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AIRBORNE LIDAR FEATURE SELECTION FOR URBAN CLASSIFICATION USING RANDOM FORESTS
Various multi-echo and Full-waveform (FW) lidar features can be processed. In this paper, multiple classifers are applied to lidar feature selection for urban scene classification. Random forests areExpand
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Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests
TLDR
We present a multi-source framework using aerial lidar (multi-echo and full waveform) and aerial multispectral image data to extract urban vegetation with high density point clouds. Expand
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Large-scale classification of water areas using airborne topographic lidar data
TLDR
This paper presents an automatic, efficient, and versatile workflow for land/water classification of airborne topographic lidar points, effective at large scales (>300 km 2 ). Expand
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RETRIEVING FOREST STRUCTURE VARIABLES FROM VERY HIGH RESOLUTION SATELLITE IMAGES USING AN AUTOMATIC METHOD
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We propose a method to describe the forest structure of maritime pine stands from Very High Resolution satellite imagery using spectral and Haralick's texture features. Expand
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Comparison of Pleiades and LiDAR Digital Elevation Models for Terraces Detection in Farmlands
TLDR
Pleiades DEMs can be used to automatically detect terrace slopes greater than 2 m with a detection rate of more than 80% of the total length of the terraces. Expand
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Object-based change detection in wind storm-damaged forest using high-resolution multispectral images
TLDR
An efficient, quasi-automatic object-based method for change mapping using high-spatial-resolution (HR) (5–10 m) satellite imagery is proposed. Expand
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Satellite Image Time Series Classification With Pixel-Set Encoders and Temporal Self-Attention
TLDR
We propose an alternative approach in which the convolutional layers are advantageously replaced with encoders operating on unordered sets of pixels to exploit the typically coarse resolution of publicly available satellite images. Expand
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Terrain Modeling From Lidar Range Data in Natural Landscapes: A Predictive and Bayesian Framework
TLDR
We present a global methodology for estimating the terrain height by deriving a predictive filter paradigm that combines the predicted topographic values and the effective measured values. Expand
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Spectral Band Selection for Urban Material Classification Using Hyperspectral Libraries
TLDR
A superspectral sensor dedicated to urban materials classification and spectral band subsets for such sensor. Expand
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Terrain modeling from lidar data: Hierarchical K-means filtering and Markovian regularization
Lidar 3D point cloud corresponds to the terrestrial topography, including true ground and objects belonging either to vegetated areas or to human made features. This paper deals with DTM (digitalExpand
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