ABSTRACT With respect to satellite and aerial RGB images, ground-based acquisitions can provide a more detailed representation of a natural landscape, especially for steep slopes. Moreover, for some applications the generation of a new... more
ABSTRACT With respect to satellite and aerial RGB images, ground-based acquisitions can provide a more detailed representation of a natural landscape, especially for steep slopes. Moreover, for some applications the generation of a new 'virtual' view-point can provide a valuable visualization tool. Using such a technique, the visual appearance of a landscape (generated from one or several geo-registered images) can be seen from a new, specified vantage point. In this paper, we propose a method of generating a visual appearance model and subsequent virtual views, through a direct re-projection of visual content from source geo-registered images.
ABSTRACT Dealing with a high number of features belonging to different types of data such as Hyperspectral image and Morphological Attribute Profiles (MAPs) might lead to a poor predictive performance of the classifier and hence low final... more
ABSTRACT Dealing with a high number of features belonging to different types of data such as Hyperspectral image and Morphological Attribute Profiles (MAPs) might lead to a poor predictive performance of the classifier and hence low final accuracies of classification. This is due to the Hughes effect that consistently decreases the power of prediction of the classifier, in case of a limited and fixed number of training samples. In order to reduce the number of features and only keeping those which are more informative, a novel supervised feature selection technique based on GAs and the measure of the relevance of the features is presented in this work. Moreover, the effectiveness of the proposed technique was demonstrated by experimenting on an optical remote sensed dataset.
Research Interests:
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... more
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
Research Interests: Earth Sciences, Principal Component Analysis, Discriminant Analysis, Data Fusion, Spatial Information, and 8 moreEfficient Algorithm for ECG Coding, Feature Extraction, THERMAL INFRARED REMOTE SENSING DATA, Hyperspectral Data, Kernel Pca, Hyperspectral Imagery, Large Dataset Analysis, and Classification Accuracy
ABSTRACT Morphological attribute filters have been widely exploited for characterizing the spatial structures in remote sensing images. They have proven their effectiveness especially when computed in multi-scale architectures, such as... more
ABSTRACT Morphological attribute filters have been widely exploited for characterizing the spatial structures in remote sensing images. They have proven their effectiveness especially when computed in multi-scale architectures, such as for Attribute Profiles. However, the question how to choose a proper set of filter thresholds in order to build a representative profile re- mains one of the main issues. In this paper, a novel methodology for the selection of the filters’ parameters is presented. A set of thresholds is selected by analysing granulometric characteristic functions, which provide information on the image decomposition according to a given measure. The method exploits a tree (i.e., min-, max- or inclusion-tree) representation of an image, which allows us to avoid the filtering steps usually required prior the threshold selection, making the process computationally effective. The experimental analysis performed on two real remote sensing images shows the effectiveness of the proposed approach in providing representative and non-redundant multi-level image decompositions.
Research Interests:
Research Interests:
ABSTRACT 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... more
ABSTRACT 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.
Research Interests:
Research Interests:
This paper proposes a novel approach to the retrieval of buildings' height from multi-angular high spatial resolution images. To achieve this task, we combined two main concepts: multilevel morphological attribute... more
This paper proposes a novel approach to the retrieval of buildings' height from multi-angular high spatial resolution images. To achieve this task, we combined two main concepts: multilevel morphological attribute filters, used for the definition of the objects in the image, and geometric invariant moments exploited for the characterization of the spatial properties of the previously detected shapes. The main
Research Interests:
Research Interests: Remote Sensing, Data Mining, Principal Component Analysis, Independent Component Analysis, Support Vector Machines, and 16 moreMathematical Morphology, Image Classification, Geoscience, EAP, Sensor Fusion, Feature Extraction, Geomatic Engineering, Support vector machine, Accuracy, Hyperspectral Imaging, Decision Fusion, Svm, High resolution satelite image, Hyperspectral Image Analysis, Electrical And Electronic Engineering, and Pixel
Research Interests:
ABSTRACT Submitted to ISMM 2015 conference
The automated classification of urban areas in one of the main topic in the Geomatics domain. Several papers dealing with this topic have been already presented in the last decade. Most of these approaches uses multi-spectral or LiDAR... more
The automated classification of urban areas in one of the main topic in the Geomatics domain. Several papers dealing with this topic have been already presented in the last decade. Most of these approaches uses multi-spectral or LiDAR data or both of them as input. In this paper, an algorithm for urban areas classification based only on overlapping RGB images is presented. The integration of radiometric and geometric information derived from aerial images is exploited in order to extract the three main classes of urban areas (i.e. building, vegetation and road) in automated way and without prior information. A photogrammetric Digital Surface Model (DSM) is firstly generated applying dense image matching techniques and this information as well as some spatial features provided by morphological filters are combined to derive a first classification. Subsequently, a thematic classification of the surveyed areas is performed considering the surface's reflectance in the visible spectr...
ABSTRACT Thanks to the recent technological advances, a large variety of image data is at our disposal with variable geometric, radiometric and temporal resolution. In many applications the processing of such images needs high performance... more
ABSTRACT Thanks to the recent technological advances, a large variety of image data is at our disposal with variable geometric, radiometric and temporal resolution. In many applications the processing of such images needs high performance computing techniques in order to deliver timely responses e.g. for rapid decisions or real-time actions. Thus, parallel or distributed computing methods, Digital Signal Processor (DSP) architectures, Graphical Processing Unit (GPU) programming and Field-Programmable Gate Array (FPGA) devices have become essential tools for the challenging issue of processing large amount of geo-data. The article focuses on the processing and registration of large datasets of terrestrial and aerial images for 3D reconstruction, diagnostic purposes and monitoring of the environment. For the image alignment procedure, sets of corresponding feature points need to be automatically extracted in order to successively compute the geometric transformation that aligns the data. The feature extraction and matching are ones of the most computationally demanding operations in the processing chain thus, a great degree of automation and speed is mandatory. The details of the implemented operations (named LARES) exploiting parallel architectures and GPU are thus presented. The innovative aspects of the implementation are (i) the effectiveness on a large variety of unorganized and complex datasets, (ii) capability to work with high-resolution images and (iii) the speed of the computations. Examples and comparisons with standard CPU processing are also reported and commented.
ABSTRACT This paper presents a classification method for hyperspectral images utilizing Differential Morphological Profiles (DMPs) which permit to include in the analysis spatial information since they can provide an estimate of the size... more
ABSTRACT This paper presents a classification method for hyperspectral images utilizing Differential Morphological Profiles (DMPs) which permit to include in the analysis spatial information since they can provide an estimate of the size and contrast characteristics of the structures in an image. Due to the wide variety of objects present in a scene, the pixels belonging to the same semantic structure may not have homogeneous spatial and spectral features. In addition, instead of a single peak (which can be related to a measure of the scale), multiple local maxima and multiple responses are usually observed in the DMP. In order to handle such intra-class variability, class-specific weighting functions are employed in order to differently modulate the DMP values according to the different characteristics of the land cover types. In such way, it is possible to differentiate the behaviors of the DMP for each pixel in the image according to its semantic, providing an increase of the separability of the classes. At first, a DMP computed with opening by reconstruction (DMPO) and one with closing by reconstruction (DMPC) are derived on each of the first principle components extracted from the hyperspectral image. Then, both profiles are weighted by each class-specific weighting function and concatenated in a single data structure. The constructed feature vectors are considered by a random forest classifier.
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... more
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
Research Interests:
ABSTRACT Digital Elevation Models (DEMs) are a valuable resource in geoscience and remote sensing. However, not all DEMs demonstrate the same level of accuracy, resolution and precision. Moreover, in some regions of the globe coverage is... more
ABSTRACT Digital Elevation Models (DEMs) are a valuable resource in geoscience and remote sensing. However, not all DEMs demonstrate the same level of accuracy, resolution and precision. Moreover, in some regions of the globe coverage is either sparse or non-existent, for example at the poles where ice also hampers accurate readings. In this paper, we propose to use collective archives of geo-located landscape photos acquired at ground-level to correct, validate and possibly reconstruct DEMs.
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... more
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
Research Interests:
Research Interests: Remote Sensing, Data Mining, Principal Component Analysis, Independent Component Analysis, Support Vector Machines, and 16 moreMathematical Morphology, Image Classification, Geoscience, EAP, Sensor Fusion, Feature Extraction, Geomatic Engineering, Support vector machine, Accuracy, Hyperspectral Imaging, Decision Fusion, Svm, High resolution satelite image, Hyperspectral Image Analysis, Electrical And Electronic Engineering, and Pixel
The analysis of changes occurred in multi-temporal images acquired by the same sensor on the same geographical area at different dates is usually done by performing a comparison of the two images after co-registration. When one considers... more
The analysis of changes occurred in multi-temporal images acquired by the same sensor on the same geographical area at different dates is usually done by performing a comparison of the two images after co-registration. When one considers very high resolution (VHR) remote sensing images, the spatial information of the pixels becomes very important and should be included in the analysis.
Research Interests:
In this paper we propose Alternating Sequential Attribute Filters, which are Alternating Sequential Filters (ASFs) computed with Attribute Filters. ASFs are obtained by the iterative subsequent application of morphological opening and... more
In this paper we propose Alternating Sequential Attribute Filters, which are Alternating Sequential Filters (ASFs) computed with Attribute Filters. ASFs are obtained by the iterative subsequent application of morphological opening and closing transformations and process an image by filtering both bright and dark structures. ASFs are widely used for achieving a simplification of a scene and for the removal of
Research Interests:
ABSTRACT In this paper we investigate the application of Morphological Attribute Profiles to both hyperspectral and LiDAR data to fuse spectral, spatial and elevation data for classification purposes. While hyperspectral data provides a... more
ABSTRACT In this paper we investigate the application of Morphological Attribute Profiles to both hyperspectral and LiDAR data to fuse spectral, spatial and elevation data for classification purposes. While hyperspectral data provides a wealth of spectral information, multi-return LiDAR data provides geometrical information on the elevation and the structure of the objects on the ground as well as a measure of their laser cross section. Therefore, hyperspectral and LiDAR data are complementary information sources and potentially their joint analysis can improve classification accuracies. Morphological Profiles (MPs) and Morphological Attribute Profiles (MAPs) have been successfully used as tools to combine spectral and spatial information for classification of remote sensing data. MPs and MAPs can also be used with the LiDAR data to reduce the irregularities in the LiDAR measurements which are inherent with the sampling strategy used in the acquisition process. Experiments carried out on hyperspectral and LiDAR data acquired on a urban area of the city of Trento (Italy) point out the effectiveness of MAPs for the classification process.
Research Interests: Remote Sensing and LiDAR
In this paper a general approach based on morphological connected filters for the spatial simplification of very high resolution remote sensing images is introduced. In greater detail, the proposed approach is made up of two steps: i) the... more
In this paper a general approach based on morphological connected filters for the spatial simplification of very high resolution remote sensing images is introduced. In greater detail, the proposed approach is made up of two steps: i) the selection of the parameters defining the connected filters driven by the information available on the scene and on the specific application; and
Research Interests:
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... more
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
Research Interests:
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... more
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
Research Interests:
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).... more
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
Research Interests:
ABSTRACT Morphological and attribute profiles have been proven to be effective tools to fuse spectral and spatial information for classification of remote sensing data. A wide range of filters (i.e., number of levels in the profiles) is... more
ABSTRACT Morphological and attribute profiles have been proven to be effective tools to fuse spectral and spatial information for classification of remote sensing data. A wide range of filters (i.e., number of levels in the profiles) is usually necessary in order to properly model the spatial information in a remote sensing scene. A dense sampling of the values of the parameters of the filters generates profiles that have both a very large dimensionality (leading to the Hughes phenomenon in classification) and a high redundancy. In this paper, a novel iterative technique based on genetic algorithms (GAs) is proposed to automatically optimize the selection of the optimal features from the profiles. The selection of the filtered images that compose the profile is performed by dividing them into three classes corresponding to high, medium, and low importance. We propose to measure the importance (modeled in terms of discriminative power in the classification task) using a random forest classifier, which provides a rank for each feature with its model. Only the set of images associated with the highest importance is selected, i.e., preserved for classification. The proposed technique is applied to the features labeled with medium importance, whereas the images with the lowest importance are removed from the profile. This method is employed to classify three hyperspectral data sets achieving significantly high classification accuracy values. A parallel computing implementation has been developed in order to significantly reduce the time required for the run of the GAs.
Research Interests:
Research Interests:
Morphological attribute profiles (APs) are defined as a generalization of the recently proposed morphological profiles (MPs). APs provide a multilevel characterization of an image created by the sequential application of morphological... more
Morphological attribute profiles (APs) are defined as a generalization of the recently proposed morphological profiles (MPs). APs provide a multilevel characterization of an image created by the sequential application of morphological attribute filters that can be used to model different kinds of the structural information. According to the type of the attributes considered in the morphological attribute transformation, different parametric