Didier Boldo

Detection, Characterization, and Modeling Vegetation in Urban Areas From High-Resolution Aerial Imagery

Research in the area of 3-D city modeling from remote sensed data greatly developed in recent years with an emphasis on systems dealing with the detection and representation of man-made objects, such as buildings and streets. While these systems produce accurate representations of urban environments, they ignore information about the vegetation component of a city. This paper presents a complete image analysis system which, from high-resolution color infrared (CIR) digital images, and a Digital Surface Model (DSM), extracts, segments, and classifies vegetation in high density urban areas, with very high reliability. The process starts with the extraction of all vegetation areas using a supervised classification system based on a Support Vector Machines (SVM) classifier. The result of this first step is further on used to separate trees from lawns using texture criteria computed on the DSM. Tree crown borders are identified through a robust region growing algorithm based on tree-shape criteria. A SVM classifier gives the species class for each tree-region previously identified. This classification is used to enhance the appearance of 3-D city models by a realistic representation of vegetation according to the vegetation land use, shape and tree species.

A high-reliability, high-resolution method for land cover classification into forest and non-forest

We present several methods for per-region land-cover classification based on distances on probability distributions and whole-region probabilities. We present results on using this method for locating forest areas in high-resolution aerial images with very high reliability, achieving more than 95% accuracy, using raw radiometric channels as well as derived color and texture features. Region boundaries are obtained from a multi-scale hierarchical segmentation or from a registration of cadastral maps.

Automatic Extraction of Urban Vegetation Structures from High Resolution Imagery and Digital Elevation Model

This paper presents a method for automatic extraction and characterisation of vegetation structures (such as trees, shrubs, hedges or lawns) in high density urban areas. We present a hierarchical strategy to extract, analyze and delineate vegetation areas according to their height. Spectral indices are used to detect urban vegetation areas. We differentiate lawns from treed areas by computing a texture operator on the digital elevation model (DEM) corresponding to the vegetation areas previously detected. A robust region growing method based on the DEM is developed for an accurate delineation of tree crowns. We evaluate the accuracy of the tree crown delineation results to a reference manual delineation. Results obtained are discussed and the influential factors are put forward.

Model-Based Analysis–Synthesis for Realistic Tree Reconstruction and Growth Simulation

Due to complexity, vegetation analysis and reconstruction of remote sensing data are challenging problems. Using architectural tree models combined with model inputs estimated from aerial image analysis, this paper presents an analysis-synthesis approach for urban vegetation detection, modeling, and reconstruction. Tree species, height, and crown size information are extracted by aerial image analysis. These variables serve for model inversion to retrieve plant age, climatic growth conditions, and competition with neighbors. Functional-structural individual-based tree models are used to reconstruct and visualize virtual trees and their time evolutions realistically in a 3-D viewer rendering the models with geographical coordinates in the reconstructed scene. Our main contributions are: 1) a novel approach for generating plant models in 3-D reconstructed scenes based on the analysis of the geometric properties of the data, and 2) a modeling workflow for the reconstruction and growth simulation of vegetation in urban or natural environments.

Remote sensing of aerosols in urban areas: sun/shadow retrieval procedure from airborne very high spatial resolution images

Remote sensing of urban areas is currently in significant development thanks to the achievement of new instruments allowing the observation of cities at very high spatial resolution (∼1m). With such sensors, appropriate techniques must be developed. The characterization of urban aerosols to perform atmospheric corrections of remote sensing images is an issue that can take advantage of those new possibilities. A new characterization procedure of the atmospheric particles is presented in this paper. Based on the observation of sun/shadow transitions, it allows the retrieval of an aerosol model and of its spectral optical thickness. Retrieval results performed with synthetic images are presented to show the potential of this new method.

Automatic extraction and classification of vegetation areas from high resolution images in urban areas

This paper presents a complete high resolution aerial-images processing workflow to detect and characterize vegetation structures in high density urban areas. We present a hierarchical strategy to extract, analyze and delineate vegetation areas according to their height. To detect urban vegetation areas, we develop two methods, one using spectral indices and the second one based on a Support Vector Machines (SVM) classifier. Once vegetation areas detected, we differentiate lawns from treed areas by computing a texture operator on the Digital Surface Model (DSM). A robust region growing method based on the DSM is proposed for an accurate delineation of tree crowns. Delineation results are compared to results obtained by a Random Walk region growing technique for tree crown delineation. We evaluate the accuracy of the tree crown delineation results to a reference manual delineation. Results obtained are discussed and the influential factors are put forward.

Simulation of at sensor signals over urban area: two cases study

As the resolution of new earth observation sensors increases, a precise modeling of the signal is necessary for the urban medium. For very high spatial resolution, 3D structure and directional effects must be taken into account. This paper aims to estimate the contribution of the facades and the directional properties in the simulation of at sensor signals. Two opposite urban scenes illustrate this study.