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In this paper, we focus the attention on the superresolution of multispectral (MS) multiresolution images (e.g., Sentinel 2, Aster, MODIS). By taking advantage of the high spatial resolution bands, we minimize an objective function containing a quadratic data fitting term, an edge preserving regularizer, and a patch-based plug-and play prior promoting self-similar images. To cope with the ill-posedness...
This paper presents an approach to the update of land-cover maps by classifying Remote Sensing (RS) images in an unsupervised way. The proposed method assumes that: i) an old thematic map is available; ii) no ground truth data are available; iii) the source used to generate the available thematic map is unknown. To classify the most recent RS image available on the considered area, the method automatically...
This paper focuses on the scientific preliminary results of the project “S2-4Sci Land and Water - Multitemporal Analysis” funded by the European Space Agency (ESA) in the framework of the Scientific Exploitation of Operational Missions (SEOM). The aim of the project is the development of advanced multitemporal methods tailored on the specific properties of S2 images. The Sentinel-2 (S2) constellation...
Light Detection And Ranging (LiDAR) data have proven to be very effective in the estimation of parameters for forestry applications. However, little research has been done regarding the multitemporal analysis of these data. In this paper we propose a novel hierarchical change detection approach that first performs the detection of major changes (e.g., harvested trees) and then focuses on the detection...
In this paper we present a growth-model based approach to the accurate estimation of stem diameter at single tree level by using high-density LiDAR data. First, we detect classes of trees characterized by different growth conditions by means of a data-driven inference process. To this end, all the environmental factors that can affect the growth of the tree (i.e., forest density and topography) are...
In this paper we present a hierarchical approach to the segmentation of high-density LiDAR data which aims to automatically detect and delineate the single tree crowns of both the dominant and the dominated layers of the forest. First, we detect the dominant tree crowns by using both the image derived from the LiDAR data and the LiDAR point cloud. Hence, the detected crowns are delineated directly...
Modern forest inventory is based on the accurate and precise characterization of the 3D structure of the forest. Although LiDAR (Light Detection and Ranging) is an effective tool to estimate forest parameters, when acquired from single view point it is not able to represent accurately the entire scene. To solve this problem, in this paper we present a method that integrates the terrestrial and airborne...
In this paper, a sensor-driven domain adaptation method is proposed for the classification of remote sensing images. The method aims at classifying an image where ground truth is not available exploiting the reference data acquired on a different but related image. This is done by taking advantage from a sensor-driven strategy that exploits the invariance of the measurements of some sensors on some...
Light detection and ranging (LiDAR) is one of the most efficient remote sensing technologies for the estimation of forest parameters. However, when acquired with a low laser sampling density, LiDAR data fail in providing accurate tree height measures. In order to address this issue, in this paper we propose a novel technique for the reconstruction of tree-top height based on the joint use of low-density...
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