Jocelyn Chanussot

Braids of partitions for the hierarchical representation and segmentation of multimodal images

By Guillaume Tochon, Mauro Dalla Mura, Miguel Angel Veganzones, Thierry Géraud, Jocelyn Chanussot

2019-07-01

In Pattern Recognition

Abstract

Hierarchical data representations are powerful tools to analyze images and have found numerous applications in image processing. When it comes to multimodal images however, the fusion of multiple hierarchies remains an open question. Recently, the concept of braids of partitions has been proposed as a theoretical tool and possible solution to this issue. In this paper, we demonstrate the relevance of the braid structure for the hierarchical representation of multimodal images. We first propose a fully operable procedure to build a braid of partitions from two hierarchical representations. We then derive a framework for multimodal image segmentation, relying on an energetic minimization scheme conducted on the braid structure. The proposed approach is investigated on different multimodal images scenarios, and the obtained results confirm its ability to efficiently handle the multimodal information to produce more accurate segmentation outputs.

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Constructing a braid of partitions from hierarchies of partitions

By Guillaume Tochon, Mauro Dalla Mura, Jocelyn Chanussot

2019-03-13

In Mathematical morphology and its application to signal and image processing – proceedings of the 14th international symposium on mathematical morphology (ISMM)

Abstract

Braids of partitions have been introduced in a theoretical framework as a generalization of hierarchies of partitions, but practical guidelines to derive such structures remained an open question. In a previous work, we proposed a methodology to build a braid of partitions by experimentally composing cuts extracted from two hierarchies of partitions, notably paving the way for the hierarchical representation of multimodal images. However, we did not provide the formal proof that our proposed methodology was yielding a braid structure. We remedy to this point in the present paper and give a brief insight on the structural properties of the resulting braid of partitions.

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Advances in utilization of hierarchical representations in remote sensing data analysis

By Guillaume Tochon, Mauro Dalla Mura, Miguel-Angel Veganzones, Silvia Valero, Philippe Salembier, Jocelyn Chanussot

2017-11-08

In Comprehensive remote sensing, 1st edition

Abstract

The latest developments in sensor design for remote sensing and Earth observation purposes are leading to images always more complex to analyze. Low-level pixel-based processing is becoming unadapted to efficiently handle the wealth of information they contain, and higher levels of abstraction are required. Region-based representations intend to exploit images as collections of regions of interest bearing some semantic meaning, thus easing their interpretation. However, the scale of analysis of the images has to be fixed beforehand, which can be problematic as different applications may not require the same scale of analysis. On the other hand, hierarchical representations are multiscale descriptions of images, as they encompass in their structures all potential regions of interest, organized in a hierarchical manner. Thus, they allow to explore the image at various levels of details and can serve as a single basis for many different further processings. Thanks to its flexibility, the binary partition tree (BPT) representation is one of the most popular hierarchical representations, and has received a lot of attention lately. This article draws a comprehensive review of the most recent works involving BPT representations for various remote sensing data analysis tasks, such as image segmentation and filtering, object detection or hyperspectral classification, and anomaly detection.

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Object tracking by hierarchical decomposition of hyperspectral video sequences: Application to chemical gas plume tracking

By Guillaume Tochon, Jocelyn Chanussot, Mauro Dalla Mura, Andrea Bertozzi

2017-04-20

In IEEE Transactions on Geoscience and Remote Sensing

Abstract

It is now possible to collect hyperspectral video sequences at a near real-time frame rate. The wealth of spectral, spatial and temporal information of those sequences is appealing for various applications, but classical video processing techniques must be adapted to handle the high dimensionality and huge size of the data to process. In this article, we introduce a novel method based on the hierarchical analysis of hyperspectral video sequences to perform object tracking. This latter operation is tackled as a sequential object detection process, conducted on the hierarchical representation of the hyperspectral video frames. We apply the proposed methodology to the chemical gas plume tracking scenario and compare its performances with state-of-the-art methods, for two real hyperspectral video sequences, and show that the proposed approach performs at least equally well.

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Estimating the number of endmembers to use in spectral unmixing of hyperspectral data with collaborative sparsity

By Lucas Drumetz, Guillaume Tochon, Jocelyn Chanussot, Christian Jutten

2016-11-22

In Proceedings of the 13th international conference on latent variable analysis and signal separation (LVA-ICA)

Abstract

Spectral Umixing (SU) in hyperspectral remote sensing aims at recovering the signatures of the pure materials in the scene (endmembers) and their abundances in each pixel of the image. The usual SU chain does not take spectral variability (SV) into account, and relies on the estimation of the Intrinsic Dimensionality (ID) of the data, related to the number of endmembers (NOE) to use. However, the ID can be significantly overestimated in difficult scenarios, and sometimes does not correspond to the desired scale and application dependent NOE. Spurious endmembers are then frequently extracted and included in the model. We propose an algorithm for SU incorporating SV, using collaborative sparsity to discard the least explicative endmembers in the whole image. We compute an algorithmic regularization path for this problem to select the optimal set of endmembers using a statistical criterion. Results on simulated and real data show the interest of the approach.

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