multispectral image and spectral resolution of the hyperspectral image, by fusion of hyperspectral and multispectral image. The proposed methodology also addresses the band remapping problem, which arises due to different regions of spectral coverage by multispectral and hyperspectral images. Therefore we apply algorithms to restore the spatial information of the hyperspectral image by fusing hyperspectral bands with only those bands which come under each multispectral band range. The proposed methodology is applied over Henry Island, of the Sunderban eco-geographic province. The data is collected by the Hyperion hyperspectral sensor and LISS IV multispectral sensor.

This paper presents a new algorithm for hyperspectral image analysis using spectral-angle based support vector clustering (SVC) and principal component analysis (PCA). In the classical approach to hyper-spectral dimensionality reduction based on principal component analysis (PCA), no meaning or behavior of the spectrum is considered and results are influenced by majority components in the scene. A spectral angle based classification before dimensionality reduction is a possible solution to this problem. Clustering based on support vectors using spectral based kernels is proposed in this work, which is found to generate good results in hyperspectral image classification. The algorithm is tested with two hyperspectral image data sets of 210 bands each, which are taken with hyper-spectral digital imagery collection experiment (HYDICE) air-borne sensors. A comparative study of the proposed algorithm and other two conventional algorithms (PCA alone and PCA with spectral angle mapping (SAM)) is also done

Abstract. Citrus black spot (CBS), one of the most common fungal diseases of citrus, affects lesions on the rind and early fruit drop before its mature stage. This disease can significantly reduce crop yield, making the highly blemished fruit unsuitable for market, and influence the fresh fruit market eventually. Identification of this devastating disease before harvesting or shipment to other district can avoid healthy fruit infected by diseased fruit or spread this disease to new areas. And CBS identification can also assure fruit quality and safety and enhance the competitiveness and profitability of the citrus industry. To this end, the objective of this research was to develop an efficient algorithm using selected wavelengths based on hyperspectral image data, and to detect citrus black spot ahead of harvesting. A hyperspectral imaging system was used to acquire hyperspectral images of citrus samples in the wavelengths range between 396 and 1010 nm. However, hyperspectral image contains hundreds of wavelengths, and many of them would be considered as redundant information, which may even decrease the classification accuracy. Band selection was used to reduce the dimensionality of hyperspectral images and select the useful bands for further application. In this study, a principal components analysis (PCA) and four band ranking methods, T-test, Kullback-Leibler distance, Chernoff bound and Receiver Operating Characteristic (ROC) were applied and 533 nm was selected to classify healthy and CBS infected. Kernel SVM, KNN and C4.5 classification methods were used to evaluate the performance of the selected band, and Kernel SVM achieved the highest accuracy of above 98%. Then Sequential Forward Selection (SFS) and Greedy Stepwise methods were carried out to select bands to distinguishing five CBS symptoms, and they selected 14 and four bands, respectively. After selecting optimal bands using each method, C4.5 was used to test the performance of distinguishing CBS infected and healthy based on selected bands. The overall classification accuracy was about 61%.

The goal of this study was to explore the potential of using near-infrared (NIR) hyperspectral imaging in combination with multivariate analysis for the identification of kiwifruits treated with exogenous plant growth regulator (EPGR). One hundred twenty kiwifruits, variety ‘Xixuan,’ treated with EPGR and another 120 ‘Xixuan’ kiwifruits without EPGR (named as normal ones) were used in the study. Hyperspectral images were acquired using a near-infrared hyperspectral imaging system in the spectral range of 865.11–1,711.71 nm. Based on the Kennard-Stone method, the samples were divided into two sets: 180 samples in calibration set and 60 samples in validation set. Standard normal variate transformation was used to preprocess obtained spectra. Principal component analysis (PCA) and successive projections algorithm (SPA) were used to select principal components and characteristic wavelengths. Partial least square (PLS) regression and support vector machine (SVM) modeling methods were used to establish models for identifying EPGR-treated kiwifruits and normal ones. The results indicated that average correct identification rates of all models were higher than 98.9 and 96.7 % for the calibration set and validation set, respectively. The identification performance of PLS was better than that of SVM, and the best model was PLS-SPA, whose average accuracy rate reached 100 % for the calibration set and 98.4 % for the validation set. The results demonstrated that NIR hyperspectral imaging technique can be used as a noninvasive method for distinguishing kiwifruits treated with EPGR from normal ones.

The use of hyperspectral imaging in the VNIR (400–1000 nm) and SWIR range (1000–2500 nm) for detecting counterfeit drugs with identical API composition

Abstract. Over the past decade hyper spectral (HS) image analysis has turned into one of the most powerful and growing technologies in the field of remote sensing. While HS images cover large area at fine spectral resolution, their spatial resolutions are often too coarse for the use in various applications. Hence improving their resolution has a high payoff. This paper presents a novel approach for super-resolution (SR) of HS images using compressive sensing (CS). Besides ill-posedness of SR problem, the main challenge in HS super-resolution is to preserve spectral contents among all bands while increasing their spatial resolutions. In this work, we first obtain an initial estimate of the super-resolution on a reduced dimension HS data. The HS observations of different wavelengths are represented as linear combination of smaller number of basis image planes (BIPs) using principal component analysis (PCA). The novelty of our approach lies in using CS based approach to super-resolve the most informative PCA transformed image representing highest spectral variance (i.e. the first principal component). Our approach uses low and high spatial resolution dictionaries of patches generated by random sampling of raw patches of PCA transformed images that are generated using the training images having similar statistical properties. Using the sparsity constraint, low resolution test patch is represented as a sparse linear combination of relevant dictionary elements adaptively, that leads to initial estimate of super-resolved PCA image having maximum spectral variability. Since SR is an ill-posed problem, we obtain the final solution using a regularization framework considering the sparse coefficients obtained by the CS approach and the autoregressive (AR) parameters obtained from the initial estimate. The remaining PCA images are up-scaled using regularization, considering the same AR parameters which were obtained from super-resolved PCA image having maximum spectral variability. Experiments are conducted on real HS images collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Visual inspections and quantitative comparison confirm that our method enhances spatial information without introducing significant spectral distortion.