Adrien Gressin

Semantic Approach in Image Change Detection

Change detection is a main issue in various domains, and especially for remote sensing purposes. Indeed, plethora of geospatial images are available and can be used to update geographical databases. In this paper, we propose a classification-based method to detect changes between a database and a more recent image. It is based both on an efficient training point selection and a hierarchical decision process. This allows to take into account the intrinsic heterogeneity of the objects and themes composing a database while limiting false detection rates. The reliability of the designed framework method is first assessed on simulated data, and then successfully applied on very high resolution satellite images and two land-cover databases.

Updating the new French national land cover database

Land-Cover databases (LC-DB) are very useful for environmental purposes, but need to be regularly updated to provide robust and instructive spatial indicators. Moreover, very high resolution satellite images allow to cover large areas regularly. Thus, automatic methods have to been developed to tackle this issue. In this paper, a hierarchical inspection method is proposed to both update and extend LC-DB using satellite image. This framework is successfully applied on the French National LC-DB using a single VHR satellite image.

Combining top-down and bottom-up approaches for building detection in a single very high resolution satellite image

Building detection from geospatial optical images has been a popular topic of research for the last twenty years and in particular with the emergence of very high resolution satellites. Existing methods exhibit various flaws and prevent them from being efficient at large scales of space and time: they are context-dependent, require a tedious parameter tuning or several data sources. In this paper, we propose a fully automatic method that alleviates some of these issues by combining the strengths of bottom-up and top-down approaches, i.e., of both classification and pattern recognition algorithms. This allows to correctly detect the objects by geometric prior knowledge while finely delineating their borders and preserving their shapes. The method is evaluated over a complex area of more than 230 buildings using a 0.5 m multispectral pansharpened Pleiades image.

Fusion of LiDAR and radar data for land-cover mapping in natural environments

Land-cover geodatabases are key products for the understanding of environmental systems and for setting up national and international prevention and protection policies. However, their automatic generation and update remain complicated with high accuracy over large scales. In natural environments, most of the existing solutions are semi-automatic in order to achieve a suitable discrimation of the large number of forest and crop classes. A large amount of remote sensing possibilities is at the moment available and data fusion appears to be the most suitable solution for that purpose. The paper tackles the issue of land-cover mapping in such areas assuming the existence of a partly non-updated 5-class geodatabase: buildings, roads, water, crops, forests. Lidar point clouds and Radar images at two spatial resolutions and bands are merged at the feature level and fed into an efficient supervised classification framework. Results show that some classes benefit from the joint exploitation of multiple observations in terms of accuracy or recall.

Multi-temporal optical VHR image fusion for Land-Cover mapping

Land-Cover databases (LC-DB) are very useful for environmental purposes, but need to be semantically detailed to provide robust and instructive spatial indicators. Moreover, remote sensed data allow to cover large areas with high temporal resolution. Such multi-temporal data are very useful input to discriminate LC classes. Nevertheless, automatic fusion method need to be developed to provide high quality LC-DB. In this paper, several fusion methods are proposed and introduced in an existing Land-Cover mapping framework. Those fusion methods allow to take advantage of multi-temporal data. Those methods are compared, and assessed thanks to a very high resolution LC-DB.

UN-sensored very high resolution Land-Cover mapping

Land-Cover databases (LC-DB) are mandatory for environmental purposes, but need to be regularly updated to provide robust and instructive spatial indicators. Moreover, an increasing number of sensors, such as optical and SAR satellite images or Lidar point cloud, allow to cover large areas regularly, and with a very high precision. Thus, automatic methods have to be developed to take into account the complementarity of available observations. In this paper, several fusion methods are proposed and introduced in an existing Land-Cover mapping framework. Those methods are compared on several scenarii (based on optical, SAR and Lidar datasets), and evaluated thanks to a very high resolution LC-DB.

A unified framework for land-cover database update and enrichment using satellite imagery

2D land-cover databases (LC-DB) have been established at various levels (global, national or regional scales), various spatial samplings and for various themes of interest (forest, agriculture, urban areas, etc.). However, they exhibit many flaws (limited geometric accuracy, low coverage) and require to be updated with automatic algorithms. Very High Resolution satellite imagery offers a suitable solution for setting up such on-purpose algorithms, and a large body of literature has tackled this topic. This paper proposes a framework that is able to deal with both LC-DB update of any kind and their enrichment in case of incomplete DB. The supervised classification-based solution integrates an efficient learning strategy that allows to capture the heterogeneity of the appearances of the various themes of interest. The proposed framework is favorably compared with two state-of-the-art methods, on a reconstructed dataset, composed of sub-metric satellite image patches.