Nicola Acito

Signal-Dependent Noise Modeling and Model Parameter Estimation in Hyperspectral Images

In this paper, a novel method to characterize random noise sources in hyperspectral (HS) images is proposed. Noise is described using a parametric model that accounts for the dependence of noise variance on the useful signal. Such model takes into account the photon noise contribution and is therefore suitable for noise characterization in the data acquired by new-generation HS sensors where electronic noise is not dominant. A new algorithm is developed for the estimation of noise parameters which consists of two steps. First, the noise and signal realizations are extracted from the original image by resorting to the multiple-linear-regression-based approach. Then, the model parameters are estimated by using a maximum likelihood approach. The new method does not require the intervention of a human operator and the selection of homogeneous regions in the scene. The performance of the new technique is analyzed on simulated HS data. Results on real data are also presented and discussed. Images acquired with a new-generation HS camera are analyzed to give an experimental evidence of the dependence of random noise on the signal level and to show the results of the estimation algorithm. The algorithm is also applied to a well-known Airborne Visible/Infrared Imaging Spectrometer data set in order to show its effectiveness when noise is dominated by the signal-independent term.

Statistical CLEAN Technique for ISAR Imaging

Inverse synthetic aperture radar (ISAR) images are frequently used in target classification and recognition applications. Some classifiers often require features that can be more easily obtained by extracting scattering centers from ISAR data rather than by reconstructing ISAR images. An available method for scattering center extraction, namely, the CLEAN technique, was proposed in a recent paper by Yang et al. In this paper, an improvement of this CLEAN technique is proposed that introduces a new method for detecting scattering centers. The proposed technique is based on a Gaussianity test, and its effectiveness is first theoretically proven and then tested on real data. Moreover, a comparison with the technique proposed by Yang et al. is shown.

Subspace-Based Striping Noise Reduction in Hyperspectral Images

In this paper, a new algorithm for striping noise reduction in hyperspectral images is proposed. The new algorithm exploits the orthogonal subspace approach to estimate the striping component and to remove it from the image, preserving the useful signal. The algorithm does not introduce artifacts in the data and also takes into account the dependence on the signal intensity of the striping component. The effectiveness of the algorithm in reducing striping noise is experimentally demonstrated on real data acquired both by airborne and satellite hyperspectral sensors.

Hyperspectral Signal Subspace Identification in the Presence of Rare Signal Components

In this paper, we investigate the problem of signal subspace identification (SSI) and dimensionality reduction in hyperspectral images. We consider two recently proposed SSI algorithms: the Maximum Orthogonal Complement Analysis (MOCA) algorithm and the Robust Signal Subspace Estimator (RSSE) algorithm. Such algorithms are robust to the presence of rare signal components and are particularly effective in reducing the number of features in the preprocessing step for small target detection applications. In this paper, MOCA and RSSE are briefly revisited and integrated in a common theoretical framework in order to better highlight and understand their peculiarities. Furthermore, their performances are compared in terms of computational complexity and of their ability to address both the abundant and the rare signal components. A modified version of the MOCA is also introduced, which is computationally more efficient than the original algorithm. Results on simulated data are discussed, and a case study is presented concerning real Airborne Visible/Infrared Imaging Spectrometer data.

A New Algorithm for Robust Estimation of the Signal Subspace in Hyperspectral Images in the Presence of Rare Signal Components

This paper deals with the problem of signal subspace estimation for dimensionality reduction (DR) in hyperspectral images in the presence of rare pixels, i.e., pixels that are scarcely represented in the image and containing spectral components that are linearly independent of the background. Most of the classical methods proposed in the literature are based on the analysis of second-order statistics (SOS), which are weakly influenced by the rare signals. Therefore, such techniques estimate the signal subspace addressing mostly the background and ignoring the presence of rare pixels. This may reduce the target/background spectral contrast, thus decreasing the detection performance when DR is adopted as preprocessing task in small-target detection applications. In this paper, a new robust algorithm, namely, robust signal subspace estimation (RSSE), is developed, which preserves both abundant and rare signal components. It combines the analysis of SOS and a recent approach based on the analysis of the l 2 infin norm. The novel contribution of this paper is twofold. First, the RSSE algorithm is presented, which includes a new iterative procedure to derive the signal subspace and an original statistical method to estimate the data dimensionality. Second, an ad hoc simulation strategy is proposed to assess the performance of signal subspace estimation methods in the presence of rare signal components. The procedure is adopted to compare the RSSE algorithm with a classical technique based on the analysis of SOS. The results obtained by applying the two methods on a real Airborne Visible Infrared Imaging Spectrometer hyperspectral image are also presented and discussed.

An Automatic Approach to Adaptive Local Background Estimation and Suppression in Hyperspectral Target Detection

This paper deals with subspace-based target detection in hyperspectral images. Specifically, it focuses on a general detection scheme where, first, background is suppressed through orthogonal-subspace projection and then target detection is accomplished. An adequate estimation of the background subspace is essential to a successful outcome. The background subspace has been typically estimated globally. However, global approaches may be ineffective for small-target-detection applications since they tend to overestimate the background interference affecting a given target. This may result in a low target residual energy after background suppression that is detrimental to detection performance. In this paper, we propose a novel and fully automatic algorithm for local background-subspace estimation (LBSE). Local background has typically a lower inherent complexity than that of global background. By estimating the background subspace over a local neighborhood of the test pixel, the resulting background-subspace dimension is expected to be low, thus resulting in a higher target residual energy after suppression which benefits the detection performance. Specifically, the proposed LBSE acts on a per-pixel basis, thus adaptively tailoring the estimated basis to the local complexity of background. Both simulated and real hyperspectral data are employed to investigate the detection-performance improvements offered by LBSE with respect to both global and local methodologies previously presented.

An unsupervised algorithm for hyperspectral image segmentation based on the Gaussian mixture model

A new algorithm for hyperspectral image segmentation based on the statistical approach is presented. The algorithm is completely unsupervised and relies only on the spectral information. The hyperspectral image is statistically characterized by means of the Gaussian Mixture Model (GMM). Preliminary results obtained on experimental data are presented and discussed.

A Novel Method Based on Voltammetry for the Qualitative Analysis of Water

This paper deals with the use of voltammetric techniques for the qualitative analysis of water, where we focus on the signal analysis approach and its evaluation in terms of discrimination capability. The prototype described in this paper and the signal analysis chain has been designed in the framework of the development of a field instrument for classification and change detection purposes. The common concept in the various approaches presented in the literature lies in the combination of poorly selective sensors for the characterization of liquids. The fundamental idea of this paper is to investigate how an adequate signal processing approach applied to a mature and affordable sensor technique (voltammetry) can address the issue of extracting aggregate chemical information, which is useful to characterize the liquid under measurement. In the proposed approach, dimensionality reduction is performed in a transformed domain via discrete cosine transform with an appropriate selection of a low-dimensionality subset of the transformed coefficients. The novel methodological approach to the signal processing, two application experiments, and the test set that has been built for the experiments are described here. Finally, the capability of discriminating between different kinds of water is discussed.

The PRISMA hyperspectral mission: Science activities and opportunities for agriculture and land monitoring

The main objectives of the PRISMA (Hyperspectral Precursor of the Application Mission) mission are: the implementation of an Earth Observation pre-operative payload, the in-orbit demonstration and qualification of an Italian state-of-the-art hyperspectral/panchromatic technology and the validation of end-to-end data processing system able to support the development of new applications based on high spectral resolution images. The aim of the paper is to provide an overview of the PRISMA mission by describing the current status of the program and giving a brief outline of the work done till now in the framework of the SAP4PRISMA project scientific studies in supporting the exploitation of the future PRISMA hyperspectral images for environmental applications.

Comparative analysis of clutter removal techniques over experimental IR images

Infrared surveillance systems have the task of detecting small moving targets having low signal-to-clutter ratio. Detection is usually accomplished by (1) removing the background structures from each frame and (2) integrating the target signal over consecutive frames of the residual sequence. We focus on the analysis of background removal techniques based on linear and nonlinear two-dimensional filters such as the window average, median, max-median, and max-mean. We introduce two modified versions of the window average and max-mean filters, where an appropriate guard window is used to reduce the bias due to the target. We define an ad hoc methodology to compare the different background estimation techniques on the basis of their ability to suppress background structures and to preserve the target of interest. Finally, we present and discuss the results obtained over two experimental IR sequences containing a highly structured background.