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

Exploring the use of neural network-based band selection on hyperspectral imagery to identify informative wavelengths for improving classifier task performance

Hyperspectral imagery is a highly dimensional type of data resulting in high computational costs during analysis. Band selection aims to reduce the original hyperspectral image to a smaller subset that reduces these costs while preserving the maximum amount of spectral information within the data. This thesis explores various types of band selection techniques used in hyperspectral image processing. Modifying Neural network-based techniques and observing the effects on the band selection process due to the change in network architecture or objective are of particular focus in this thesis. Herein, a generalized neural network-based band selection technique is developed and compared to state-of-the-art algorithms that are applied to a unique dataset and the Pavia City Center dataset where the subsequent selected bands are fed into a classifier to gather comparison results

A Comparative analysis of spectral band selection techniques

The ability to determine optimal spectral band sets for the exploitation of multispectral and hyperspectral imagery is of great concern due to data transfer, storage, and computational constraints. This study compares the performance of three band selection techniques across a range of scenarios and image exploitation algorithms. Thresholded Divergence, a technique based on Gaussian Maximum Likelihood classification, Forward Sequential Band Selection, an iterative method based on target identification algorithms, and Spectral Basis Functions, a method independent of end-exploitation, were selected for evaluation. Each of these band selection techniques was applied to two M7 multispectral images and two HYDICE hyperspectral images. Each selected optimal spectral band set for each image was classified and assessed for classification accuracy. Comparisons between band selection techniques were made based on spectral band subset size, image exploitation algorithm, image and scene type, and input parameter set

Effect of Denoising in Band Selection for Regression Tasks in Hyperspectral Datasets

This paper presents a comparative analysis of six band selection methods applied to hyperspectral datasets for biophysical variable estimation problems, where the effect of denoising on band selection performance has also been analyzed. In particular, we consider four hyperspectral datasets and three regressors of different nature ("�SVR, Regression Trees, and Kernel Ridge Regression). Results show that the denoising approach improves the band selection quality of all the tested methods. We show that noise filtering is more beneficial for the selection methods that use an estimator based on the whole dataset for the prediction of the output than for methods that use strategies based on local information (neighboring points)

Energy-efficiency improvements for optical access

This article discusses novel approaches to improve energy efficiency of different optical access technologies, including time division multiplexing passive optical network (TDM-PON), time and wavelength division multiplexing PON (TWDM-PON), point-to-point (PTP) access network, wavelength division multiplexing PON (WDM-PON), and orthogonal frequency division multiple access PON (OFDMA-PON). These approaches include cyclic sleep mode, energy-efficient bit interleaving protocol, power reduction at component level, or frequency band selection. Depending on the target optical access technology, one or a combination of different approaches can be applied

Dimensionality reduction using parallel ICA and its implementation on FPGA in hyperspectral image analysis

Hyperspectral images, although providing abundant information of the object, also bring high computational burden to data processing. This thesis studies the challenging problem of dimensionality reduction in Hyperspectral Image (HSI) analysis. Currently, there are two methods to reduce the dimension: band selection and feature extraction. This thesis presents a band selection technique based on Independent Component Analysis (ICA), an unsupervised signal separation algorithm. Given only the observations of hyperspectral images, the ICA –based band selection picks the independent bands which contain most of the spectral information of the original images. Due to the high volume of hyperspectral images, ICA -based band selection is a time consuming process. This thesis develops a parallel ICA algorithm which divides the decorrelation process into internal decorrelation and external decorrelation such that computation burden can be distributed from single processor to multiple processors, and the ICA process can be run in a parallel mode. Hardware implementation is always a faster and real -time solution to HSI analysis. Until now, there are few hardware designs for ICA -related processes. This thesis synthesizes the parallel ICA -based band selection on Field Programmable Gate Array (FPGA), which is the best choice for moderate designs and fast implementations. Compared to other design syntheses, the synthesis present in this thesis develops three ICA re-configurable components for the purpose of reusability. In addition, this thesis demonstrates the relationship between the design and the capacity utilization of a single FPGA, then discusses the features of High Performance Reconfigurable Computing (HPRC) to accomodate large capacity and design requirements. Experiments are conducted on three data sets obtained from different sources. Experimental results show the effectiveness of the proposed ICA -based band selection, parallel ICA and its synthesis on FPGA

Novel gumbel-softmax trick enabled concrete autoencoder with entropy constraints for unsupervised hyperspectral band selection.

As an important topic in hyperspectral image (HSI) analysis, band selection has attracted increasing attention in the last two decades for dimensionality reduction in HSI. With the great success of deep learning (DL)-based models recently, a robust unsupervised band selection (UBS) neural network is highly desired, particularly due to the lack of sufficient ground truth information to train the DL networks. Existing DL models for band selection either depend on the class label information or have unstable results via ranking the learned weights. To tackle these challenging issues, in this article, we propose a Gumbel-Softmax (GS) trick enabled concrete autoencoder-based UBS framework (CAE-UBS) for HSI, in which the learning process is featured by the introduced concrete random variables and the reconstruction loss. By searching from the generated potential band selection candidates from the concrete encoder, the optimal band subset can be selected based on an information entropy (IE) criterion. The idea of the CAE-UBS is quite straightforward, which does not rely on any complicated strategies or metrics. The robust performance on four publicly available datasets has validated the superiority of our CAE-UBS framework in the classification of the HSIs