Unsupervised Segmentation Of Hyperspectral Images Research Articles

Unsupervised hyperspectral image segmentation of films: a hierarchical clustering-based approach

Hyperspectral imaging (HSI) has been drastically applied in recent years to cultural heritage (CH) analysis, conservation, and also digital restoration. However, the efficient processing of the large datasets registered remains challenging and still in development. In this paper, we propose to use the hierarchical clustering algorithm (HCA) as an alternative machine learning approach to the most common practices, such as principal component analysis(PCA). HCA has shown its potential in the past decades for spectral data classification and segmentation in many other fields, maximizing the information to be extracted from the high-dimensional spectral dataset via the formation of the agglomerative hierarchical tree. However, to date, there has been very limited implementation of HCA in the field of cultural heritage. Data used in this experiment were acquired on real historic film samples with various degradation degrees, using a custom-made push-broom VNIR hyperspectral camera (380–780nm). With the proposed HCA workflow, multiple samples in the entire dataset were processed simultaneously and the degradation areas with distinctive characteristics were successfully segmented into clusters with various hierarchies. A range of algorithmic parameters was tested, including the grid sizes, metrics, and agglomeration methods, and the best combinations were proposed at the end. This novel application of the semi-automating and unsupervised HCA could provide a basis for future digital unfading, and show the potential to solve other CH problems such as pigment mapping.

Deep Spectral Clustering With Regularized Linear Embedding for Hyperspectral Image Clustering

The past decade has witnessed the rapid development of deep learning techniques, especially for large-scale and complex data sets. However, it is still a noteworthy problem in dealing with unsupervised hyperspectral image segmentation since inefficiency and misleading result from the absence of supervised information. Generally, spectral clustering is one of the most powerful clustering algorithms, as it often outperforms other methods for image segmentation. Unfortunately, the poor scalability and generalization severely limit the use of spectral clustering, especially for large-scale and high-dimensional hyperspectral images processing. The major motivation of this work is to solve this problem, and we designed a novel algorithm, termed Deep Spectral Clustering with Regularized Linear Embedding (DSCRLE), to benefit from both spectral graph theory and deep learning techniques. The brief procedure is first to construct a fully connected neural network to extract latent feature representations, and then normalize the feature representations by the spectral orthonormal constraint. Lastly, by introducing low-dimensional embedding, we refined the final outputs of all given unlabeled hyperspectral pixels. Extensive experiments have demonstrated that the competitiveness of the proposed method, and it outperforms state-of-art clustering approaches in the task of hyperspectral image segmentation.

Novel Spectral Loss Function for Unsupervised Hyperspectral Image Segmentation

Neural networks (NNs) have gained importance in hyperspectral image (HSI) segmentation for earth observation due to its unparalleled data-driven feature extraction capability. However, in many real-life situations, ground truth is not available, and the performance of unsupervised NNs is still susceptible to enhancement. To overcome this challenge, this letter presents a new loss function to improve the performance of unsupervised HSI segmentation models. The spectral loss function, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sl</i> , which can be included in different models, is based on the purity of the unmixing endmembers and the spectral similarity of the clusters provided by the NN to determine the classes. It is incorporated into a 3D convolutional autoencoder to validate its performance on four standard HSI benchmarks. Furthermore, its performance has been qualitatively examined in a real case study, an oil spill without ground truth. The results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sl</i> is a breakthrough in unsupervised HS segmentation, obtaining the best overall performance and highlighting the importance of spectral signatures. Additionally, dimensional reduction is also vital in compacting the spectral information, which facilitates its segmentation. Source code available at [https://github.com/mhaut/HSI-3DSpLoss].

Simultaneous band selection and segmentation of hyperspectral images via a mixture of finite maximum margin mixtures

ABSTRACT Unsupervised segmentation of hyperspectral images is an important problem in the field of image processing with many applications, including scene labelling of remote-sensing images and object detection in medical images. However, the high-dimensionality of hyperspectral images poses considerable challenges in this problem. In previous studies, to deal with high-dimensionality, band selection has been performed as a separate step before image segmentation. In this approach, however, information loss can occur because band selection methods do not consider the segmentation task when they select the bands. In addition, in most segmentation tasks, the fact that the number of segments of the image needs to be given before the segmentation task is also an issue. To address these challenges, we propose a method for simultaneous segmentation of hyperspectral images and selection of important bands that are highly involved in the segmentation task. The proposed method, called a mixture of finite maximum margin mixtures, combines a mixture of finite mixtures, which allows the number of segments to be automatically inferred from the data, and a maximum margin classifier, which allows a flexible segmentation and tractable inference on the segmentation labels. Furthermore, we simultaneously select useful bands that help with the segmentation during the segmentation procedure by introducing a latent binary vector that represents discriminating or non-discriminating bands. We demonstrate the advantages of the proposed method using four real datasets.

Unsupervised segmentation of LiDAR fused hyperspectral imagery using pointwise mutual information

ABSTRACT In the segmentation of hyperspectral images (HSI), difficulties arise when different objects with similar spectral characteristics are being distinguished. If these objects with similar spectral information have different altitudes, it is possible to partition them based on the elevation information which can be obtained with a light detection and ranging (LiDAR) sensor. In this study, we propose a new affinity matrix to be used in a spectral clustering (SC) framework for the unsupervised segmentation of HSI and LiDAR data. To compose this new affinity matrix, spatial-spectral information obtained from HSI and elevation information obtained from LiDAR are combined using Pointwise Mutual Information (PMI). PMI is a measure of how much one pixel tells about the others in an image. It relies on the fact that the pixels of the same object in the given scene have a higher statistical dependence than the pixels of different objects. Hence, segmenting HSI and LiDAR data using PMI can provide a more comprehensive interpretation of the objects in images. The experimental results on two different real data sets show that the proposed method is very effective for unsupervised segmentation of HSI and LiDAR data and it is much faster when compared to competing spectral clustering algorithms.

Unsupervised Segmentation of Hyperspectral Images through Evolved Cellular Automata

The problem of segmenting multidimensional images, in particular hyperspectral images, is still an open subject. The main issue is related to preserving the multidimensional character of the signals throughout the segmentation process avoiding an early projection onto a 2D plane with the consequent loss of the wealth of information these images provide. The approach followed here is based on the use of cellular automata (CA) and their emergent behavior over the hyperspectral cube in order to achieve this objective. Using cellular automata for segmentation in hyperspectral images is not new, but most approaches to this problem involve hand designing the rules for the automata. Additionally, most references found are just extensions of one or three-dimensional methods to multidimensional images, and, as a consequence, average out the spectral information present. The main contributions of this paper is the study of the application of evolutionary methods to produce the CA rule sets that result in the best possible segmentation properties under different circumstances without resorting to any form of projection until the information is presented to the user. The procedure has been tested over synthetic and real hyperspectral images.

Segmentation of Hyperspectral Images via Subtractive Clustering and Cluster Validation Using One-Class Support Vector Machines

This paper presents an unsupervised hyperspectral image segmentation with a new subtractive-clustering-based similarity segmentation and a novel cluster validation method using one-class support vector (SV) machine (OC-SVM). An estimation of the correct number of clusters is an important task in hyperspectral image segmentation. The proposed cluster validity measure is based on the power of spectral discrimination (PWSD) measure and utilizes the advantage of the inherited cluster contour definition feature of OC-SVM. Hence, this novel cluster validity method is referred to as SV-PWSD. SVs found by OC-SVM are located at the minimum distance to the hyperplane in the feature space and at the arbitrarily shaped cluster contours in the input space. SV-PWSD guides the segmentation/clustering process to find the optimal number of clusters in hyperspectral data. Because of the high computational load of subtractive clustering and OC-SVM, a subset of the image (only ground-truth data) is initially used in the clustering and validation phases. Then, it is proposed to use <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -nearest neighbor classification, with the already clustered subset being used as training data, to project the initial clustering results onto the entire data set.

Two lattice computing approaches for the unsupervised segmentation of hyperspectral images

Endmembers for the spectral unmixing analysis of hyperspectral images are sets of affinely independent vectors, which define a convex polytope covering the data points that represent the pixel image spectra. Strong lattice independence (SLI) is a property defined in the context of lattice associative memories convergence analysis. Recent results show that SLI implies affine independence, confirming the value of lattice associative memories for the study of endmember induction algorithms. In fact, SLI vector sets can be easily deduced from the vectors composing the lattice auto-associative memories (LAM). However, the number of candidate endmembers found by this algorithm is very large, so that some selection algorithm is needed to obtain the full benefits of the approach. In this paper we explore the unsupervised segmentation of hyperspectral images based on the abundance images computed, first, by an endmember selection algorithm and, second, by a previously proposed heuristically defined algorithm. We find their results comparable on a qualitative basis.