An Approach for the Customized High-Dimensional Segmentation of Remote Sensing Hyperspectral Images

Abstract: This paper addresses three problems in the field of hyperspectral image segmentation: the fact that the way an image must be segmented is related to what the user requires and the application; the lack and cost of appropriately labeled reference images; and, finally, the information loss problem that arises in many algorithms when high dimensional images are projected onto lower dimensional spaces before starting the segmentation process. To address these issues, the Multi-Gradient based Cellular Automaton (MGCA) structure is proposed to segment multidimensional images without projecting them to lower dimensional spaces. The MGCA structure is coupled with an evolutionary algorithm (ECAS-II) in order to produce the transition rule sets required by MGCA segmenters. These sets are customized to specific segmentation needs as a function of a set of low dimensional training images in which the user expresses his segmentation requirements. Constructing high dimensional image segmenters from low dimensional training sets alleviates the problem of lack of labeled training images. These can be generated online based on a parametrization of the desired segmentation extracted from a set of examples. The strategy has been tested in experiments carried out using synthetic and real hyperspectral images, and it has been compared to state-of-the-art segmentation approaches over benchmark images in the area of remote sensing hyperspectral imaging.Ministerio de Economía y competitividad; TIN2015-63646-C5-1-RMinisterio de Economía y competitividad; RTI2018-101114-B-I00Xunta de Galicia: ED431C 2017/1

Exploring synergetic effects of dimensionality reduction and resampling tools on hyperspectral imagery data classification

The present paper addresses the problem of the classification of hyperspectral images with multiple imbalanced classes and very high dimensionality. Class imbalance is handled by resampling the data set, whereas PCA and a supervised filter are applied to reduce the number of spectral bands. This is a preliminary study that pursues to investigate the benefits of combining several techniques to tackle the imbalance and the high dimensionality problems, and also to evaluate the order of application that leads to the best classification performance. Experimental results demonstrate the significance of using together these two preprocessing tools to improve the performance of hyperspectral imagery classification. Although it seems that the most effective order corresponds to first a resampling strategy and then a feature (or extraction) selection algorithm, this is a question that still needs a much more thorough investigation in the futureThis work has partially been supported by the Spanish Ministry of Education and Science under grants CSD2007–00018, AYA2008–05965–0596 and TIN2009–14205, the Fundació Caixa Castelló–Bancaixa under grant P1–1B2009–04, and the Generalitat Valenciana under grant PROMETEO/2010/02

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

Testing the Potential Application of Simulated Multispectral Data in Discriminating Tree Species in Taita Hills

Peer reviewe

Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics

In [1], we have explored the theoretical aspects of feature selection and evolutionary algorithms. In this chapter, we focus on optimization algorithms for enhancing data analytic process, i.e., we propose to explore applications of nature-inspired algorithms in data science. Feature selection optimization is a hybrid approach leveraging feature selection techniques and evolutionary algorithms process to optimize the selected features. Prior works solve this problem iteratively to converge to an optimal feature subset. Feature selection optimization is a non-specific domain approach. Data scientists mainly attempt to find an advanced way to analyze data n with high computational efficiency and low time complexity, leading to efficient data analytics. Thus, by increasing generated/measured/sensed data from various sources, analysis, manipulation and illustration of data grow exponentially. Due to the large scale data sets, Curse of dimensionality (CoD) is one of the NP-hard problems in data science. Hence, several efforts have been focused on leveraging evolutionary algorithms (EAs) to address the complex issues in large scale data analytics problems. Dimension reduction, together with EAs, lends itself to solve CoD and solve complex problems, in terms of time complexity, efficiently. In this chapter, we first provide a brief overview of previous studies that focused on solving CoD using feature extraction optimization process. We then discuss practical examples of research studies are successfully tackled some application domains, such as image processing, sentiment analysis, network traffics / anomalies analysis, credit score analysis and other benchmark functions/data sets analysis

Classification of high dimensional and imbalanced hyperspectral imagery data

The present paper addresses the problem of the classification of hyperspectral images with multiple imbalanced classes and very high dimensionality. Class imbalance is handled by resampling the data set, whereas PCA is applied to reduce the number of spectral bands. This is a preliminary study that pursues to investigate the benefits of using together these two techniques, and also to evaluate the application order that leads to the best classification performance. Experimental results demonstrate the significance of combining these preprocessing tools to improve the performance of hyperspectral imagery classification. Although it seems that the most effective order of application corresponds to first a resampling algorithm and then PCA, this is a question that still needs a much more thorough investigationPartially supported by the Spanish Ministry of Education and Science under grants CSD2007–00018, AYA2008–05965–0596–C04–04/ESP and TIN2009–14205–C04–04, and by Fundació Caixa Castelló–Bancaixa under grant P1–1B2009–0

A Neural Network Approach to Identify Hyperspectral Image Content

A Hyperspectral is the imaging technique that contains very large dimension data with the hundreds of channels. Meanwhile, the Hyperspectral Images (HISs) delivers the complete knowledge of imaging; therefore applying a classification algorithm is very important tool for practical uses. The HSIs are always having a large number of correlated and redundant feature, which causes the decrement in the classification accuracy; moreover, the features redundancy come up with some extra burden of computation that without adding any beneficial information to the classification accuracy. In this study, an unsupervised based Band Selection Algorithm (BSA) is considered with the Linear Projection (LP) that depends upon the metric-band similarities. Afterwards Monogenetic Binary Feature (MBF) has consider to perform the ‘texture analysis’ of the HSI, where three operational component represents the monogenetic signal such as; phase, amplitude and orientation. In post processing classification stage, feature-mapping function can provide important information, which help to adopt the Kernel based Neural Network (KNN) to optimize the generalization ability. However, an alternative method of multiclass application can be adopt through KNN, if we consider the multi-output nodes instead of taking single-output node

Efficient multitemporal change detection techniques for hyperspectral images on GPU

Hyperspectral images contain hundreds of reflectance values for each pixel. Detecting regions of change in multiple hyperspectral images of the same scene taken at different times is of widespread interest for a large number of applications. For remote sensing, in particular, a very common application is land-cover analysis. The high dimensionality of the hyperspectral images makes the development of computationally efficient processing schemes critical. This thesis focuses on the development of change detection approaches at object level, based on supervised direct multidate classification, for hyperspectral datasets. The proposed approaches improve the accuracy of current state of the art algorithms and their projection onto Graphics Processing Units (GPUs) allows their execution in real-time scenarios