7,718 research outputs found
Discrete anisotropic radiative transfer (DART 5) for modeling airborne and satellite spectroradiometer and LIDAR acquisitions of natural and urban landscapes
International audienceSatellite and airborne optical sensors are increasingly used by scientists, and policy makers, and managers for studying and managing forests, agriculture crops, and urban areas. Their data acquired with given instrumental specifications (spectral resolution, viewing direction, sensor field-of-view, etc.) and for a specific experimental configuration (surface and atmosphere conditions, sun direction, etc.) are commonly translated into qualitative and quantitative Earth surface parameters. However, atmosphere properties and Earth surface 3D architecture often confound their interpretation. Radiative transfer models capable of simulating the Earth and atmosphere complexity are, therefore, ideal tools for linking remotely sensed data to the surface parameters. Still, many existing models are oversimplifying the Earth-atmosphere system interactions and their parameterization of sensor specifications is often neglected or poorly considered. The Discrete Anisotropic Radiative Transfer (DART) model is one of the most comprehensive physically based 3D models simulating the Earth-atmosphere radiation interaction from visible to thermal infrared wavelengths. It has been developed since 1992. It models optical signals at the entrance of imaging radiometers and laser scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental configuration and instrumental specification. It is freely distributed for research and teaching activities. This paper presents DART physical bases and its latest functionality for simulating imaging spectroscopy of natural and urban landscapes with atmosphere, including the perspective projection of airborne acquisitions and LIght Detection And Ranging (LIDAR) waveform and photon counting signals
Manifold Forests: Closing the Gap on Neural Networks
Decision forests (DFs), in particular random forests and gradient boosting
trees, have demonstrated state-of-the-art accuracy compared to other methods in
many supervised learning scenarios. In particular, DFs dominate other methods
in tabular data, that is, when the feature space is unstructured, so that the
signal is invariant to permuting feature indices. However, in structured data
lying on a manifold---such as images, text, and speech---deep networks (DNs),
specifically convolutional deep networks (ConvNets), tend to outperform DFs. We
conjecture that at least part of the reason for this is that the input to DNs
is not simply the feature magnitudes, but also their indices (for example, the
convolution operation uses feature locality). In contrast, naive DF
implementations fail to explicitly consider feature indices. A recently
proposed DF approach demonstrates that DFs, for each node, implicitly sample a
random matrix from some specific distribution. These DFs, like some classes of
DNs, learn by partitioning the feature space into convex polytopes
corresponding to linear functions. We build on that approach and show that one
can choose distributions in a manifold-aware fashion to incorporate feature
locality. We demonstrate the empirical performance on data whose features live
on three different manifolds: a torus, images, and time-series. In all
simulations, our Manifold Oblique Random Forest (MORF) algorithm empirically
dominates other state-of-the-art approaches that ignore feature space structure
and challenges the performance of ConvNets. Moreover, MORF runs significantly
faster than ConvNets and maintains interpretability and theoretical
justification. This approach, therefore, has promise to enable DFs and other
machine learning methods to close the gap to deep networks on manifold-valued
data.Comment: 12 pages, 4 figure
Learning Prescriptive ReLU Networks
We study the problem of learning optimal policy from a set of discrete
treatment options using observational data. We propose a piecewise linear
neural network model that can balance strong prescriptive performance and
interpretability, which we refer to as the prescriptive ReLU network, or
P-ReLU. We show analytically that this model (i) partitions the input space
into disjoint polyhedra, where all instances that belong to the same partition
receive the same treatment, and (ii) can be converted into an equivalent
prescriptive tree with hyperplane splits for interpretability. We demonstrate
the flexibility of the P-ReLU network as constraints can be easily incorporated
with minor modifications to the architecture. Through experiments, we validate
the superior prescriptive accuracy of P-ReLU against competing benchmarks.
Lastly, we present examples of interpretable prescriptive trees extracted from
trained P-ReLUs using a real-world dataset, for both the unconstrained and
constrained scenarios.Comment: 17 pages, 6 figures, accepted at ICML 2
A NOVEL SPLIT SELECTION OF A LOGISTIC REGRESSION TREE FOR THE CLASSIFICATION OF DATA WITH HETEROGENEOUS SUBGROUPS
A logistic regression tree (LRT) is a hybrid machine learning method that combines a decision tree model and logistic regression models. An LRT recursively partitions the input data space through splitting and learns multiple logistic regression models optimized for each subpopulation. The split selection is a critical procedure for improving the predictive performance of the LRT. In this paper, we present a novel separability-based split selection method for the construction of an LRT. The separability measure, defined on the feature space of logistic regression models, evaluates the performance of potential child models without fitting, and the optimal split is selected based on the results. Heterogeneous subgroups that have different class-separating patterns can be identified in the split process when they exist in the data. In addition, we compare the performance of our proposed method with the benchmark algorithms through experiments on both synthetic and real-world datasets. The experimental results indicate the effectiveness and generality of our proposed method
New SAR Target Imaging Algorithm based on Oblique Projection for Clutter Reduction
International audienceWe have developed a new Synthetic Aperture Radar (SAR) algorithm based on physical models for the detection of a Man-Made Target (MMT) embedded in strong clutter (trunks in a forest). The physical models for the MMT and the clutter are represented by low-rank subspaces and are based on scattering and polarimetric properties. Our SAR algorithm applies the oblique projection of the received signal along the clutter subspace onto the target subspace. We compute its statistical performance in terms of probabilities of detection and false alarms. The performances of the proposed SAR algorithm are improved compared to those obtained with existing SAR algorithms: the MMT detection is greatly improved and the clutter is rejected. We also studied the robustness of our new SAR algorithm to interference modeling errors. Results on real FoPen (Foliage Penetration) data showed the usefulness of this approach
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