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

A novel band selection and spatial noise reduction method for hyperspectral image classification.

As an essential reprocessing method, dimensionality reduction (DR) can reduce the data redundancy and improve the performance of hyperspectral image (HSI) classification. A novel unsupervised DR framework with feature interpretability, which integrates both band selection (BS) and spatial noise reduction method, is proposed to extract low-dimensional spectral-spatial features of HSI. We proposed a new Neighboring band Grouping and Normalized Matching Filter (NGNMF) for BS, which can reduce the data dimension whilst preserve the corresponding spectral information. An enhanced 2-D singular spectrum analysis (E2DSSA) method is also proposed to extract the spatial context and structural information from each selected band, aiming to decrease the intra-class variability and reduce the effect of noise in the spatial domain. The support vector machine (SVM) classifier is used to evaluate the effectiveness of the extracted spectral-spatial low-dimensional features. Experimental results on three publicly available HSI datasets have fully demonstrated the efficacy of the proposed NGNMF-E2DSSA method, which has surpassed a number of state-of-the-art DR methods

Remote sensing satellite image processing techniques for image classification: a comprehensive survey

This paper is a brief survey of advance technological aspects of Digital Image Processing which are applied to remote sensing images obtained from various satellite sensors. In remote sensing, the image processing techniques can be categories in to four main processing stages: Image preprocessing, Enhancement, Transformation and Classification. Image pre-processing is the initial processing which deals with correcting radiometric distortions, atmospheric distortion and geometric distortions present in the raw image data. Enhancement techniques are applied to preprocessed data in order to effectively display the image for visual interpretation. It includes techniques to effectively distinguish surface features for visual interpretation. Transformation aims to identify particular feature of earth’s surface and classification is a process of grouping the pixels, that produces effective thematic map of particular land use and land cover

MIMN-DPP: Maximum-information and minimum-noise determinantal point processes for unsupervised hyperspectral band selection

Band selection plays an important role in hyperspectral imaging for reducing the data and improving the efficiency of data acquisition and analysis whilst significantly lowering the cost of the imaging system. Without the category labels, it is challenging to select an effective and low-redundancy band subset. In this paper, a new unsupervised band selection algorithm is proposed based on a new band search criterion and an improved Determinantal Point Processes (DPP). First, to preserve the original information of hyperspectral image, a novel band search criterion is designed for searching the bands with high information entropy and low noise. Unfortunately, finding the optimal solution based on the search criteria to select a low-redundancy band subset is a NP-hard problem. To solve this problem, we consider the correlation of bands from both original hyperspectral image and its spatial information to construct a double-graph model to describe the relationship between spectral bands. Besides, an improved DPP algorithm is proposed for the approximate search of a low-redundancy band subset from the double-graph model. Experiment results on several well-known datasets show that the proposed optical band selection algorithm achieves better performance than many other 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

Non-Parametric Spatial Spectral Band Selection methods

© Cranfield University 2021. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright ownerThis project is about the development of band selection (BS) techniques for better target detection and classification in remote sensing and hyperspectral imaging (HSI). Conventionally, this is achieved just by using the spectral features for guiding the band compression. However, this project develops a BS method which uses both spatial and spectral features to allow a handful of crucial spectral bands to be selected for enhancing the target detection and classification performances. This thesis firstly outlines the fundamental concepts and background of remote sensing and HSI, followed by the theories of different atmospheric correction algorithms — in order to assess the reflectance conversion for band selection — and BS techniques, with a detailed explanation of the Hughes principle, which postulates the fundamental drawback for having high-dimensional data in HSI. Subsequently, the thesis highlights the performances of some advanced BS techniques and to point out their deficiencies. Most of the existing BS work in the field have exhibited maximal classification accuracy when more spectral bands have been utilized for classification; this apparently disagrees with the theoretical model of the Hughes phenomenon. The thesis then presents a spatial spectral mutual information (SSMI) BS scheme which utilizes a spatial feature extraction technique as a pre-processing step, followed by the clustering of the mutual information (MI) of spectral bands for enhancing the BS efficiency. Through this BS scheme, a sharp ’bell’-shaped accuracy-dimensionality characteristic has been observed, peaking at about 20 bands. The performance of the proposed SSMI BS scheme has been validated through 6 HSI datasets, and its classification accuracy is shown to be ~10% better than 7 state-of-the-art BS algorithms. These results confirm that the high efficiency of the BS scheme is essentially important to observe, and to validate, the Hughes phenomenon at band selection through experiments for the first time.PH

Image-based Semantic Segmentation of Large-scale Terrestrial Laser Scanning Point Clouds

Large-scale point cloud data acquired using terrestrial laser scanning (TLS) often need to be semantically segmented to support many applications. To this end, various three-dimensional (3D) methods and two-dimensional (i.e., image-based) methods have been developed. For large-scale point cloud data, 3D methods often require extensive computational effort. In contrast, image-based methods are favourable from the perspective of computational efficiency. However, the semantic segmentation accuracy achieved by existing image-based methods is significantly lower than that achieved by 3D methods. On this basis, the aim of this PhD thesis is to improve the accuracy of image-based semantic segmentation methods for TLS point cloud data while maintaining its relatively high efficiency. In this thesis, the optimal combination of commonly used features was first found, and an efficient manual feature selection method was proposed. It was found that existing image-based methods are highly dependent on colour information and do not provide an effective means of representing and utilising geometric features of scenes in images. To address this problem, an image enhancement method was developed to reveal the local geometric features in images derived by the projection of point cloud coordinates. Subsequently, to better utilise neural network models that are pre-trained on three-channel (i.e., RGB) image datasets, a feature extraction method (LC-Net) and a feature selection method (OSTA) were developed to reduce the higher dimension of image-based features to three. Finally, a stacking-based semantic segmentation (SBSS) framework was developed to further improve segmentation accuracy. By integrating SBSS, the dimension-reduction method (i.e. OSTA) and locally enhanced geometric features, a mean Intersection over Union (mIoU) of 76.6% and an Overall Accuracy (OA) of 93.8% were achieved on the Semantic3D (Reduced-8) benchmark. This set the state-of-the-art (SOTA) for the semantic segmentation accuracy of image-based methods and is very close to the SOTA accuracy of 3D method (i.e., 77.8% mIoU and 94.3% OA). Meanwhile, the integrated method took less than 10% of the processing time (52.64s versus 563.6s) of the fastest SOTA 3D method