nils-herrmann/clean_scirepeval huggingface dataset:
Spectral Unmixing for the Classification of Hyperspectral Images at a Finer Spatial Resolution

The problem of classification of hyperspectral images containing mixed pixels is addressed. Hyperspectral imaging is a continuously growing area of remote sensing applications. The wide spectral range of such imagery, providing a very high spectral resolution, allows to detect and classify surfaces and chemical elements of the observed image. The main problem of hyperspectral data is the (relatively) low spatial resolution, which can vary from a few to tens of meters. Many factors make the spatial resolution one of the most expensive and hardest to improve in imaging systems. For classification, the major problem caused by low spatial resolution are the mixed pixels, i.e., parts of the image where more than one land cover map lie in the same pixel. In this paper, we propose a method to address the problem of mixed pixels and to obtain a finer spatial resolution of the land cover classification maps. The method exploits the advantages of both soft classification techniques and spectral unmixing algorithms, in order to determine the fractional abundances of the classes at a sub-pixel scale. Spatial regularization by simulated annealing is finally performed to spatially locate the obtained classes. Experiments carried out on synthetic real data sets show excellent results both from a qualitative and quantitative point of view.
A physics-based unmixing method for thermal hyperspectral images

The estimation of surface emissivity and temperature from thermal hyperspectral data is a challenge. There are several methods that estimate the temperature and the emissivity by assuming that the pixel is composed by a single material. However, the estimation of the temperature on a mixed pixel, i.e. a pixel composed by more than one material, is more complex and has scarcely been investigated in the literature. This paper addresses this issue by jointly estimating the materials composing the mixed pixel and their temperatures. It uses an unmixing method based on the linearization of the Black Body law. The performance of this strategy is studied using synthetic data and a real thermal image acquired by the TASI sensor.
Canonical polyadic decomposition of hyperspectral patch tensors

Spectral unmixing (SU) is one of the most important and studied topics in hyperspectral image analysis. By means of spectral unmixing it is possible to decompose a hyperspectral image in its spectral components, the so-called endmembers, and their respective fractional spatial distributions, so-called abundance maps. The Canonical Polyadic (CP) tensor decomposition has proved to be a powerful tool to decompose a tensor data onto a few rank-one terms in a multilinear fashion. Here, we establish the connection between the CP decomposition and the SU problem when the tensor data is built by stacking small patches of the hyperspectral image. It turns out that the CP decomposition of this hyperspectral patch-tensor is equivalent to solving a blind regularized Extended Linear Mixing Model (ELMM).
The comparability of aggregated emissivity and temperature of heterogeneous pixel to conventional tes methods

Unmixing in the reflective domain is a well known technique to retrieve materials and their abundances from hyperspectral imaging scene. However, for the thermal domain (8–14 μm) only few unmixing models were developed due to the complexity of retrieving three unknowns (i.e. materials abundance, temperature and emissivity) from the pixel radiance. Attempt to solve this complexity is using aggregation models. The aggregation models are physics based models which estimate the pixel radiance with the contribution of acquisition parameters and scene description including the temperature and the emissivity of materials. This paper focuses on the comparison of temperature / emissivity estimated using inhouse aggregation model with two conventional Temperature and Emissivity Separation (TES) methods: Maximum Minimum Differences (MMD/TES) and SPectral SMoothness method (SpSm). This comparison is achieved using synthetic mixed pixels of two sensors AHS-160 and SEBASS. The results show that below a temperature difference of 30 K, the aggregation models are in good adequacy with the TES estimations.
Hyperspectral image segmentation using Binary Partition Trees

The work presented here proposes a new Binary Partition Tree pruning strategy aimed at the segmentation of hyperspectral images. The BPT is a region-based representation of images that involves a reduced number of elementary primitives and therefore allows to design a robust and efficient segmentation algorithm. Here, the regions contained in the BPT branches are studied by recursive spectral graph partitioning. The goal is to remove subtrees composed of nodes which are considered to be similar. To this end, affinity matrices on the tree branches are computed using a new distance-based measure depending on canonical correlations relating principal coordinates. Experimental results have demonstrated the good performances of BPT construction and pruning.
Empirical Automatic Estimation of the Number of Endmembers in Hyperspectral Images

In this letter, an eigenvalue-based empirical method is proposed in order to estimate the number of endmembers in hyperspectral data. This method is based on the distribution of the differences of the eigenvalues from the correlation and the covariance matrices, respectively. The eigenvalues corresponding to the noise are identical in the covariance and the correlation matrices, while the eigenvalues corresponding to the signal (the endmembers) are larger in the correlation matrix than in the covariance matrix. The proposed method is totally parameter free and very fast. It is validated by experiments carried on both synthetic and real data sets.
Unmixing-based gas plume tracking in LWIR hyperspectral video sequences

It is now possible to collect hyperspectral video sequences (HVS) at a near real-time frame rate. The wealth of spectral, spatial and temporal information of those sequences is particularly appealing for chemical gas plume tracking. Existing state-of-the-art methods for such applications however produce only a binary information regarding the position and shape of the gas plume in the HVS. Here, we introduce a novel method relying on spectral unmixing considerations to perform chemical gas plume tracking, which provides information related to the gas plume concentration in addition to its spatial localization. The proposed approach is validated and compared with three state-of-the-art methods on a real HVS.
Nonlinear Multiple Kernel Learning With Multiple-Structure-Element Extended Morphological Profiles for Hyperspectral Image Classification

In this paper, we propose a novel multiple kernel learning (MKL) framework to incorporate both spectral and spatial features for hyperspectral image classification, which is called multiple-structure-element nonlinear MKL (MultiSE-NMKL). In the proposed framework, multiple structure elements (MultiSEs) are employed to generate extended morphological profiles (EMPs) to present spatial-spectral information. In order to better mine interscale and interstructure similarity among EMPs, a nonlinear MKL (NMKL) is introduced to learn an optimal combined kernel from the predefined linear base kernels. We integrate this NMKL with support vector machines (SVMs) and reduce the min-max problem to a simple minimization problem. The optimal weight for each kernel matrix is then solved by a projection-based gradient descent algorithm. The advantages of using nonlinear combination of base kernels and multiSE-based EMP are that similarity information generated from the nonlinear interaction of different kernels is fully exploited, and the discriminability of the classes of interest is deeply enhanced. Experiments are conducted on three real hyperspectral data sets. The experimental results show that the proposed method achieves better performance for hyperspectral image classification, compared with several state-of-the-art algorithms. The MultiSE EMPs can provide much higher classification accuracy than using a single-SE EMP.
Information extraction by blind source separation from polarimetric SAR data

Cloude and Pottier H/α feature space [1] is one of the most employed methods for unsupervised PolSAR data classification based on Incoherent Target Decomposition. The association of the coherence matrix eigenvectors to the most dominant scatters in the analysed pixel introduces unfeasible regions in the H/α plane. The Independent Component Analysis provides promising new information to better interpret non-Gaussian heterogeneous clutter in the frame of polarimetric incoherent target decompositions. Not constrained to any orthogonality between the estimated scattering mechanisms that compose the clutter under analysis, ICA does not introduce any unfeasible region in the H/α plane, increasing the range of possible natural phenomenons depicted in the aforementioned feature space.
Pansharpening Quality Assessment using Modulation Transfer Function Filters

Quality assessment of pansharpening methods is not a trivial task. The generally used indexes i.e., Q4, SAM and ERGAS require a reference image at the same resolution as the fused image. Since, this reference image is not available the quality of pansharpening algorithms is tested at degraded resolution. However, this does not provide the means to assess the quality of fused images at the desired high resolution. In this paper we propose a method to assess the fusion quality at high resolution by making use of modulation transfer function filters in the frame works of Wald's spectral consistency protocol and Zhou's spatial quality protocol. The results are compared with the recently proposed QNR quality index which also does not require a reference high resolution multispectral image, Zhou's protocol, Q4, ERGAS and SAM.
Detection of Hedges in a Rural Landscape Using a Local Orientation Feature: From Linear Opening to Path Opening

The detection of hedges is addressed in this paper. A hierarchical detection scheme in two steps is proposed. It is based on the use of both spatial information and spectral information in the detection process. First, woody elements are detected using the spectral information. From the membership map, the local orientation of each pixel is computed using directional filters. The morphological directional profile is defined as the composition of the outputs of directional filters with a varying orientation parameter. The local orientation feature is defined as the difference between the maximum and the minimum values of the morphological directional profile. A second detection step is done using the local orientation feature and the membership value to the woody elements class to extract the hedges. Experiments conducted on several real satellite images show that the method provides very good results in terms of detection accuracies. For one experiment, the overall accuracy is increased 80% to 91% with the proposed method. Furthermore, the methods is robust even if the size of the training samples is limited.
Multiscale stochastic watershed for unsupervised hyperspectral image segmentation

This paper deals with unsupervised segmentation of hyper-spectral images. It is based on the stochastic watershed, an approach to estimate a probability density function (pdf) of contours of an image using MonteCarlo simulations of watershed segmentations. In particular, it is introduced for the first time a multiscale framework for the computation of the pdf of contours using the stochastic watershed. Two multiscale approaches are considered: i) a linear scale-space using Gaussian filters, ii) a nonlinear morphological scale-space pyramid using levelings. In addition, a multiscale pyramid obtained by modifying the size of the random markers is also studied. Then, it is shown how the pdf of contours can finally be segmented using the non-parametric waterfalls algorithm. The performances of the proposed methods are compared using two examples of standard remote sensing hyperspectral images.
The Physical Meaning of Independent Components and Artifact Removal of Hyperspectral Data from Mars using ICA

The truth about what chemicals are to be found on the surface of Mars lies hidden in Gigabytes of hyperspectral data. How to reveal this mystery is the subject of this paper. Independent component analysis (ICA) is used for identification and classification of endmembers and for artifact removal. The classification results are compared with the result of a wavelet classifier and reference spectra are used for identification of known substances. CO2 ice and water ice and an intimate mixture of CO2 ice and dust are effectively found as independent components, but because of high negative correlation of dust and CO2 ice, dust is not found as a separate component. ICA can be used to valuate the atmospheric effect removal, which is currently being used and can help in this preprocessing. ICA can also be used for other artifacts, such as to find and clean corrupted channels and to detect the effect of the overlay of sensors. It is proposed to view the mixing matrix as a collection of independent components (ICs) spectra, and use this for automatic detection of known endmembers
Combining Airborne Photographs and Spaceborne SAR Data to Monitor Temperate Glaciers: Potentials and Limits

Monitoring temperate glacier activity has become more and more necessary for economical and security reasons and as an indicator of the local effects of global climate change. Remote sensing data provide useful information on such complex geophysical objects, but they require specific processing techniques to cope with the difficult context of moving and changing features in high-relief areas. This paper presents the first results of a project involving four laboratories developing and combining specific methods to extract information from optical and synthetic aperture radar (SAR) data. Two different information sources are processed, namely: 1) airborne photography and 2) spaceborne C-band SAR interferometry. The difficulties and limitations of their processing in the context of Alpine glaciers are discussed and illustrated on two glaciers located in the Mont-Blanc area. The results obtained by aerial triangulation techniques provide digital terrain models with an accuracy that is better than 30 cm, which is compatible with the computation of volume balance and useful for precise georeferencing and slope measurement updating. The results obtained by SAR differential interferometry using European Remote Sensing Satellite images show that it is possible to measure temperate glacier surface velocity fields from October to April in one-day interferograms with approximately 20-m ground sampling. This allows to derive ice surface strain rate fields required to model the glacier flow. These different measurements are complementary to results obtained during the summer from satellite optical data and ground measurements that are available only in few accessible points
Guest Editorial Computational Imaging for Earth Sciences

The papers in this special section focused on computational imaging for the earth sciences market. From the core of the earth to the farthest reaches of our planets magnetic ﬁelds, the earth sciences are concerned with all aspects of monitoring, exploring, explaining, and exploiting of natural events and resources in the geosphere. Revolutions in computational imaging over the past two decades have had profound implications across this ﬁeld, bringing new modalities into common use and impacting application domains from weather monitoring and prediction, subsurface sensing, seismic imaging and exploration, to the production of minerals, oil and gas. In all these areas, reliable information extraction by collecting and processing sensor data increasingly hinges on integrated sensor design, system modeling, and efﬁcient computational methods. The scope of such models and methods have more recently integrated statistical pattern recognition nd machine learning systems to account for variability that cannot be completely captured by physical models or simulation. Enabling most all of these processing methods are concomitant developments in optimization theory both in Euclidean spaces as well as on more general manifolds. This special issue solicited relevant contributions from researchers in signal and image processing, inverse problems, machine learning, and related areas as applied to problems of image formation and analysis arising in the context of earth science applications covering the wide range of application domains.
Multiple Feature Kernel Sparse Representation Classifier for Hyperspectral Imagery

Multiple types of features, e.g., spectral, filtering, texture, and shape features, are helpful for hyperspectral image (HSI) classification tasks. Combining multiple features can describe the characteristics of pixels from different perspectives, and always results in better classification performance. Recently, multifeature combination learning has been widely employed to the multitask-learning-based representation-based model to obtain a multifeature representation vector. However, the linear sparse representation-based classifier (SRC) cannot handle the HSI with highly nonlinear distribution, and kernel sparse representation-based classifier (KSRC) can remedy the drawback of linear SRC. By adopting nonlinear mapping, the samples in kernel space are often of high or even infinite dimensionality. In this paper, we integrate kernel principal component analysis into multifeature-based KSRC and propose a novel multiple feature kernel sparse representation-based classifier (namely, MFKSRC) for hyperspectral imagery. More specifically, spatial features, Gabor textures, local binary patterns, and difference morphological profiles are adopted and then each kind of feature is transformed nonlinearly into a new low-dimensional kernel space. The proposed framework can handle data with nonlinear distribution and add a dimensionality reduction stage in kernel space before optimizing the corresponding cost function. Experimental results on different HSIs demonstrate that the proposed MFKSRC algorithm outperforms the state-of-the-art classifiers.
From local to global unmixing of hyperspectral images to reveal spectral variability

The linear mixing model is widely assumed when unmixing hyperspectral images, but it cannot account for endmembers spectral variability. Thus, several workarounds have arisen in the hyperspectral unmixing literature, such as the extended linear mixing model (ELMM), which authorizes endmembers to vary pixelwise according to scaling factors, or local spectral unmixing (LSU) where the unmixing process is conducted locally within the image. In the latter case however, results are difficult to interpret at the whole image scale. In this work, we propose to analyze the local results of LSU within the ELMM framework, and show that it not only allows to reconstruct global endmembers and fractional abundances from the local ones, but it also gives access to the scaling factors advocated by the ELMM. Results obtained on a real hyperspectral image confirm the soundness of the proposed methodology.
Multisource Data Fusion and Fisher Criterion-Based Nearest Feature Space Approach to Landslide Classification

In this paper, a novel technique known as the Fisher criterion-based nearest feature space (FCNFS) approach is proposed for supervised classification of multisource images for the purpose of landslide hazard assessment. The method is developed for land cover classification based upon the fusion of remotely sensed images of the same scene collected from multiple sources. This paper presents a framework for data fusion of multisource remotely sensed images, consisting of two approaches: 1) the band generation process(BGP); and 2) the FCNFS classifier. We propose the BGP to create a new set of additional bands that are specifically accommodated to the landslide class and are extracted from the original multisource images. In comparison to the original nearest feature space (NFS) method, the proposed improved FCNFS classifier uses the Fisher criterion of between-class and within-class discrimination to enhance the classifier. In the training phase, the labeled samples are discriminated by the Fisher criterion, which can be treated as a preprocessing step of the NFS method. After completion of the training, the classification results can be obtained from the NFS algorithm. In order for the proposed FCNFS to be effective for multispectral images, a multiple adaptive BGP is introduced to create an additional set of bands specially accommodated to landslide classes. Experimental results show that the proposed BGP/FCNFS framework is suitable for land cover classification in Earth remote sensing and improves the classification accuracy compared to conventional classifiers.
On the use of ICA for hyperspectral image analysis

Independent component analysis (ICA) is a very popular method that has shown success in blind source separation, feature extraction and unsupervised recognition. In recent years ICA has been largely studied by researchers from the signal processing community. This paper addresses a more in-depth study on the use of this method, applied to hyper-spectral images used for remote sensing purposes. In a first part, source separation is addressed. Since the independence of sources is usually not verified in hyperspectral real data images, ICA, if used alone, is not a suitable tool to unmix sources. We propose a hierarchical approximation for the use of ICA as a pre-processing step for a Bayesian Positive Source Separation method. In a second part, the use of ICA for dimensionality reduction is studied in the frame of hyperspectral data classification. Experimental results show the effectiveness of ICA when used for hyperspectral image pre-processing for the two considered applications.
Spherical Symmetry of Complex Stochastic Models in Multivariate High-Resolution PolSAR Images

The multiplicative model, expressed as a product between the square root of a scalar positive quantity (texture) and the description of an equivalent homogeneous surface (speckle), is one of the most appropriate and disseminated models used to describe high-resolution polarimetric synthetic aperture radar (PolSAR) clutter. Generally, the texture is assumed polarization independent, which causes PolSAR data to present a spherical symmetry property, allowing for the usage of most of the algorithms present in the literature. Nevertheless, the existence of polarization-dependent clutter has also been reported, for which specific algorithms need to be derived. Therefore, it becomes clear that the first step in SAR data analysis should be the validation of the model employed. Within this context, this paper presents a new methodological framework to assess the conformity of multivariate high-resolution SAR data with respect to the product model in terms of asymptotic statistics. More precisely, spherical symmetry is investigated by applying statistical hypothesis testing on the structure of the quadricovariance matrix. Simulated data, data from the P-band airborne data set acquired by the Office National d'Études et de Recherches Aérospatiales (ONERA) over the French Guiana in 2009 in the frame of the European Space Agency campaign TropiSAR and a RAMSES X-band image acquired over Brétigny, France, are taken into consideration to investigate the performance of the derived test. The detection results are qualitatively and quantitatively analyzed, and some important conclusions are drawn regarding the methodology employed in analyzing SAR data.