Efficient mapping of lava flow fields during volcanic eruptions is crucial for hazard mitigation. Here we present a machine learning approach to classify the lava flow field of the 2021 Fagradalsfjall eruption on the Reykjanes Peninsula, Iceland using orthomosaics and layers derived from digital elevation models. A supervised classification method achieves a 96.7% accuracy against the manually digitized outline of the lava flow field after post-processing. The addition of an unsupervised clustering step yields a 99.8% accuracy. These results are a stepping stone for automated monitoring of lava flows on the Reykjanes Peninsula, which can improve the efficiency of hazard assessment.

The Sentinel-2 (S2) satellite constellation provides images at three different spatial resolutions and model based superresolution methods have proved useful for sharpening them to their maximum resolution. Algorithm unrolling is a way of building efficient, interpretable neural networks by reimplementing traditional algorithms in a neural network context. In this paper, an unrolled model based method to superresolve S2 images is proposed and unsupervised single image training is performed using reduced scale data. The method is evaluated using both real and simulated data.

Hyperspectral image (HSI) denoising methods that are implemented using deep learning frameworks rarely consider the intrinsic characteristics of HSIs, and often lack both physical interpretability, and generalization. In this paper, a low-rank and sparse model-based unrolled network for HSI de-noising, termed LRS-Net, is proposed. The method unrolls a model-based denoising method into a deep-unrolled network. The network is much faster than the previous method and is also able to automatically select the tuning parameters. The method inherits the advantages of model-based methods, i.e., physical interpretability and generalization, and also advantages from deep learning based methods, i.e., computational efficiency and data-based learning capabilities. Using both simulated and real HSIs it is shown the proposed method can outperform other comparative methods, both in quantitative and visual assessments.

This paper proposes a new spectral-spatial kernel minimum noise fraction transformation (SS-KMNF) as a dimensionality reduction method for hyperspectral image (HSI) classification. The kernel minimum noise fraction (KMNF) method generates new components ordered by image quality and the key index of image quality is noise fraction. In SS-KMNF, the high correlation between bands in homogeneous regions obtained using a superpixel technique is applied to improve the precision of noise fraction. Compared with the original KMNF, SS-KMNF can fully use spectral and spatial information contained in a HSI and is more effective in enhancing the performance of dimensionality reduction for HSI classification. Moreover, a new classification strategy is proposed based on superpixels which are considered as the basic unit instead of pixels for classifying features extracted by SS-KMNF. Experimental results show that SS-KMNF can get better dimensionality reduction performance than KMNF, and the superpixel is not only beneficial to precisely estimating noise fraction but can also increase the classification accuracy of features extracted by SS-KMNF. In addition, SS-KMNF can also be applied to classify transmission lines in electrical power systems.

Spectral and spatial correlation in hyperspectral images (HSIs) can be exploited in HSI processing because it directly induces a sparse and low-rank prior via linear transformations. Researchers have used the sparse and low-rank prior as an image prior for HSI restoration, such as denoising, deblurring, and super-resolution. This paper proposes a HSI denoising method that incorporates a sparse and low-rank prior with a deep image prior (DIP). The sparse and low-rank prior is obtained using the 2-dimensional discrete wavelet transform (2-D DWT), and singular value decomposition (SVD), while the DIP is provided by the structure of a convolutional neural network (CNN). The combination of a sparse and low-rank prior with a DIP views the CNN-based denoising method similar to a model-based method, inheriting the advantages of both model-based and CNN-based methods. Experimental results with simulated and real HSI datasets show that the proposed method outperforms the conventional sparse and low-rank based methods in both quantitative and qualitative performance. Codes are available at https://github.com/hvn2/DIP-SLR

In recent years numerous model-based methods for super-resolution of Sentinel-2 (S2) multispectral images have been suggested. Super-resolution aims to enhance the resolution of a captured image by upscaling and enhancing the details. The performance of model-based methods relies on carefully selecting regularizers and tuning parameters. This paper investigates whether using Wald's protocol, i.e., selecting tuning parameters at reduced-resolution, translates to a good performance at a full-scale. To investigate this, we use the recently proposed S2Sharp method and show that selecting its tuning parameters using Wald's protocol improves its performance.

Sentinel-2 (S2) is a constellation of two satellites that frequently acquire optical imagery over land and coastal waters. The S2 sensors have three spatial resolutions: 10, 20, and 60 m. Many remote sensing applications require the spatial resolution to be at the highest resolution, i.e., 10 m for S2. To address this demand, researchers have proposed various methods that exploit the spectral and spatial correlation in multispectral data to sharpen the S2 bands to 10 m. In this paper, we fuse S2 data with high-resolution aerial RGB images. A method called S2Sharp is modified to include the red, green, and blue bands of the aerial image and sharpen S2 data to the resolution of the RGB image. The method, termed S2PF, is evaluated using an S2 image and aerial photographs of Reykjavik, Iceland.

Hyperspectral images (HSI) are composed of hundreds of spectral bands, covering a broad range of the electromagnetic spectrum. However, images can only be visualized using three spectral channels for red, green, and blue (RGB) colors. Generating realistic RGB images using HSI is seldom the main focus of remote sensing researchers, and is therefore sometimes lacking. In this paper, we present an algorithm which creates realistic color images of HSI, using standardized methods. Research, conducted on the human perception of color in the 1920s culminated in the CIE 1931 XYZ color space. The algorithm maps every spectral band in the visible spectrum to the XYZ color space, using D65 as the reference illuminant, and then maps the XYZ to the sRGB (standard Red Green Blue) color space. The image is gamma-corrected and finally thresholded to improve contrast. The method was validated using two HSIs, creating realistic color images.

Sentinel-2 (S2) satellite constellations can provide multispectral images of 10 m, 20 m, and 60 m resolution for visible, near-infrared (NIR) and short-wave infrared (SWIR) in the electromagnetic spectrum. In this paper, we present a sharpening method based on a symmetric skipped connection convolutional neural network, called SSC-CNN, to sharpen 20 m bands using 10 m bands. The main advantage of SSC-CNN architecture is that it brings the features of the input branch to the output, thus improving convergence without using too many deep layers. The proposed method uses the reduced-scale combination of 10 m bands and 20 m bands, and the observed 20 m bands as the training pairs. The experimental results using two Sentinel-2 datasets show that our method outperforms competitive methods in quantitative metrics and visualization.

Blind hyperspectral unmixing is the process of decomposing hyperspectral images (HSIs) into pure material spectra (endmembers) and abundances. In this paper, we examine scaling the pixels of the HSI inversely proportional to their ℓ2norm, controlled with a tuning parameter. We promote sparse abundances using an ℓqpenalty and softly enforce the abundance sum constraint using matrix augmentation. The minimization problem is solved using a variant of sparse nonnegative matrix factorization (NMF) and all tuning parameters are selected using Bayesian optimization. The proposed method is evaluated using two real hyperspectral images.

Hyperspectral unmixing consists of estimating pure material spectra (endmembers) and their corresponding abundances in hyperspectral images. In this paper, a blind nonlinear hyperspectral unmixing algorithm is presented. The algorithm promotes sparse abundance maps using an lq regularizer and assumes that the spectra are mixed according to an extension to generalized bilinear model, called the Fan model. The algorithm is evaluated using both simulated and real hyperspectral data.

Blind hyperspectral unmixing (HU) is the task of jointly estimating the spectral signatures of materials and abundances in hyperspectral images. Most unmixing algorithms assume the linear mixture model, however, nonlinear models have recently gained interest, as they represent more complicated scenes. This letter proposes two blind nonlinear HU algorithms. The former algorithm assumes that the spectra are mixed according to the generalized bilinear model, while the latter assumes an extension to this model, called the Fan model. Both the algorithms use the ℓqregularizer to promote sparse abundances and solve the minimization problems using cyclic descent. The algorithms are evaluated and compared with other unmixing algorithms using both simulated and real data.

Hyperspectral unmixing is a challenging inverse problem that involves determining the fractional abundances of the representive material (endmembers) in each pixel. In this paper, we develop a neural network autoencoder, that dynamically exploits the sparsity of the abundances and enforces the abundance sum constraint (ASC) for hyperspectral unmixing. Instead of using the conventional mean square error (MSE) objective function, we use the spectral information divergence (SID) measure. Experiments are performed using a real hyperspectral dataset and we compare results obtained using both MSE and SID. It is demonstrated by qualitative inspection that using SID gives significantly better results than using MSE.

In this paper, we present a fast blind multitemporal hyperspectral unmixing algorithm, using an l1penalty to promote sparse abundances. The method is able to account for different acquisition conditions of multitemporal images, by allowing the spectral signatures in the different temporal images to vary. The new algorithm is tested on simulated data and applied on real hyperspectral data.

In this paper, hyperspectral data is modeled as a combination of a sparse component, a low rank component and noise. The low rank component is a product of the endmembers and the abundances in an image, and the sparse component is composed of outliers and structured noise. Outliers and structured noise in this context are, e.g. band specific noise, vertical or horizontal artifacts or saturated pixels. Sparse and low rank matrix decomposition (SLR) is a method that has recently been developed for estimating those components. Here, an algorithm based on ℓ1SLR is developed using sparse blind hyperspectral unmixing and soft thresholding. The number of endmembers and the sparsity parameters are selected using the extended Bayesian information criterion (EBIC). The proposed algorithm is evaluated using a real remote sensing hyperspectral image.

Blind hyperspectral unmixing jointly estimates spectral signatures and abundances in hyperspectral images (HSIs). Hyperspectral unmixing is a powerful tool for analyzing hyperspectral data. However, the usual huge size of HSIs may raise difficulties for classical unmixing algorithms, namely, due to limitations of the hardware used. Therefore, some researchers have considered distributed algorithms. In this paper, we develop a distributed hyperspectral unmixing algorithm that uses the alternating direction method of multipliers and ℓ1sparse regularization. The hyperspectral unmixing problem is split into a number of smaller subproblems that are individually solved, and then the solutions are combined. A key feature of the proposed algorithm is that each subproblem does not need to have access to the whole HSI. The algorithm may also be applied to multitemporal HSIs with due adaptations accounting for variability that often appears in multitemporal images. The effectiveness of the proposed algorithm is evaluated using both simulated data and real HSIs.

Non-negative matrix factorization (NMF) has found use in fields such as remote sensing and computer vision where the signals of interest are usually non-negative. Data dimensions in these applications can be huge and traditional algorithms break down due to unachievable memory demands. One is then compelled to consider distributed algorithms. In this paper, we develop for the first time a distributed version of NMF using the alternating direction method of multipliers (ADMM) algorithm and dyadic cyclic descent. The algorithm is compared to well established variants of NMF using simulated data, and is also evaluated using real remote sensing hyperspectral data.

Blind hyperspectral unmixing is the task of jointly estimating the spectral signatures of material in a hyperspectral images and their abundances at each pixel. The size of hyperspectral images are usually very large, which may raise difficulties for classical optimization algorithms, due to limited memory of the hardware used. One solution to this problem is to consider distributed algorithms. In this paper, we develop a distributed sparse hyperspectral unmixing algorithm using the alternating direction method of multipliers (ADMM) algorithm and ℓ1sparse regularization. Each sub-problem does not need to have access to the whole hyperspectral image. The algorithm is evaluated using a very large real hyperspectral image.

Big data applications, such as medical imaging and genetics, typically generate datasets that consist of few observations n on many more variables p, a scenario that we denote asp ≫ n. Traditional data processing methods are often insufficient for extracting information out of big data. This calls for the development of new algorithms that can deal with the size, complexity, and the special structure of such datasets. In this paper, we consider the problem of classifying p ≫ n data and propose a classification method based on linear discriminant analysis (LDA). Traditional LDA depends on the covariance estimate of the data, but when p ≫ n, the sample covariance estimate is singular. The proposed method estimates the covariance by using a sparse version of noisy principal component analysis (nPCA). The use of sparsity in this setting aims at automatically selecting variables that are relevant for classification. In experiments, the new method is compared to state-of-the art methods for big data problems using both simulated datasets and imaging genetics datasets.

Blind hyperspectral unmixing involves jointly estimating endmembers and fractional abundances in hyperspectral images. An endmember is the spectral signature of a specific material in an image, while an abundance map specifies the amount of a material seen in each pixel in an image. In this paper, a new cyclic descent algorithm for blind hyperspectral unmixing using total variation (TV) and ℓq sparse regularization is proposed. Abundance maps are both spatially smooth and sparse. Their sparsity derives from the fact that each material in the image is not represented in all pixels. The abundance maps are assumed to be piecewise smooth since adjacent pixels in natural images tend to be composed of similar material. The TV regularizer is used to encourage piecewise smooth images, and the ℓqregularizer promotes sparsity. The dyadic expansion decouples the problem, making a cyclic descent procedure possible, where one abundance map is estimated, followed by the estimation of one endmember. A novel debiasing technique is also employed to reduce the bias of the algorithm. The algorithm is evaluated using both simulated and real hyperspectral images.

Blind hyperspectral unmixing jointly estimates both the endmembers and the abundances of hyperspectral images. The endmembers represent the spectral signatures of material found in the image and the abundances specify the amount of each material seen in each pixel in the image. In this paper, a blind hyperspectral unmixing method for feature extraction and classification using total variation (TV) and ℓqsparse regularization is proposed. The abundances found are used as features for classification. The classification results are compared to results obtained using Principal Component analysis (PCA) and also to results obtained using hyperspectral unmixing using only TV and sparsity, respectively.

In this paper, an effective method is proposed that combines supervised and unsupervised unmixing. We assume a linear model for the hyperspectral data and incorporate information about endmembers that are known to be in the data into the model. This information can be acquired from a spectral library or extracted from the data. Utilizing a priori information can both improve the unmixing, and reduce the complexity of the problem. The method is quantitatively evaluated using simulated data and it is shown that the unmixing results improve and the computational time decreases when a priori information is used. The method is also applied on a real hyperspectral data set of an urban landscape. The estimated abundance maps improve when information about known endmembers is incorporated into the model.

Hyperspectral unmixing is an important technique for analyzing remote sensing images. In this paper, we consider and examine the ℓq, 0 ≤ q ≤ 1 penalty on the abundances for promoting sparse unmixing of hyperspectral data. We also apply a first-order roughness penalty to promote piecewise smooth end-members. A novel iterative algorithm for simultaneously estimating the end-members and the abundances is developed and tested both on simulated and two real hyperspectral data sets. We present an extensive simulation study where we vary both the SNR and the sparsity of the simulated data and identify the model parameters that minimize the reconstruction errors and the spectral angle distance. We show that choosing 0 ≤ q1penalty when the SNR is low or the sparsity of the underlying model is high. We also examine the effects of the imposing the abundance sum constraint using a real hyperspectral data set.

We propose CUR methods for hyperspectral unmixing that decompose the data matrix into non-negative endmembers and abundance maps. The endmembers will be selected from a dictionary constructed from the data matrix. Each endmember will coincide with certain columns of the data matrix. By doing this we are assured that the dictionary will be physically meaningful and may be interpreted unambiguously from the data set. This assumption, that the endmembers are contained within the data, is called the pixel purity assumption. We compare two regularization terms to promote sparsity in our solutions, the first is ℓ2regularization and the second is vector ℓ0regularization. The methods are evaluated both on simulated data and a real hyperspectral image of an urban landscape.

Hyperspectral unmixing is the task of decomposing hyperspectral images into endmembers and their abundances. The endmembers are spectral signatures of specific material in the image and the abundances dictate the amount of the material found in each pixel. In this paper we present a blind signal separation method, based on the total variation penalty, that simultaneously estimates the endmembers and the abundances. We evaluate our method using both simulated and a real data set.

Hyperspectral unmixing is the process where the reflectance spectrum from a mixed pixel is decomposed into separate distinct spectral signatures (endmembers). A mixed pixel results when spectra from more than one material is recorded by a sensor in one pixel. The goal of linear unmixing is to identify the number of endmembers in an image, the endmembers themselves and their abundances in each pixel. This paper presents a new smooth method for unmixing hyperspectral images using nonnegative cyclic descent. The proposed method uses iterative cyclic descent algorithm to find the endmembers and their abundances. The algorithm uses an ℒ1norm to promote sparseness in the abundances. Because the spectrum of the endmembers varies smoothly, a first order roughness penalty is added to discourage roughness in the endmembers. The algorithm does not use any prior information about the data. The method is tested using a real hyperspectral image of an urban landscape.