Léonard Denise

Extraction and Three-Dimensional Reconstruction of Isolated Buildings in Urban Scenes From High-Resolution Optical and SAR Spaceborne Images

In this paper, we propose a new complete semi-automatic processing chain able to provide, from a couple of high-resolution optical and synthetic aperture radar (SAR) images, a simple 3-D reconstruction of buildings in urban scenes. A sequence of processing, exploring the complementarities of both optical and SAR data, is developed for building reconstruction. The chain is decomposed into the main following steps: First, potential building footprints are extracted from the monoscopic optical image through global detection followed by a boundary refinement. Then, the optical footprints are projected and registered into SAR data to get a fine superposition between optical and SAR homologous ground features. Finally, the last step, based on the optimization of two SAR criteria, is performed to deal with building validation and height retrieval. Each of these steps is methodologically described and applied on scenes of interest on Quickbird and TerraSAR-X images. A qualification of each reconstructed building by a score of confidence is then proposed. Good results of building detection are obtained, and relevant height estimations are retrieved.

Building extraction and 3D reconstruction in urban areas from high-resolution optical and SAR imagery

In this paper, we propose to investigate the joint use of high-resolution optical and SAR data, for building extraction and 3D reconstruction in large urban areas. A sequence of methods providing, in a semi-automatic way, the building detection and reconstruction is presented. Potential building footprints are first extracted on an optical image by a two-phases process (coarse detection and boundaries refinement). The framework of fusion with SAR data is then developed. A first step of registration allows us to get a fine superposition of optical and SAR building features. Then, we show how to take benefit from the introduction of SAR data: The proposed methodology, based on the optimization of criteria refering to building SAR characteristics, allows us to simultaneously deal with the building presence validation and with the height retrieval.

Building detection by fusion of optical and SAR features in metric resolution data

In this paper, we propose to jointly use optical and SAR features issued from satellite images with metric resolution, to deal with the problem of building detection and height retrieval. In a first part, a process — described in previous works — for building boundary extraction, is briefly exposed and illustrated on a Quickbird urban scene. In a second part, the framework of fusion with SAR data is developed. After the steps of feature projection and registration, a new method for building height estimation is proposed. This one is based on a Likelihood criterion optimization and is built on the scheme ”height hypothesis — characteristic areas generation — energy minimization”. Such an approach refers to the adequation between a potential building signature and the real signature, effectively present on the SAR image and defined by characteristic building areas such as layover, shadow, roof and ground / wall echo. This height retrieval process is tested on simulated and real (TerraSAR-X) data.

Building detection and height retrieval in urban areas in the framework of high resolution optical and SAR data fusion

In this paper, we propose a symmetrized version of a semiautomatic processing chain, able to provide the simple 3D reconstruction of buildings in urban scenes, from high-resolution optical and SAR imagery. The new elaborated chain gives an equivalent part to the optical and SAR components, in order to fully exploit complementary information provided by proper building features in both images.

Feature based maximum likelihood model inversion for three-dimensional building extraction from single high resolution SAR images

In this paper, a new method for three-dimensional building extraction from building specific low-level features is presented. This method, which addresses both building detection and building parameter estimation, relies on a maximum likelihood model inversion performed from low-level primitives, after they have been extracted from a single SAR HR amplitude image. The effectiveness of the proposed approach, which has been designed so as to robustly deal with noisy primitives, is demonstrated on a TerraSar-X image.

A symmetric scheme for building reconstruction from a couple of HR optical and SAR data

In this paper, we propose a new version of a complete processing chain for simple building reconstruction, from a couple of HR optical and SAR satellite images.

Building change detection in a couple of optical and SAR high resolution images

A novel approach for building extraction in high resolution optical and SAR images and its application to building change detection are presented in this paper. The proposed object-based building extraction technique relies on an hypothesis generation-optimization-validation scheme, whose estimation and detection performances are characterized on Quickbird and TerraSAR-X semi-urban images. The introduced building extraction approach is then applied to building change detection in a pair of high resolution heterogeneous images.