Journal ArticleDOI
A Gibbs Point Process for Road Extraction from Remotely Sensed Images
TLDR
A new method for the extraction of roads from remotely sensed images is proposed, under the assumption that roads form a thin network in the image, by connected line segments by minimizing an energy function.Abstract:
In this paper we propose a new method for the extraction of roads from remotely sensed images. Under the assumption that roads form a thin network in the image, we approximate such a network by connected line segments.
To perform this task, we construct a point process able to simulate and detect thin networks. The segments have to be connected, in order to form a line-network. Aligned segments are favored whereas superposition is penalized. These constraints are enforced by the interaction model (called the Candy model). The specific properties of the road network in the image are described by the data term. This term is based on statistical hypothesis tests.
The proposed probabilistic model can be written within a Gibbs point process framework. The estimate for the network is found by minimizing an energy function. In order to avoid local minima, we use a simulated annealing algorithm, based on a Monte Carlo dynamics (RJMCMC) for finite point processes. Results are shown on SPOT, ERS and aerial images.read more
Citations
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Proceedings ArticleDOI
DeepRoadMapper: Extracting Road Topology from Aerial Images
TL;DR: This paper takes advantage of the latest developments in deep learning to have an initial segmentation of the aerial images and proposes an algorithm that reasons about missing connections in the extracted road topology as a shortest path problem that can be solved efficiently.
Journal ArticleDOI
Automatic recognition of man-made objects in high resolution optical remote sensing images by SVM classification of geometric image features
TL;DR: An image processing system for the detection and recognition of man-made objects in high resolution optical remote sensing images using a high number of geometric image features which allows to characterise several classes of objects with different geometric properties using a supervised learning approach.
Journal ArticleDOI
Learning Aerial Image Segmentation from Online Maps
TL;DR: Can training with large-scale publicly available labels replace a substantial part of the manual labeling effort and still achieve sufficient performance and can satisfying performance can be obtained with significantly less manual annotation effort?
Proceedings ArticleDOI
A Higher-Order CRF Model for Road Network Extraction
TL;DR: A novel CRF formulation for road labeling is developed, in which the prior is represented by higher-order cliques that connect sets of super pixels along straight line segments, which significantly improves both the per-pixel accuracy and the topological correctness of the extracted roads.
Journal ArticleDOI
Point processes for unsupervised line network extraction in remote sensing
TL;DR: This paper addresses the problem of unsupervised extraction of line networks from remotely sensed images by model the target line network by an object process, where the objects correspond to interacting line segments, and shows the relevance of using an offline computation of the data potential.
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