TARABALKA , Yuliya

Hyperspectral imaging enables accurate classification, but also presents challenges of high-dimensional data analysis. While pixelwise classification methods classify each pixel independently, recent studies have shown the advantage of considering the correlations between spatially adjacent pixels for accurate image analysis. This paper...

The latest generation of aerial- and satellite-based imaging sensors acquires huge volumesof Earth's images with high spatial, spectral and temporal resolution, which open the doorto a large range of important applications, such as the monitoring of natural disasters, theplanning of urban environments and precision agriculture. In order to...

A new spectral-spatial method for classification of hyperspectral images is proposed. The HSegClas method is based on the integration of probabilistic classification and shape analysis within the hierarchical step-wise optimization algorithm. First, probabilistic support vector machines classification is applied. Then, at each iteration two...

We propose a new spectral-spatial method for hyperspectral image classification based on a graph cut. The classification task is formulated as an energy minimization problem on the graph of image pixels, and is solved by using the graph-cut alpha-expansion approach. The energy to optimize is computed as a sum of data and interaction energy...

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We propose a new method based on graph cuts for the segmentation of burned areas in time series of satellite images. The method consists in rewriting a segmentation problem as a (s, t)-min-cut on the spatio-temporal image graph and computing this minimal cut. As burned areas grow in time, we introduce growth constraint in graph cuts by using...

While geographic information systems typically use polygonal representations to map Earth's objects, most state-of-the-art methods produce maps by performing pixelwise classification of remote sensing images, then vectorizing the outputs. This paper studies if one can learn to directly output a vectorial semantic labeling of the image. We here...

Semantic segmentation on satellite images is used to automatically detect and classify objects of interest over very large areas. Training a neural network for this task generally requires a lot of human-made ground truth classification masks for each object class of interest. We aim to reduce the time spent by humans in the whole process of...

In machine learning the best performance on a certain task is achieved by fully supervised methods when perfect ground truth labels are available. However, labels are often noisy, especially in remote sensing where manually curated public datasets are rare. We study the multi-modal cadaster map alignment problem for which available annotations...

We tackle here the problem of multimodal image non-rigid registration, which is of prime importance in remote sensing and medical imaging. The difficulties encountered by classical registration approaches include feature design and slow optimization by gradient descent. By analyzing these methods, we note the significance of the notion of...

The modern optical satellite sensors capture images in stereo and tri-stereo acquisition modes. This allows reconstruction of high-resolution (30-70 cm) topography from the satellite data. However, numerous areas on the Earth exhibit complex topography with a lot of "discontinuities". One case is tectonic fault sites, which form steep...

The recent advances in hyperspectral remote sensing technology allow the simultaneous acquisition of hundreds of spectral wavelengths for each image pixel. This rich spectral information of the hyperspectral data makes it possible to discriminate different physical substances, leading to a potentially more accurate classification and thus...