Detection of leaf structures in close-range hyperspectral images using morphological fusion

Close-range hyperspectral images are a promising source of information in plant biology, in particular, for in vivo study of physiological changes. In this study, we investigate how data fusion can improve the detection of leaf elements by combining pixel reflectance and morphological information. The detection of image regions associated to the leaf structures is the first step toward quantitative analysis on the physical effects that genetic manipulation, disease infections, and environmental conditions have in plants. We tested our fusion approach on Musa acuminata (banana) leaf images and compared its discriminant capability to similar techniques used in remote sensing. Experimental results demonstrate the efficiency of our fusion approach, with significant improvements over some conventional methods

Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral image

Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with their corresponding proportions (or abundances). Endmember extraction algorithms can be employed for recovering the spectral signatures while abundances are estimated using an inversion step. Recent works have shown that exploiting spatial dependencies between image pixels can improve spectral unmixing. Markov random fields (MRF) are classically used to model these spatial correlations and partition the image into multiple classes with homogeneous abundances. This paper proposes to define the MRF sites using similarity regions. These regions are built using a self-complementary area filter that stems from the morphological theory. This kind of filter divides the original image into flat zones where the underlying pixels have the same spectral values. Once the MRF has been clearly established, a hierarchical Bayesian algorithm is proposed to estimate the abundances, the class labels, the noise variance, and the corresponding hyperparameters. A hybrid Gibbs sampler is constructed to generate samples according to the corresponding posterior distribution of the unknown parameters and hyperparameters. Simulations conducted on synthetic and real AVIRIS data demonstrate the good performance of the algorithm

Mapping Chestnut Stands Using Bi-Temporal VHR Data

This study analyzes the potential of very high resolution (VHR) remote sensing images and extended morphological profiles for mapping Chestnut stands on Tenerife Island (Canary Islands, Spain). Regarding their relevance for ecosystem services in the region (cultural and provisioning services) the public sector demand up-to-date information on chestnut and a simple straight-forward approach is presented in this study. We used two VHR WorldView images (March and May 2015) to cover different phenological phases. Moreover, we included spatial information in the classification process by extended morphological profiles (EMPs). Random forest is used for the classification process and we analyzed the impact of the bi-temporal information as well as of the spatial information on the classification accuracies. The detailed accuracy assessment clearly reveals the benefit of bi-temporal VHR WorldView images and spatial information, derived by EMPs, in terms of the mapping accuracy. The bi-temporal classification outperforms or at least performs equally well when compared to the classification accuracies achieved by the mono-temporal data. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements on the final map. Overall the new proposed classification strategy proves useful for mapping chestnut stands in a heterogeneous and complex landscape, such as the municipality of La Orotava, Tenerife

A new kernel method for hyperspectral image feature extraction

Hyperspectral image provides abundant spectral information for remote discrimination of subtle differences in ground covers. However, the increasing spectral dimensions, as well as the information redundancy, make the analysis and interpretation of hyperspectral images a challenge. Feature extraction is a very important step for hyperspectral image processing. Feature extraction methods aim at reducing the dimension of data, while preserving as much information as possible. Particularly, nonlinear feature extraction methods (e.g. kernel minimum noise fraction (KMNF) transformation) have been reported to benefit many applications of hyperspectral remote sensing, due to their good preservation of high-order structures of the original data. However, conventional KMNF or its extensions have some limitations on noise fraction estimation during the feature extraction, and this leads to poor performances for post-applications. This paper proposes a novel nonlinear feature extraction method for hyperspectral images. Instead of estimating noise fraction by the nearest neighborhood information (within a sliding window), the proposed method explores the use of image segmentation. The approach benefits both noise fraction estimation and information preservation, and enables a significant improvement for classification. Experimental results on two real hyperspectral images demonstrate the efficiency of the proposed method. Compared to conventional KMNF, the improvements of the method on two hyperspectral image classification are 8 and 11%. This nonlinear feature extraction method can be also applied to other disciplines where high-dimensional data analysis is required

Classification of hyperspectral images by tensor modeling and additive morphological decomposition

International audiencePixel-wise classification in high-dimensional multivariate images is investigated. The proposed method deals with the joint use of spectral and spatial information provided in hyperspectral images. Additive morphological decomposition (AMD) based on morphological operators is proposed. AMD defines a scale-space decomposition for multivariate images without any loss of information. AMD is modeled as a tensor structure and tensor principal components analysis is compared as dimensional reduction algorithm versus classic approach. Experimental comparison shows that the proposed algorithm can provide better performance for the pixel classification of hyperspectral image than many other well-known techniques

Cellular neural networks, Navier-Stokes equation and microarray image reconstruction

Copyright @ 2011 IEEE.Although the last decade has witnessed a great deal of improvements achieved for the microarray technology, many major developments in all the main stages of this technology, including image processing, are still needed. Some hardware implementations of microarray image processing have been proposed in the literature and proved to be promising alternatives to the currently available software systems. However, the main drawback of those proposed approaches is the unsuitable addressing of the quantification of the gene spot in a realistic way without any assumption about the image surface. Our aim in this paper is to present a new image-reconstruction algorithm using the cellular neural network that solves the Navier–Stokes equation. This algorithm offers a robust method for estimating the background signal within the gene-spot region. The MATCNN toolbox for Matlab is used to test the proposed method. Quantitative comparisons are carried out, i.e., in terms of objective criteria, between our approach and some other available methods. It is shown that the proposed algorithm gives highly accurate and realistic measurements in a fully automated manner within a remarkably efficient time

Multiscale Astronomical Image Processing Based on Nonlinear Partial Differential Equations

Astronomical applications of recent advances in the field of nonastronomical image processing are presented. These innovative methods, applied to multiscale astronomical images, increase signal-to-noise ratio, do not smear point sources or extended diffuse structures, and are thus a highly useful preliminary step for detection of different features including point sources, smoothing of clumpy data, and removal of contaminants from background maps. We show how the new methods, combined with other algorithms of image processing, unveil fine diffuse structures while at the same time enhance detection of localized objects, thus facilitating interactive morphology studies and paving the way for the automated recognition and classification of different features. We have also developed a new application framework for astronomical image processing that implements some recent advances made in computer vision and modern image processing, along with original algorithms based on nonlinear partial differential equations. The framework enables the user to easily set up and customize an image-processing pipeline interactively; it has various common and new visualization features and provides access to many astronomy data archives. Altogether, the results presented here demonstrate the first implementation of a novel synergistic approach based on integration of image processing, image visualization, and image quality assessment