Optimal Clustering Framework for Hyperspectral Band Selection

Band selection, by choosing a set of representative bands in hyperspectral image (HSI), is an effective method to reduce the redundant information without compromising the original contents. Recently, various unsupervised band selection methods have been proposed, but most of them are based on approximation algorithms which can only obtain suboptimal solutions toward a specific objective function. This paper focuses on clustering-based band selection, and proposes a new framework to solve the above dilemma, claiming the following contributions: 1) An optimal clustering framework (OCF), which can obtain the optimal clustering result for a particular form of objective function under a reasonable constraint. 2) A rank on clusters strategy (RCS), which provides an effective criterion to select bands on existing clustering structure. 3) An automatic method to determine the number of the required bands, which can better evaluate the distinctive information produced by certain number of bands. In experiments, the proposed algorithm is compared to some state-of-the-art competitors. According to the experimental results, the proposed algorithm is robust and significantly outperform the other methods on various data sets

Bi-Objective Nonnegative Matrix Factorization: Linear Versus Kernel-Based Models

Nonnegative matrix factorization (NMF) is a powerful class of feature extraction techniques that has been successfully applied in many fields, namely in signal and image processing. Current NMF techniques have been limited to a single-objective problem in either its linear or nonlinear kernel-based formulation. In this paper, we propose to revisit the NMF as a multi-objective problem, in particular a bi-objective one, where the objective functions defined in both input and feature spaces are taken into account. By taking the advantage of the sum-weighted method from the literature of multi-objective optimization, the proposed bi-objective NMF determines a set of nondominated, Pareto optimal, solutions instead of a single optimal decomposition. Moreover, the corresponding Pareto front is studied and approximated. Experimental results on unmixing real hyperspectral images confirm the efficiency of the proposed bi-objective NMF compared with the state-of-the-art methods

Índices espectrais baseados em programação genética para classificação de imagens de sensoriamento remoto

Orientador: Ricardo da Silva TorresDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Sensoriamento remoto é o conjunto de técnicas que permitem, por meio de sensores, analisar objetos a longas distâncias sem estabelecer contato físico com eles. Atualmente, sua contribuição em ciências naturais é enorme, dado que é possível adquirir imagens de alvos em mais regiões do espectro eletromagnético além do canal visível. Trabalhar com imagens compostas por múltiplas bandas espectrais requer tratar grandes quantidades de informação associada a uma única entidade, coisa que afeta negativamente o desempenho de algoritmos de predição, fazendo nacessário o uso de técnicas de redução da dimensionalidade. Este trabalho apresenta uma abordagem de extração de características baseada em índices espectrais aprendidos por Programação Genética (GP), que projetam os dados associados aos pixels em novos espaços de características, com o objetivo de aprimorar a acurácia de algoritmos de classificação. Índices espectrais são funções que relacionam a refletância, em canais específicos do espectro, com valores reais que podem ser interpretados como a abundância de características de interesse de objetos captados à distância. Com GP é possível aprender índices que maximizam a separabilidade de amostras de duas classes diferentes. Assim que os índices especializados para cada par possível de classes são obtidos, empregam-se duas abordagens diferentes para combiná-los e construir um sistema de classificação de pixels. Os resultados obtidos para os cenários binário e multi-classe mostram que o método proposto é competitivo com respeito a técnicas tradicionais de redução da dimensionalidade. Experimentos adicionais aplicando o método para análise sazonal de biomas tropicais mostram claramente a superioridade de índices aprendidos por GP para propósitos de discriminação, quando comparados a índices desenvolvidos por especialistas, independentemente da especificidade do problemaAbstract: Remote sensing is the set of techniques that allow, by means of sensor technologies, to analyze objects at long distances without making physical contact with them. Currently, its contribution for natural sciences is enormous, since it is possible to acquire images of target objects in more regions of the electromagnetic spectrum than the visible region only. Working with images composed of various spectral bands demands dealing with huge amounts of data associated with single entities, which affects negatively the performance in prediction tasks, and makes necessary the use of dimensionality reduction techniques. This work introduces a feature extraction approach, based on spectral indices learned by Genetic Programming (GP), to project data from pixel values into new feature spaces aiming to improve classification accuracy. Spectral indices are functions that map the reflectance of remotely sensed objects in specific wavelength intervals, into real scalars that can be interpreted as the abundance of features of interest. Through GP, it is possible to learn indices that maximize the separability of samples from two different classes. Once the indices specialized for all the pairs of classes are obtained, they are used in two different approaches to fuse them into a pixel classification system. Results for the binary and multi-class scenarios show that the proposed method is competitive with respect to traditional dimensionality reduction techniques. Additional experiments in tropical biomes seasonal analysis show clearly how superior GP-based spectral indices are for discrimination purposes, when compared to indices developed by experts, regardless the specificity of the problemMestradoCiência da ComputaçãoMestre em Ciência da Computação134089/2015-4CNP

Advances in Multi-Sensor Data Fusion: Algorithms and Applications

With the development of satellite and remote sensing techniques, more and more image data from airborne/satellite sensors have become available. Multi-sensor image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single sensor. In image-based application fields, image fusion has emerged as a promising research area since the end of the last century. The paper presents an overview of recent advances in multi-sensor satellite image fusion. Firstly, the most popular existing fusion algorithms are introduced, with emphasis on their recent improvements. Advances in main applications fields in remote sensing, including object identification, classification, change detection and maneuvering targets tracking, are described. Both advantages and limitations of those applications are then discussed. Recommendations are addressed, including: (1) Improvements of fusion algorithms; (2) Development of “algorithm fusion” methods; (3) Establishment of an automatic quality assessment scheme