Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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Convex Analysisの二,三の進展について

On robust estimation of the location parameter

A random forest (RF) classifier is an ensemble classifier that produces multiple decision trees, using a randomly selected subset of training samples and variables. This classifier has become popular within the remote sensing community due to the accuracy of its classifications. The overall objective of this work was to review the utilization of RF classifier in remote sensing. This review has revealed that RF classifier can successfully handle high data dimensionality and multicolinearity, being both fast and insensitive to overfitting. It is, however, sensitive to the sampling design. The variable importance (VI) measurement provided by the RF classifier has been extensively exploited in different scenarios, for example to reduce the number of dimensions of hyperspectral data, to identify the most relevant multisource remote sensing and geographic data, and to select the most suitable season to classify particular target classes. Further investigations are required into less commonly exploited uses of this classifier, such as for sample proximity analysis to detect and remove outliers in the training samples.

Random forest in remote sensing: A review of applications and future directions

Ce papier illustre l'utilisation de techniques de traitement d'image pour segmenter le plan temps-frequence (et temps-echelle). Cette etude est appliquee a la separation d'ondes sismiques. On considere des donnees issues d'une rangee de capteurs. Pour chaque signal enregistre, l'application d'une transformee temps-frequence decrit l'information dans une image sur laquelle les differentes ondes sont localisees et separees. La segmentation par Ligne de Partage des Eaux (LPE) de ces representations a deux dimensions permet une caracterisation automatique des filtres temps-frequence menant a la separation des differentes ondes. Ensuite, pour appliquer cet algorithme de separation a l'ensemble des signaux issus des differents capteurs, on utilise la continuite d'un signal a l'autre pour effectuer le suivi des differentes ondes d'une image a l'autre. Hormis une phase d'initialisation, on obtient ainsi un algorithme automatique. Cet algorithme est valide et compare a une methode classique en sismique sur un jeu de donnees reelles. En comparaison, l'algorithme propose a l'avantage de separer toutes les ondes simultanement, et sans introduire d'artefacts. Les limites de l'algorithme sont atteintes lorsque les motifs caracterisant chacune des ondes ne sont plus convenablement separes dans la representation temps-frequence.

Partition d'une séquence d'images temps-échelle pour la séparation d'ondes dans un profil sismique

In recent years, there have been many advances in sensor technology which provides more useful information about the observed scene. Two of the newest remote sensing technologies are hyperspectral (HS) and Light Detection And Ranging (LiDAR) sensors. Since pixels in a small spatial neighborhood are more likely to share similar abundances, hypergraph regularization (HG-NMF) can be employed to handle the similarity relevance among the spatial neighborhood pixels. In this paper, we provide a LiDAR data-aided HS unmixing using HG-NMF. The composite usage of all these valuable information can lead to higher accuracy unmixing results. The obtained convex optimization problem is solved by Spectral Unmixing by Split Augmented Lagrangian (SUnSAL-TV) Algorithm. Experiments on synthetic data are conducted. The advantage of HG-NMF regularization is also demonstrated.

LiDAR Data-Aided Hypergraph Regularized Multi-Modal Unmixing

In this letter, a novel neural architecture search (NAS) method based on reinforcement learning (RL), called RLNAS, is devised to realize the automatic architecture design in the field of hyperspectral unmixing (HU). This method first trains the search network in the constructed self-supervised datasets based on hyperspectral images. The block-based searching and weight-sharing strategies are then introduced to reduce the computational cost in the training phase. The final optimal architecture is obtained by optimizing the multiobjective reward function to balance the trade-off between accuracy and computational efficiency. Compared with the state-of-the-art unmixing algorithms, the proposed RLNAS method can yield better unmixing results on the synthetic and real hyperspectral datasets, which verifies its effectiveness and superiority. In addition, the proposed method offers promising potential of the NAS for HU.

Reinforcement Learning for Neural Architecture Search in Hyperspectral Unmixing

In this paper we will review seminal contributions of Prof. Jose Bioucas Dias for the improvement of the spatial resolution of hyperspectral images. Be it through the extension of pansharpening algorithms with spatial and spectral sparsity priors, using spectral unmixing, using a low-rank assumption from complementary multisource data, or by designing an edge-preserving convex formulation, Jose Bioucas Dias set up very solid and rigourous foundations for countless subsequent works.

On Hyperspectral Super-Resolution

A method based on spectral unmixing (SU) for snow cover estimation from a time series of optical images is proposed in this letter. Specifically, we have developed an endmember estimation procedure that exploits the temporal continuity of a scene. Consecutive dates are jointly processed for a more precise description of background materials improving the estimation of a fractional snow cover map. The proposed workflow relies on up to three consecutive acquisitions over the same area to extract an appropriate set of background spectra to be considered as endmembers. In greater details, multiple sets of endmembers are extracted from different images in the time series by a geometrical automated endmember extraction algorithm and the most relevant one is selected in terms of reconstruction error. Snow cover maps are then estimated by SU considering as endmembers the snow spectra coming from a spectral library and those associated with the background materials as estimated by the proposed procedure. The proposed technique is quantitatively validated considering Moderate-Resolution Imaging Spectroradiometer Terra data over the French Alps and Moroccan High Atlas image time series and comparing the estimated snow cover maps with high-resolution reference data. The experiment clearly demonstrates the effectiveness of the generated set of endmembers using three different approaches to abundance estimation.

Jocelyn Chanussot

Papers

Partition d'une séquence d'images temps-échelle pour la séparation d'ondes dans un profil sismique

LiDAR Data-Aided Hypergraph Regularized Multi-Modal Unmixing

Reinforcement Learning for Neural Architecture Search in Hyperspectral Unmixing

On Hyperspectral Super-Resolution

Snow Cover Estimation From Image Time Series Based on Spectral Unmixing