Unsupervised Band Selection in Hyperspectral Images using Autoencoder

International audienceHyperspectral images provide fine details of the observed scene from the exploitation of contiguous spectral bands. However, the high dimensionality of hyperspectral images causes a heavy burden on processing. Therefore, a common practice that has been largely adopted is the selection of bands before processing. Thus, in this work, a new unsupervised approach for band selection based on autoencoders is proposed. During the training phase of the autoencoder, the input data samples have some of their features turned to zero, through a masking noise transform. The subsequent reconstruction error is assigned to the indices with masking noise. The bigger the error, the greater the importance of the masked features. The errors are then summed up during the whole training phase. At the end, the bands corresponding to the biggest indices are selected. A comparison with four other band selection approaches reveals that the proposed method yields better results in some specific cases and similar results in other situations

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

Dimensionality reduction based on determinantal point process and singular spectrum analysis for hyperspectral images

Dimensionality reduction is of high importance in hyperspectral data processing, which can effectively reduce the data redundancy and computation time for improved classification accuracy. Band selection and feature extraction methods are two widely used dimensionality reduction techniques. By integrating the advantages of the band selection and feature extraction, the authors propose a new method for reducing the dimension of hyperspectral image data. First, a new and fast band selection algorithm is proposed for hyperspectral images based on an improved determinantal point process (DPP). To reduce the amount of calculation, the dual-DPP is used for fast sampling representative pixels, followed by k-nearest neighbour-based local processing to explore more spatial information. These representative pixel points are used to construct multiple adjacency matrices to describe the correlation between bands based on mutual information. To further improve the classification accuracy, two-dimensional singular spectrum analysis is used for feature extraction from the selected bands. Experiments show that the proposed method can select a low-redundancy and representative band subset, where both data dimension and computation time can be reduced. Furthermore, it also shows that the proposed dimensionality reduction algorithm outperforms a number of state-of-the-art methods in terms of classification accuracy

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

Application-Dependent Wavelength Selection For Hyperspectral Imaging Technologies

Hyperspectral imaging has proven to provide benefits in numerous application domains, including agriculture, biomedicine, remote sensing, and food quality management. Unlike standard color imagery composed of these broad wavelength bands, hyperspectral images are collected over numerous (possibly hundreds) of narrow wavelength bands, thereby offering vastly more information content than standard imagery. It is this higher information content which enables improved performance in complex classification and regression tasks. However, this successful technology is not without its disadvantages which include high cost, slow data capture, high data storage requirements, and computational complexity. This research seeks to overcome these disadvantages through the development of algorithms and methods to enable the benefits of hyperspectral imaging in inexpensive portable devices that collect spectral data at only a handful (i.e., 5-7) of wavelengths specifically selected for the application of interest.This dissertation focuses on two applications of practical interest: fish fillet species classification for the prevention of food fraud and tissue oxygenation estimation for wound monitoring. Genetic algorithm, self-organizing map, and simulated annealing approaches for wavelength selection are investigated for the first application, combined with common machine learning classifiers for species classification. The simulated annealing approach for wavelength selection is carried over to the wound monitoring application and is combined with the Extended Modified Lambert-Beer law, a tissue oxygenation method that has proven to be robust to differences in melanin concentrations. Analyses for this second application included spectral convolutions to represent data collection with the envisioned inexpensive portable devices. Results of this research showed that high species classification accuracy (\u3e 90%) and low tissue oxygenation error (\u3c 1%) is achievable with just 5-7 selected wavelengths. Furthermore, the proposed wavelength selection and estimation algorithms for the wound monitoring application were found to be robust to variations in the peak wavelength and relatively wide bandwidths of the types of LEDs that may be featured in the designs of such devices