Mauro Dalla Mura

Classification of Hyperspectral Images by Using Extended Morphological Attribute Profiles and Independent Component Analysis

In this letter, a technique based on independent component analysis (ICA) and extended morphological attribute profiles (EAPs) is presented for the classification of hyperspectral images. The ICA maps the data into a subspace in which the components are as independent as possible. APs, which are extracted by using several attributes, are applied to each image associated with an extracted independent component, leading to a set of extended EAPs. Two approaches are presented for including the computed profiles in the analysis. The features extracted by the morphological processing are then classified with an SVM. The experiments carried out on two hyperspectral images proved the effectiveness of the proposed technique.

Extended profiles with morphological attribute filters for the analysis of hyperspectral data

Extended attribute profiles and extended multi-attribute profiles are presented for the analysis of hyperspectral high-resolution images. These extended profiles are based on morphological attribute filters and, through a multi-level analysis, are capable of extracting spatial features that can better model the spatial information, with respect to conventional extended morphological profiles. The features extracted by the proposed extended profiles were considered for a classification task. Two hyperspectral high-resolution datasets acquired for the city of Pavia, Italy, were considered in the analysis. The effectiveness of the introduced operators in modelling the spatial information was proved by the higher classification accuracies obtained with respect to those achieved by a conventional extended morphological profile.

Automatic Generation of Standard Deviation Attribute Profiles for Spectral–Spatial Classification of Remote Sensing Data

Extended attribute profiles, which are based on attribute filters, have recently been presented as efficient tools for spectral-spatial classification of remote sensing images. However, construction of these profiles usually requires manual selection of parameters for the corresponding attribute filters. In this letter, we present a technique to automatically build the extended attribute profiles with the standard deviation attribute based on the statistics of the samples belonging to the classes of interest. The methodology is tested on two widely used hyperspectral images and the results are found to be highly accurate.

Classification of hyperspectral data using extended attribute profiles based on supervised and unsupervised feature extraction techniques

The classification of remote sensing data based on the exploitation of spatial features extracted with morphological and attribute profiles has been recently gaining importance. With the development of efficient algorithms to construct the profiles for large datasets, such methods are becoming even more relevant. When dealing with hyperspectral imagery, the profiles are traditionally built on the first few principal components computed from the data. However, it needs to be determined if other feature reduction approaches are better suited to create base images for the profiles. In this article, we explore the use of profiles based on features derived from three supervised feature extraction techniques (i.e. Discriminant Analysis Feature Extraction, Decision Boundary Feature Extraction and Non-parametric Weighted Feature Extraction) and two unsupervised feature-extraction techniques (i.e. Principal Component Analysis (PCA) and Kernel PCA) in classification and compare the classification accuracies obtained by using different techniques for two different classification methods. The obtained results indicate significant improvements in the accuracies using the supervised feature extraction methods. However, the choice of the method affects the quality of the results for different datasets depending on the availability of the training samples.

Self-dual attribute profiles for the analysis of remote sensing images

The spatial relations are essential information that should be considered when analyzing remote sensing images. Attribute profiles (combinations of an anti-granulometry and a granulometry computed with connected operators based on attributes) can be employed for the modeling of the spatial information of the surveyed scene. In this paper we propose self-dual attribute profiles which are attribute profiles computed on an inclusion tree with self-dual operators. The proposed variant of the attribute profile was effectively considered for the classification of a very high geometrical resolution remote sensing image.

Hierarchical analysis of remote sensing data: morphological attribute profiles and binary partition trees

The new generation of very high resolution sensors in airborne or satellite remote sensing open the door to countless new applications with a high societal impact. In order to bridge the gap between the potential offered by these new sensors and the needs of the end-users to actually face tomorrows challenges, advanced image processing methods need to be designed. In this paper we discuss two of the most promising strategies aiming at a hierarchical description and analysis of remote sensing data, namely the Extended Attribute Profiles (EAP) and the Binary Partition Trees (BPT). The EAP computes for each pixel a vector of attributes providing a local multiscale representation of the information and hence leading to a fine description of the local structures of the image. Using different attributes allows to address different contexts or applications. The BPTs provide a complete hierarchical description of the image, from the pixels (the leaves) to larger regions as the merging process goes on. The pruning of the tree provides a partition of the image and can address various goals (segmentation, object extraction, classification). The EAP and BPT approaches are used in experiments and the obtained results demonstrate their importance.

Classification of hyperspectral images with Extended Attribute Profiles and feature extraction techniques

In this paper we investigate the combined use of morphological attribute filters and feature extraction techniques for the classification of a high resolution hyperspectral image. In greater detail, we propose to model the spatial information with Extended Attribute Profiles computed on the hyperspectral data and to reduce the high dimensionality of the morphological features computed (which show a high degree of redundancy) with feature extraction techniques. The features extracted are analyzed by two classifiers. The experimental analysis was carried out on a high resolution hyperspectral image acquired by the airborne sensor ROSIS-03 on the University of Pavia, Italy. The obtained results compared to those obtained without feature reduction proved the importance of the application of a stage of feature extraction in the process.

The Evolution of the Morphological Profile: from Panchromatic to Hyperspectral Images

Almost a decade has passed since the concept of morphological profile (MP) was defined for the analysis of panchromatic remote sensing images. From that time, the MP has largely proved to be a powerful tool able to model the spatial information (e.g., contextual relations) of the image by extracting structural features (e.g., size, geometry, etc.) from the objects present in the scene. The MP processes an input image with a sequence of progressively coarser filters. This leads to a stack of filtered images showing an increasing simplification of the scene. The evaluation of how the objects in the image interact with the filters gives information on the objects structural features. The great amount of contributions present in the literature that address the application of MP to many tasks (e.g., classification, object detection, segmentation, change detection, etc.) and to different types of images (e.g., panchromatic, multispectral, hyperspectral) proves how MP is still an effective and modern tool. Moreover, many variants, extensions and refinements of its definition have also appeared stating that the MP is still under continuous development. This chapter presents the MP from its early definition to the recent advances based on morphological attribute filters. The overview of many significant contributions that have appeared in this decade allows the reader to track the evolution of the MP from the analysis of panchromatic to hyperspectral images.

Classification using Extended Morphological Attribute Profiles based on different feature extraction techniques

Extended Morphological Attribute Profiles (EAPs) are extension of Extended Morphological Profiles (EMPs). They are based on the more general Morphological Attribute Profiles (APs) rather than the conventional Morphological Profiles (MPs). EAPs are computed on few of the first principle components (PCs) extracted from the multi-/hyper-spectral data. In this paper, we propose to compute EAPs on features derived from supervised feature extraction techniques such as discriminant analysis feature extraction (DAFE), decision boundary feature extraction (DBFE) and non-parametric weighted feature extraction (NWFE)) instead of using unsupervised principal component analysis (PCA).

Morphological attribute filters for the analysis of very high resolution remote sensing images

This paper proposes the use of morphological attribute profiles as an effective alternative to the conventional morphological operators based on the geodesic reconstruction for modeling the spatial information in very high resolution images. Attribute profiles, used in multilevel approaches, result particularly effective in terms of computational complexity and capabilities in characterizing the objects in the image. In addition they are more flexible than operators by reconstruction, thanks to the definition of possible different attributes. Experimental results obtained on a Quickbird panchromatic very high resolution image proved the effectiveness of the presented attribute filters and pointed out their main properties.