7 research outputs found
Optimal Transport for Change Detection on LiDAR Point Clouds
The detection of changes occurring in multi-temporal remote sensing data
plays a crucial role in monitoring several aspects of real life, such as
disasters, deforestation, and urban planning. In the latter context,
identifying both newly built and demolished buildings is essential to help
landscape and city managers to promote sustainable development. While the use
of airborne LiDAR point clouds has become widespread in urban change detection,
the most common approaches require the transformation of a point cloud into a
regular grid of interpolated height measurements, i.e. Digital Elevation Model
(DEM). However, the DEM's interpolation step causes an information loss related
to the height of the objects, affecting the detection capability of building
changes, where the high resolution of LiDAR point clouds in the third dimension
would be the most beneficial. Notwithstanding recent attempts to detect changes
directly on point clouds using either a distance-based computation method or a
semantic segmentation pre-processing step, only the M3C2 distance
computation-based approach can identify both positive and negative changes,
which is of paramount importance in urban planning. Motivated by the previous
arguments, we introduce a principled change detection pipeline, based on
optimal transport, capable of distinguishing between newly built buildings
(positive changes) and demolished ones (negative changes). In this work, we
propose to use unbalanced optimal transport to cope with the creation and
destruction of mass related to building changes occurring in a bi-temporal pair
of LiDAR point clouds. We demonstrate the efficacy of our approach on the only
publicly available airborne LiDAR dataset for change detection by showing
superior performance over the M3C2 and the previous optimal transport-based
method presented by Nicolas Courty et al.at IGARSS 2016.Comment: Submitted to IEEE International Geoscience and Remote Sensing
Symposium 2023 (IGARSS 2023
Implicit neural representation for change detection
Detecting changes that occurred in a pair of 3D airborne LiDAR point clouds,
acquired at two different times over the same geographical area, is a
challenging task because of unmatching spatial supports and acquisition system
noise. Most recent attempts to detect changes on point clouds are based on
supervised methods, which require large labelled data unavailable in real-world
applications. To address these issues, we propose an unsupervised approach that
comprises two components: Neural Field (NF) for continuous shape reconstruction
and a Gaussian Mixture Model for categorising changes. NF offer a grid-agnostic
representation to encode bi-temporal point clouds with unmatched spatial
support that can be regularised to increase high-frequency details and reduce
noise. The reconstructions at each timestamp are compared at arbitrary spatial
scales, leading to a significant increase in detection capabilities. We apply
our method to a benchmark dataset of simulated LiDAR point clouds for urban
sprawling. The dataset offers different challenging scenarios with different
resolutions, input modalities and noise levels, allowing a multi-scenario
comparison of our method with the current state-of-the-art. We boast the
previous methods on this dataset by a 10% margin in intersection over union
metric. In addition, we apply our methods to a real-world scenario to identify
illegal excavation (looting) of archaeological sites and confirm that they
match findings from field experts.Comment: Main article is 10 pages + 3 pages of supplementary. Conference style
pape
Quantile Propagation for Wasserstein-Approximate Gaussian Processes
We develop a new approximate Bayesian inference method for Gaussian process models with factorized non-Gaussian likelihoods. Our method---dubbed Quantile Propagation (QP)---is similar to expectation propagation (EP) but minimizes the L_2 Wasserstein distance rather than the Kullback-Leibler (KL) divergence. We consider the case where likelihood factors are approximated by a Gaussian form. We show that QP matches quantile functions rather than moments as in EP and has the same mean update but a smaller variance update than EP, thereby alleviating the over-estimation of the posterior variance exhibited by EP. Crucially, QP has the same favorable locality property as EP, and thereby admits an efficient algorithm. Experiments on classification and Poisson regression tasks demonstrate that QP outperforms both EP and variational Bayes
Optimal Transport for Data Fusion in Remote Sensing
International audienceOne of the main objective of data fusion is the integration of several acquisition of the same physical object, in order to build a new consistent representation that embeds all the information from the different modalities. In this paper, we propose the use of optimal transport theory as a powerful mean of establishing correspondences between the modalities. After reviewing important properties and computational aspects, we showcase its application to three remote sensing fusion problems: domain adaptation, time series averaging and change detection in LIDAR data