1,721,024 research outputs found
A Novel Change Detection Method for Multitemporal Hyperspectral Images Based on Binary Hyperspectral Change Vectors
No full textHyperspectral (HS) images provide a dense sampling of target spectral signatures. Thus, they can be used in a multitemporal framework to detect and discriminate between different kinds of fine spectral change effectively. However, due to the complexity of the problem and the limited amount of multitemporal images and reference data, only a few works in the literature addressed change detection (CD) in HS images. In this paper, we present a novel method for unsupervised multiple CD in multitemporal HS images based on a discrete representation of the change information. Differently from the state-of-the-art methods, which address the high dimensionality of the data using band reduction or selection techniques, in this paper, we focus our attention on the representation and exploitation of the change information present in each band. After a band-by-band pixel-based subtraction of the multitemporal images, we define the hyperspectral change vectors (HCVs). The change information in the HCVs is then simplified. To this end, the radiometric information of each band is separately analyzed to generate a quantized discrete representation of the HCVs. This discrete representation is explored by considering the hierarchical nature of the changes in HS images. A tree representation is defined and used to discriminate between different kinds of change. The proposed method has been tested on a simulated data set and two real multitemporal data sets acquired by the Hyperion sensor over agricultural areas. Experimental results confirm that the discrete representation of the change information is effective when used for unsupervised CD in multitemporal HS data
A Novel Technique to Reconstruct Tricuspid Valve Anterior Leaflet in Massive Endocarditis
No full textIntraoperative graft verification in coronary surgery
No full textTransit-time flow measurement (TTFM) is a reliable method to check the graft function intraoperatively in coronary surgery. The given parameters are: Mean Graft Flow (MGF); Pulsatility Index (PI) and Insufficiency Ratio (%BF). Some cutoffs of these parameters have been identified as predictors for unfair 1-y clinical outcome: mean graft flow (MGF) less than 20 ml/min and high pulsatility index greater than 5. Other cutoffs have been found as related to postoperative angiography: MGF 15 ml/min or less and pulsatility index at least 3 (sensitivity 94%; specificity 61%); MGF less than 15 ml/min and pulsatility index greater than 3 for left coronary artery or pulsatility index greater than 5 for right coronary artery (sensitivity 96%; specificity 77%); MGF 15 ml/min or less and pulsatility index at least 5.1 left coronary artery (sensitivity 98%; specificity 26%). Hence, with the need to improve the diagnostic accuracy of TTFM, a high-resolution epicardic coronary ultrasound module has been added to graft flow evaluation providing 2D ultrasound imaging (either in short-axis or long-axis) and color-flow mapping, allowing an accurate morphological evaluation of body graft and anastomosis. An intraoperative method aimed to verify coronary grafts should be easy to handle, not time consuming, minimally invasive, easily meaningful and relatively cheap; in addition, it should offer objective parameters more than qualitative criteria. We herein report the results of our experience with intraoperative graft verification with TTFM and high-resolution imaging along with a systematic review of the literature in this field with the aim to provide a road map to be followed
A Review of Change Detection in Multitemporal Hyperspectral Images: Current Techniques, Applications, and Challenges
No full textWe review both widely used methods and new techniques proposed in the recent literature. The basic concepts, categories, open issues, and challenges related to CD in HS images are discussed and analyzed in detail. Experimental results obtained using state-of-the-art approaches are shown, to illustrate relevant concepts and problems
A novel change detection method for multitemporal hyperspectral images based on a discrete representation of the change information
No full textMultitemporal Hyperspectral (HS) images can be used in Change Detection (CD) to identify and discriminate among different kinds of change due to the fine sampling of the spectrum by HS sensors. In this work we propose a novel method for unsupervised multiple CD in multitemporal HS data based on binary Spectral Change Vectors (SCVs) and an agglomerative hierarchical clustering. First, we perform binary CD to separate changed from unchanged pixels. Second, we convert the real valued SCVs into binary ones. Thus we move from a real valued high dimensional space to a discrete one. The binary signatures are used to construct a dendrogram following an hierarchical agglomerative clustering approach. Finally, we exploit the hierarchical structure to discriminate among the kinds of change in a fully unsupervised manner. The experimental results obtained on the real dataset confirmed the effectiveness of the proposed method
A novel method for unsupervised multiple Change Detection in hyperspectral images based on binary Spectral Change Vectors
No full textIn the next years, the launch of new satellites with Hyperspectral (HS) sensors will guarantee the availability of regular multitemporal HS datasets. In order to exploit the dense sampling of the spectrum of HS sensors to discriminate multiple land-cover changes ad-hoc techniques are required. In this paper we propose a novel method for unsupervised multiple Change Detection (CD) in HS multitemporal images based on binary Spectral Change Vectors (SCVs). In greater detail, the method discriminates between unchanged and changed areas in order to focus only on the latter ones. Then, it converts the real valued SCVs in a binary form to work in a discrete high dimensional space. The binary SCVs are clustered following an hierarchical tree structure where each leaf represent a kind of change. The tree also highlights how the different changes are related among each other. The proposed approach has been tested on a multitemporal dataset acquired over an agricultural area. Experimental results confirmed that the binary SCVs allows us to detect and discriminate multiple changes by working in a simpler discrete space
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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