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

Unsupervised spectral sub-feature learning for hyperspectral image classification

Spectral pixel classification is one of the principal techniques used in hyperspectral image (HSI) analysis. In this article, we propose an unsupervised feature learning method for classification of hyperspectral images. The proposed method learns a dictionary of sub-feature basis representations from the spectral domain, which allows effective use of the correlated spectral data. The learned dictionary is then used in encoding convolutional samples from the hyperspectral input pixels to an expanded but sparse feature space. Expanded hyperspectral feature representations enable linear separation between object classes present in an image. To evaluate the proposed method, we performed experiments on several commonly used HSI data sets acquired at different locations and by different sensors. Our experimental results show that the proposed method outperforms other pixel-wise classification methods that make use of unsupervised feature extraction approaches. Additionally, even though our approach does not use any prior knowledge, or labelled training data to learn features, it yields either advantageous, or comparable, results in terms of classification accuracy with respect to recent semi-supervised methods

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

GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection

Change detection (CD) is an important application of remote sensing, which provides timely change information about large-scale Earth surface. With the emergence of hyperspectral imagery, CD technology has been greatly promoted, as hyperspectral data with the highspectral resolution are capable of detecting finer changes than using the traditional multispectral imagery. Nevertheless, the high dimension of hyperspectral data makes it difficult to implement traditional CD algorithms. Besides, endmember abundance information at subpixel level is often not fully utilized. In order to better handle high dimension problem and explore abundance information, this paper presents a General End-to-end Two-dimensional CNN (GETNET) framework for hyperspectral image change detection (HSI-CD). The main contributions of this work are threefold: 1) Mixed-affinity matrix that integrates subpixel representation is introduced to mine more cross-channel gradient features and fuse multi-source information; 2) 2-D CNN is designed to learn the discriminative features effectively from multi-source data at a higher level and enhance the generalization ability of the proposed CD algorithm; 3) A new HSI-CD data set is designed for the objective comparison of different methods. Experimental results on real hyperspectral data sets demonstrate the proposed method outperforms most of the state-of-the-arts