nils-herrmann/clean_scirepeval huggingface dataset:
Performance analysis of unsupervised unmixing models for thermal hyepsrpectral

Hyperspectral unmixing which consists of decomposing the measured pixel radiance into mixtures of `pure' spectra whose fractions are referred to as abundances, is a common procedure in the signal and image applications. In this paper, the performance of unsupervised unmixing methods for hyperspectral data is evaluated using airborne thermal data and pixel-based ground truth in-situ. Specifically, the procedures of endmember extraction and mixing using linear and bilinear models are validated using set of pixels whose mixing proportion are known.
Pansharpening of Hyperspectral images using spatial distortion optimization

This paper presents a novel method for the spatial quality improvement of low resolution Hyperspectral (HS) images by making use of a high resolution panchromatic (Pan) image. Since the introduction of all the details extracted from the Pan image into the upscaled HS images may result in spectral and spatial distortions, a detail injection model based on the optimization of QNR spatial quality index is proposed. This proposed model produces pansharpened images while preserving spectral fidelity. Also, to speed up the fusion process we propose to use the Universal Image Quality Index (UIQI) for dimensionality reduction before performing pansharpening. Finally, a comparison of the proposed method is presented with some existing pansharpening methods.
Improving road detection on SAR images using fuzzy fusion methods

This paper focuses on the use of fuzzy fusion techniques to improve the automatic detection of linear features on multi-temporal SAR images. Different fusion strategies involving different fusion operators are presented. Since T-norms and T-conorms do not lead to satisfactory results (these operators are respectively too severe and too indulgent), the first strategy consists in fusing the data using a compromise operator. The second strategy consists in fusing the results computed with two operators with opposite properties, in order to obtain a global intermediate result. Thanks to the wide range of behaviours they provide, fuzzy operators are used to test and compare these two fusion strategies on real ERS-1 data.
Segmentation and Classification of Hyperspectral Data using Watershed

The paper presents a new segmentation and classification scheme to analyze hyperspectral (HS) data. The Robust Color Morphological Gradient of the HS image is computed, and the watershed transformation is applied to the obtained gradient. After the pixel-wise Support Vector Machines classification, the majority voting within the watershed regions is performed. Experimental results are presented on a 103-airborne ROSIS image, of the University of Pavia, Italy. The integration of the spatial information from the watershed segmentation into the HS image classification improves the classification accuracies, when compared to the pixel-wise classification.
Image fusion and spectral unmixing of hyperspectral images for spatial improvement of classification maps

In this paper we propose a new approach for the improvement of the spatial resolution of hyperspectral image classification maps combining both spectral unmixing and pansharpening approaches. The main idea is to use a spectral unmixing algorithm based on neural networks to retrieve the abundances of the endmembers present in the scene, and then use the spatial information retrieved from the pansharpened image to find the location of each endmember within the enhanced pixel according to the endmembers abundances. The proposed approach has been applied both to real and synthetic datasets.
Nonnegative Tensor CP Decomposition of Hyperspectral Data

New hyperspectral missions will collect huge amounts of hyperspectral data. In addition, it is possible now to acquire time series and multiangular hyperspectral images. The process and analysis of these big data collections will require common hyperspectral techniques to be adapted or reformulated. The tensor decomposition, which is also known as multiway analysis, is a technique to decompose multiway arrays, i.e., hypermatrices with more than two dimensions (ways). Hyperspectral time series and multiangular acquisitions can be represented as a three-way tensor. Here, we apply canonical polyadic (CP) tensor decomposition techniques to the blind analysis ohyperspectral big data. In order to do so, we use a novel compression-based nonnegative CP decomposition. We show that the proposed methodology can be interpreted as multilinear blind spectral unmixing, i.e., a higher order extension of the widely known spectral unmixing. In the proposed approach, the big hyperspectral tensor is decomposed in three sets of factors, which can be interpreted as spectral signatures, their spatial distribution, and temporal/angular changes. We provide experimental validation using a study case of the snow coverage of the French Alps during the snow season.
Fusion of Multispectral and Panchromatic Images Based on Morphological Operators

Nonlinear decomposition schemes constitute an alternative to classical approaches for facing the problem of data fusion. In this paper, we discuss the application of this methodology to a popular remote sensing application called pansharpening, which consists in the fusion of a low resolution multispectral image and a high-resolution panchromatic image. We design a complete pansharpening scheme based on the use of morphological half gradient operators and demonstrate the suitability of this algorithm through the comparison with the state-of-the-art approaches. Four data sets acquired by the Pleiades, Worldview-2, Ikonos, and Geoeye-1 satellites are employed for the performance assessment, testifying the effectiveness of the proposed approach in producing top-class images with a setting independent of the specific sensor.
Classification of hyperspectral images by using morphological attribute filters and Independent Component Analysis

In this paper, a technique based on Independent Component Analysis (ICA) and morphological attribute filters is presented for the classification of high geometrical resolution hyperspectral images. The ICA is computed instead of the conventional principal component analysis (PCA) in order to better model the information in the hyperspectral image. The spatial characteristics of the objects in the scene are modeled by different multi-level attribute filters. Moreover, a method for increasing the robustness of the analysis based on a decision fusion strategy is proposed. A hyperspectral high resolution image acquired over the city of Pavia (Italy) was considered in the experiments.
Sphericity of complex stochastic models in multivariate SAR images

Polarimetry and multi-pass interferometry extend the dimensionality of SAR images, therefore the necessity to have multivariate statistic (and non-Gaussian) distributions as models for these types of data: such are the SIRV (Spherically Invariant Random Vectors). However, as the stochastic model becomes more complex, correctly estimating its parameters gets difficult. More, although they are versatile, the SIRV models are not guaranteed to match the PolSAR / InSAR data. To evaluate the pertinence of those models with respect to the PolSAR and multi-pass InSAR data, through one of their most important statistic properties, namely sphericity, it is the purpose of this paper. The proposed analysis is illustrated with spaceborne multi-pass InSAR TerraSAR-X data.
Comparison of prefiltering operators for road network extraction in SAR images

The problem of detection of road networks in Synthetic Aperture Radar (SAR) images is addressed. This task is important for the registration of SAR images to existing maps and for perfect superposition of multitemporal SAR images. SAR image analysis is a difficult task because of speckle noise and textures and low signal to noise ratios. The paper focuses on the comparison of filter performances in terms of road extraction. The study points out the interest of nonlinear adaptive filtering techniques. On the other hand, it is shown that techniques dedicated to speckle noise filtering are not always the most suited to road network extraction.
Fuzzy fusion techniques for linear features detection in multitemporal SAR images

This paper is concerned with the automatic detection of linear features in SAR satellite data, with application to road network extraction. After a directional prefiltering step, a morphological line detector is presented. To improve the detection performances, the results obtained on multitemporal data are fused. Different fusion strategies involving different fusion operators are then presented. Since extensions of classical set union and intersection do not lead to satisfactory results (the corresponding operators are either too indulgent or too severe), the first strategy consists of fusing the data using a compromise operator. The second strategy consists of fusing the results computed with two operators that have opposite properties, in order to obtain a final intermediate result. Thanks to the wide range of properties they provide, fuzzy operators are used to test and compare these two fusion strategies on real ERS-1 multitemporal data.
An unmixing-based method for the analysis of thermal hyperspectral images

The estimation of surface emissivity and temperature from thermal hyperspectral data is a challenge. Methods that estimate the temperature and emissivity on a pixel composed by one single material exist. 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 proposing an estimator which linearizes the Black Body law around the mean temperature of each material. The performance of this estimator is studied using simulated data with different hyperspectral sensor configurations and under various noise conditions. The obtained results are encouraging and show an accuracy on the estimated temperature of 0.5 K while using high spectral resolution sensor.
Hyperspectral pansharpening using convex optimization and collaborative total variation regularization

Hyperspectral pansharpening is a challenging research area and several methods have been recently developed to fuse low resolution hyperspectral and high resolution panchromatic images. In this paper we focus on a recent regularization method, called Collaborative Total Variation, exploiting a convex optimization algorithm. We evaluate the effectiveness of this novel approach in comparison to existing methods, and assess the performances on two datasets: a synthetic scene mimicking the characteristics of the Hyperion and ALI sensors and the Pavia University dataset.
Saliency based visualization of hyper-spectral images

The problem with visualization of hyper-spectral images on tri-stimulus displays arises from the fact that they contain hundreds of spectral bands while generally used display devices support only three bands/channels namely blue, green and red. Therefore, for visualization a hyper-spectral (HS) image has to be reduced to three bands. The main challenge while performing this band reduction is to retain and display the maximum information available in a hyper-spectral image. Human visual system focuses attention on certain regions in images called “salient regions”. Therefore to provide a comprehensive representation of hyper-spectral data on tri-stimulus displays we propose to use a weighted fusion method of saliency maps and hyper-spectral bands. The efficacy of the proposed algorithm has been demonstrated by tests on both urban and countryside images of AVIRIS and ROSIS sensors.
Synthesis of Multispectral Images to High Spatial Resolution: A Critical Review of Fusion Methods Based on Remote Sensing Physics

Our framework is the synthesis of multispectral images (MS) at higher spatial resolution, which should be as close as possible to those that would have been acquired by the corresponding sensors if they had this high resolution. This synthesis is performed with the help of a high spatial but low spectral resolution image: the panchromatic (Pan) image. The fusion of the Pan and MS images is classically referred as pan-sharpening. A fused product reaches good quality only if the characteristics and differences between input images are taken into account. Dissimilarities existing between these two data sets originate from two causes-different times and different spectral bands of acquisition. Remote sensing physics should be carefully considered while designing the fusion process. Because of the complexity of physics and the large number of unknowns, authors are led to make assumptions to drive their development. Weaknesses and strengths of each reported method are raised and confronted to these physical constraints. The conclusion of this critical survey of literature is that the choice in the assumptions for the development of a method is crucial, with the risk to drastically weaken fusion performance. It is also shown that the Amelioration de la Resolution Spatiale par Injection de Structures concept prevents from introducing spectral distortion into fused products and offers a reliable framework for further developments.
Hyperspectral and Multispectral Data Fusion: A comparative review of the recent literature

In recent years, enormous efforts have been made to design image-processing algorithms to enhance the spatial resolution of hyperspectral (HS) imagery. One of the most commonly addressed problems is the fusion of HS data with higher spatial resolution multispectral (MS) data. Various techniques have been proposed to solve this data-fusion problem based on different theories, including component substitution (CS), multiresolution analysis (MRA), spectral unmixing, and Bayesian probability. This article presents a comparative review of those HS-MS fusion techniques with extensive experiments. Ten state-of-the-art HS-MS fusion methods are compared by assessing their fusion performance both quantitatively and visually. Eight data sets featuring different geographical and sensor characteristics are used in the experiments to evaluate the generalizability and versatility of the fusion algorithms. To maximize the fairness and transparency of this comparison, publicly available source codes are used, and parameters are individually tuned for maximum performance.
Using High-Resolution Airborne and Satellite Imagery to Assess Crop Growth and Yield Variability for Precision Agriculture

With increased use of precision agriculture techniques, information concerning within-field crop yield variability is becoming increasingly important for effective crop management. Despite the commercial availability of yield monitors, many crop harvesters are not equipped with them. Moreover, yield monitor data can only be collected at harvest and used for after-season management. On the other hand, remote sensing imagery obtained during the growing season can be used to generate yield maps for both within-season and after-season management. This paper gives an overview on the use of airborne multispectral and hyperspectral imagery and high-resolution satellite imagery for assessing crop growth and yield variability. The methodologies for image acquisition and processing and for the integration and analysis of image and yield data are discussed. Five application examples are provided to illustrate how airborne multispectral and hyperspectral imagery and high-resolution satellite imagery have been used for mapping crop yield variability. Image processing techniques including vegetation indices, unsupervised classification, correlation and regression analysis, principal component analysis, and supervised and unsupervised linear spectral unmixing are used in these examples. Some of the advantages and limitations on the use of different types of remote sensing imagery and analysis techniques for yield mapping are also discussed.
Multimorphological Superpixel Model for Hyperspectral Image Classification

With the development of hyperspectral sensors, nowadays, we can easily acquire large amount of hyperspectral images (HSIs) with very high spatial resolution, which has led to a better identification of relatively small structures. Owing to the high spatial resolution, there are much less mixed pixels in the HSIs, and the boundaries between these categories are much clearer. However, the high spatial resolution also leads to complex and fine geometrical structures and high inner-class variability, which make the classification results very “noisy.” In this paper, we propose a multimorphological superpixel (MMSP) method to extract the spectral and spatial features and address the aforementioned problems. To reduce the difference within the same class and obtain multilevel spatial information, morphological features (multistructuring element extended morphological profile or multiattribute filter extended multi-attribute profiles) are first obtained from the original HSI. After that, simple linear iterative clustering segmentation method is performed on each morphological feature to acquire the MMSPs. Then, uniformity constraint is used to merge the MMSPs belonging to the same class which can avoid introducing the information from different classes and acquire spatial structures at object level. Subsequently, mean filtering is utilized to extract the spatial features within and among MMSPs. At last, base kernels are obtained from the spatial features and original HSI, and several multiple kernel learning methods are used to obtain the optimal kernel to incorporate into the support vector machine. Experiments conducted on three widely used real HSIs and compared with several well-known methods demonstrate the effectiveness of the proposed model.
Snow cover estimation based on spectral unmixing

Spectral Unmixing is the most recent method used to recover the Snow Cover Fraction of an area, but it depends particularly on the relevance of the set of endmembers. This communication investigates different strategies for defining set of endmembers for retrieving snow cover fraction with spectral unmixing. Endmembers can be estimated from on site measurements or estimated directly on the image. In this work we propose a set of endmembers associating semantics of field data for snow endmembers with the extraction of a set in a date without snow for other materials. A heterogeneous area in the Alps was considered in the experiment. Considering reference maps of snow available for several dates, Precision and Mean Absolute Error were computed for evaluating the estimated Snow Cover Fractions. Results obtained confirm the soundness of the proposed approach for low snow fraction.
A sparsity promoting bilinear unmixing model

An algorithm, Bilinear SPICE (BISPICE), for simultaneously estimating the number of endmembers, the endmembers, and proportions for a bilinear mixing model is derived and evaluated. BISPICE generalizes the SPICE algorithm for linear mixing. The proportion estimation steps of SPICE and BISPICE are similar. However, the endmember updates, one novel aspect of the work, are quite different. The SPICE objective function is quadratic in the endmembers. The BISPICE is a fourth degree polynomial. In SPICE, endmembers are updated simultaneously via a closed form. In BISPICE, each endmember must be updated with respect to all other endmembers. Empirically, BISPICE estimated endmembers and proportions more accurately then SPICE, even though the data fitting error was higher.
Adaptive pixel neighborhood definition for the classification of hyperspectral images with support vector machines and composite kernel

The pixel-wise classification of hyperspectral images with a reduced training set is addressed. The joint use of the spectral and the spatial information is investigated. The spectral information simply consists of the spectral value of each pixel. For the spatial information, we use an area filter to simplify the image and extract consistent connected components. These components are used to define an adaptive neighborhood for each pixel of the image. The vector median value of each component is defined as a spatial feature for the classification. support vector machines are used for the classification and a composite kernel is used to combine both the spatial and the spectral information. Experiments are conducted on AVIRIS hyperspectral data. The proposed approach provides significant improvements in terms of classification accuracy when compared with a standard statistical method (maximum likelihood) and with a SVM classifier using the spectral information alone. Robustness with respect to the size of the training set is also investigated.
Intercomparison and Validation of Techniques for Spectral Unmixing of Hyperspectral Images: A Planetary Case Study

As the volume of hyperspectral data for planetary exploration increases, efficient yet accurate algorithms are decisive for their analysis. In this paper, the capability of spectral unmixing for analyzing hyperspectral images from Mars is investigated. For that purpose, we consider the Russell megadune observed by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) and the High-Resolution Imaging Science Experiment (HiRISE) instruments. In late winter, this area of Mars is appropriate for testing linear unmixing techniques because of the geographical coexistence of seasonal CO2 ice and defrosting dusty features that is not resolved by CRISM. Linear unmixing is carried out on a selected CRISM image by seven state-of-the-art approaches based on different principles. Three physically coherent sources with an increasing fingerprint of dust are recognized by the majority of the methods. Processing of HiRISE imagery allows the construction of a ground truth in the form of a reference abundance map related to the defrosting features. Validation of abundances estimated by spectral unmixing is carried out in an independent and quantitative manner by comparison with the ground truth. The quality of the results is estimated through the correlation coefficient and average error between the reconstructed and reference abundance maps. Intercomparison of the selected linear unmixing approaches is performed. Global and local comparisons show that misregistration inaccuracies between the HiRISE and CRISM images represent the major source of error. We also conclude that abundance maps provided by three methods out of seven are generally accurate, i.e., sufficient for a planetary interpretation.
Evaluation of Kernels for Multiclass Classification of Hyperspectral Remote Sensing Data

Classification of hyperspectral remote sensing data with support vector machines (SVMs) is investigated. SVMs have been introduced recently in the field of remote sensing image processing. Using the kernel method, SVMs map the data into higher dimensional space to increase the separability and then fit an optimal hyperplane to separate the data. In this paper, two kernels have been considered. The generalization capability of SVMs as well as the ability of SVMs to deal with high dimensional feature spaces have been tested in the situation of very limited training set. SVMs have been tested on real hyperspectral data. The experimental results show that SVMs used with the two kernels are appropriate for remote sensing classification problems
Extended morphological profiles using auto-associative neural networks for hyperspectral data classification

Recently, morphological profiles have be observed as good tools to fuse spectral and spatial information to produce better classification results. In general, the profiles are built with the features derived using the principal component analysis (PCA). Auto-associative neural network (AANN), which can be seen as an implementation of non-linear PCA is used for unsupervised feature reduction of hyperspectral data. In this paper, we investigate the suitability of the features derived using AANN to build extended morphological profiles for hyperspectral data classification.