nils-herrmann/clean_scirepeval huggingface dataset:
A Regression-Based High-Pass Modulation Pansharpening Approach

Pansharpening usually refers to the fusion of a high spatial resolution panchromatic (PAN) image with a higher spectral resolution but coarser spatial resolution multispectral (MS) image. Owing to the wide applicability of related products, the literature has been populated by many papers proposing several approaches and studies about this issue. Many solutions require a preliminary spectral matching phase wherein the PAN image is matched with the MS bands. In this paper, we propose and properly justify a new approach for performing this step, demonstrating that it yields state-of-the-art performance. The comparison with existing spectral matching procedures is performed by employing four data sets, concerning different kinds of landscapes, acquired by the Pléiades, WorldView-2, and GeoEye-1 sensors.
Assessment of Feature Selection Techniques for Support Vector Machine Classification of Satellite Imagery

The problem of focusing on the most relevant information in a potentially overwhelming quantity of data has become increasingly important. Using irrelevant or noisy features not only can affect the accuracy of the classification results obtained but also the convergence time. In this paper several feature selection algorithms used with the Support Vector Machine (SVM) algorithm are presented. The feature selection algorithms are classified as filter and wrapper approaches. Two different wrapper techniques are presented: the first one uses the generalization error estimate of the leave-one-example-out error, while the second one uses the error estimate of the leave-one-feature-out error. Filter approaches with 4 different parameters are presented, namely: the mutual information, the FScore, and two advanced entropy measures are studied. Results in the context of change detection using satellite imagery are then discussed.
Hyperspectral image classification based on spectral and geometrical features

In this paper, we propose to integrate geometrical features, such as the characteristic scales of structures, with spectral features for the classification of hyperspectral images. The spectral features which only describe the material of structures can not distinguish objects made by the same material but with different semantic meanings (such as the roofs of some buildings and the roads). The use of geometrical features is therefore necessary. Moreover, since the dimension of a hyperspectral image is usually very high, we use linear unmixing algorithm to extract the endmemebers and their abundancemaps in order to represent compactly the spectral information. Afterwards, with the help of these abundance maps, we propose a method based on topographic map of images to estimate local scales of structures in hyperspectral images. The experiment shows that the geometrical features can improve the classification results, especially for the classes made by the same material but with different semantic meanings. When compared to the traditional contextual features (such as morphological profiles), the local scale feature provides satisfactory results without considerably increasing the feature dimension.
Multilinear spectral unmixing of hyperspectral multiangle images

Spectral unmixing 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. New hyperspectral missions will allow to acquire hyperspectral images in new ways, for instance, in temporal series or in multi-angular acquisitions. Working with these incoming huge databases of multi-way hyperspec-tral images will raise new challenges to the hyperspectral community. Here, we propose the use of compression-based non-negative tensor canonical polyadic (CP) decompositions to analyze this kind of datasets. Furthermore, we show that the non-negative CP decomposition could be understood as a multi-linear spectral unmixing technique. We evaluate the proposed approach by means of Mars synthetic datasets built upon multi-angular in-lab hyperspectral acquisitions.
Classification of remote sensing images from urban areas using a fuzzy possibilistic model

The classification of very high-resolution remotely sensed images from urban areas is addressed. Previous studies have shown the interest of exploiting the local geometrical information of each pixel to improve the classification. This is performed using the derivative morphological profile (DMP) obtained with a granulometric approach, using opening and closing operators. For each pixel, this DMP constitutes the feature vector on which the classification is based. In this letter, we present an interpretation of the DMP in terms of a fuzzy measurement of the characteristic size and contrast of each structure. This fuzzy measure can be compared to predefined possibility distributions to derive a membership degree for a set of given classes. The decision is taken by selecting the class with the highest membership degree. This model is illustrated and validated in a classification problem using IKONOS images.
Wet snow backscattering sensitivity on density change for SWE estimation

This paper deals particularly with the sensitivity of the wet snow backscattering coefficient on density change. The presented backscattering model is based on the approach used in the dry snow analysis , appropriately modified to account for the increased dielectric contrast caused by liquid water presence. It encircles our undertaking of simulating and analysing snow backscattering using fundamental scattering theories (IEM-B, QCA, QCA-CP). The wet snow parameters are chosen according to the area of the particular interest - the French Alps, while the choice of the SAR sensor parameters (frequency, polarization) is primarily conditioned by the initially settled goal - reaching qualitative conclusions concerning wet snow backscattering mechanism. Based on simulation results, we state the dominance of the snow pack surface backscattering component, causing the backscattering to be directly proportional to the volumetric liquid water content. This result is confirmed by the performed in situ measurements. We illustrate as well the decrease of this effect with the increase in operating frequency.
Signal characterization using generalized “Time-phase derivatives” representation

Natural signals are often characterized by a complex time-frequency behaviour. These signals exist in many different applications and systems from underwater acoustic to audio signals with sound attacks or electrical systems with partial discharges and commutation switches, for example. There is a huge number of Time-Frequency (TF) methods that aim to characterize these signals in terms of first phase derivative analysis (i.e Instantaneous Frequency Law). Recently, we introduced the time frequency distribution based on complex lag arguments. This distribution is able to reduce inner interferences terms which appear when studying non-linear TF components. It also offers access to an instantaneous law representation of any phase derivative order. In this paper, we use these two properties to study highly non-stationary signals as well as transient signals.
Very short term forecasting of the Global Horizontal Irradiance through Helioclim maps

To handle the increasing penetration rate of intermittent energies such as photo-voltaic power plants, grid managers require accurate forecasts of the primary resources. In this paper, we propose a new method to forecast the Global Horizontal Irradiance (GHI) up to 1 hour using HelioClim-3 maps as only inputs. HelioClim-3 maps are Solar Surface Irradiance (SSI) maps derived from geostationary satellite images through the Heliosat-2 method. We use a block matching algorithm to retrieve the motion vector field, allowing us to anticipate the future on site GHI values. The results were then corrected following a statistical post-processing step tuned on a training data set. The model has been tested on a one year data set acquired on the SIRTA site, Polytechnique campus, France. Besides the very promising performances of the proposed method compared to the persistence model, we extracted additional forecast information from the SSI maps like the minimum and maximum expected values, which enlighten the model's potential and limitations.
Unsupervised linear unmixing of hyperspectral image for crop yield estimation

Multispectral and hyperspectral imagery are often used for estimating crop yield. This paper describes an unsupervised unmixing scheme of hyperspectral images on field in order to estimate the crop yield. From the hyperspectral images, the endmembers and their abundance maps are computed by unsupervised unmixing. The abundance maps are then compared with the crop yield data. The results show the capability for estimating crop yield of the unmixing scheme, thanks to the high correlations between the crop yield data and the abundance maps of the endmembers corresponding to crop, even though the scheme is totally unsupervised.
Hyperspectral Image Classification With Rotation Random Forest Via KPCA

Random Forest (RF) is a widely used classifier to show a good performance of hyperspectral data classification. However, such performance could be improved by increasing the diversity that characterizes the ensemble architecture. In this paper, we propose a novel ensemble approach, namely rotation random forest via kernel principal component analysis (RoRF-KPCA). In particular, the original feature space is first randomly split into several subsets, and KPCA is performed on each subset to extract high order statistics. The obtained feature sets are merged and used as input to an RF classifier. Finally, the results achieved at each step are fused by a majority vote. Experimental analysis is conducted using real hyperspectral remote sensing images to evaluate the performance of the proposed method in comparison with RF, rotation forest, support vector machines, and RoRF-PCA. The obtained results demonstrate the effectiveness of the proposed method.
Morphological image distances for hyperspectral dimensionality exploration using Kernel-PCA and ISOMAP

The application of nonlinear manifold learning for hyperspec-tral image analysis has been widely studied in last years [1, 4]. One of the main ingredients of these data reduction techniques is the distance used to compare the spectral band images. By means of this distance the pairwise similarity matrix is built and then, the matrix is used to explore the intrinsic dimensionality of the hyperspectral image.
Mahalanobis kernel for the classification of hyperspectral images

The definition of the Mahalanobis kernel for the classification of hyperspectral remote sensing images is addressed. Class specific covariance matrices are regularized by a probabilistic model which is based on the data living in a subspace spanned by the p first principal components. The inverse of the covariance matrix is computed in a closed form and is used in the kernel to compute the distance between two spectra. Each principal direction is normalized by a hyperparameter tuned, according to an upper error bound, during the training of an SVM classifier. Results on real data sets empirically demonstrate that the proposed kernel leads to an increase of the classification accuracy by comparison to standard kernels.
Martian aerosol abundance estimation based on unmixing of hyperspectral imagery

Classical linear unmixing approaches are not valid if the atmosphere and the aerosol are present in hyperspectral data, since the mixture model is no longer linear. In this paper, we present an iterative approach for estimating the abundance of aerosol based on unmixing of Martian hyperspectral data. On one hand, the results can provide the information on the aerosol of the Mars, which is very difficult to obtain. On the other hand, we can use the result to remove the effect of the aerosol on the original image and obtain more accurate linear unmixing results on the surface reflectance.
Class-Specific Sparse Multiple Kernel Learning for Spectral–Spatial Hyperspectral Image Classification

In recent years, many studies on hyperspectral image classification have shown that using multiple features can effectively improve the classification accuracy. As a very powerful means of learning, multiple kernel learning (MKL) can conveniently be embedded in a variety of characteristics. This paper proposes a class-specific sparse MKL (CS-SMKL) framework to improve the capability of hyperspectral image classification. In terms of the features, extended multiattribute profiles are adopted because it can effectively represent the spatial and spectral information of hyperspectral images. CS-SMKL classifies the hyperspectral images, simultaneously learns class-specific significant features, and selects class-specific weights. Using an $L_{1}$-norm constraint (i.e., group lasso) as the regularizer, we can enforce the sparsity at the group/feature level and automatically learn a compact feature set for the classification of any two classes. More precisely, our CS-SMKL determines the associated weights of optimal base kernels for any two classes and results in improved classification performances. The advantage of the proposed method is that only the features useful for the classification of any two classes can be retained, which leads to greatly enhanced discriminability. Experiments are conducted on three hyperspectral data sets. The experimental results show that the proposed method achieves better performances for hyperspectral image classification compared with several state-of-the-art algorithms, and the results confirm the capability of the method in selecting the useful features.
Pansharpening with a decision fusion based on the local size information

Pansharpening may be defined as the process of synthesizing multispectral images at a higher spatial resolution. Different pansharpening methods produce images with different characteristics. In the 2006  and Context Adaptive (CBD) pansharpening methods were declared as joint winners. While assessing the quantitative quality of the pansharpened images, it was observed that the two methods outperform each other depending upon the local content of the scene. Hence, it is interesting to develop a method which could produce results locally approximately similar to the best method, among the two pansharpening methods. In this paper we propose a method which selects either of the two methods for performing pansharpening on local regions, based upon the size of the objects. The results obtained demonstrate that the proposed method produces images with quantitative results approximately similar to the method which is better among the AWLP and CBD pansharpening methods.
Using QNR index as decision criteria for improving fusion quality

Pansharpening is the process of improving spatial resolution of multispectral (MS) satellite images using the spatial details of a high resolution panchromatic (PAN) image. Quality without reference (QNR) index is a tool which can be used to determine the global quality of the fused images without requiring a high resolution reference MS image. Over the years different pansharpening methods have been proposed using different criteria to obtain improved fusion quality. Since QNR can be used to determine the quality of fused images at full resolution without requiring a reference high resolution MS image, it may be used as a decision criterion to select the best fused regions, given a number of different fused images. In this paper we propose to use the QNR index to determine the local fusion quality. We also extend this property of local quality assessment to select regions among various fused images of the same region to obtain a single image with the best quantitative measures. The proposed decision fusion is tested on Pleiades and Quickbird satellite images fused by recently proposed fusion methods.
A Physics-Based Unmixing Method to Estimate Subpixel Temperatures on Mixed Pixels

This paper presents a new algorithm for the analysis of linear spectral mixtures in the thermal infrared domain, with the goal to jointly estimate the abundance and the subpixel temperature in a mixed pixel, i.e., to estimate the relative proportion and the temperature of each material composing the mixed pixel. This novel approach is a two-step procedure. First, it estimates the emissivity and the temperature over pure pixels using the standard temperature and emissivity separation (TES) algorithm. Second, it estimates the abundance and the subpixel temperature using a new unmixing physics-based model, called Thermal Remote sensing Unmixing for Subpixel Temperature (TRUST). This model is based on an estimator of the subpixel temperature obtained by linearizing the black body law around the mean temperature of each material. The abundance is then retrieved by minimizing the reconstruction error with the estimation of the subpixel temperatures. The TRUST method is benchmarked on simulated scenes against the fully constrained least squares unmixing applied on the radiance and on the estimation of surface emissivity using the TES algorithm. The TRUST method shows better results on pure and mixed pixels composed of two materials. TRUST also shows promising results when applied on thermal hyperspectral data acquired with the Thermal Airborne Spectrographic Imager during the Detection in Urban scenario using Combined Airborne imaging Sensors campaign and estimates coherent localization of mixed-pixel areas.
Pansharpening Quality Assessment Using the Modulation Transfer Functions of Instruments

Quality assessment of pansharpening methods is not an easy task. Quality-assessment indexes, like Q4, spectral angle mapper, and relative global synthesis error, require a reference image at the same resolution as the fused image. In the absence of such a reference image, the quality of pansharpening is assessed at a degraded resolution only. The recently proposed index of Quality Not requiring a Reference (QNR) is one among very few tools available for assessing the quality of pansharpened images at the desired high resolution. However, it would be desirable to cross the outcomes of several independent quality-assessment indexes, in order to better determine the quality of pansharpened images. In this paper, we propose a method to assess fusion quality at the highest resolution, without requiring a high-resolution reference image. The novel method makes use of digital filters matching the modulation transfer functions (MTFs) of the imaging-instrument channels. Spectral quality is evaluated according to Wald's spectral consistency property. Spatial quality measures interscale changes by matching spatial details, extracted from the multispectral bands and from the panchromatic image by means of the high-pass complement of MTF filters. Eventually, we highlight the necessary and sufficient condition criteria for quality-assessment indexes by developing a pansharpening method optimizing the QNR spatial index and assessing the quality of fused images by using the proposed protocol.
Pansharpening using total variation regularization

In remote sensing, pansharpening refers to the technique that combines the complementary spectral and spatial resolution characteristics of a multispectral image and a panchromatic image, with the objective to generate a high-resolution color image. This paper presents a new pansharpening method based on the minimization of a variant of total variation. We consider the fusion problem as the colorization of each pixel in the panchromatic image. A new term concerning the gradient of the panchromatic image is introduced in the functional of total variation so as to preserve edges. Experimental results on IKONOS satellite images demonstrate the effectiveness of the proposed method.
Simultaneous group and phase correction for the estimation of dispersive propagating waves in the time-frequency plane

The paper deals with seismic signal processing. We propose a new algorithm which performs the dispersion filter estimation using a linear array of geophones. This estimation enables a robust characterization and extraction of the dispersive propagating waves from a seismic profile. The novelty of our method is the use of a simultaneous time delay and phase shift correction in order to estimate the dispersion filter. The resulting algorithm is semi-automatic and requires very few input parameters. The main advantage of our algorithm is that the signals are analysed in the time-frequency domain using all sensors simultaneously while classical approaches are either multi-sensor in the frequency domain or single sensor in the time-frequency domain. Validations on synthetic and real data show the reliability of the algorithm.
Nonlinear Unmixing of Hyperspectral Data Using Semi-Nonnegative Matrix Factorization

Nonlinear spectral mixture models have recently received particular attention in hyperspectral image processing. In this paper, we present a novel optimization method of nonlinear unmixing based on a generalized bilinear model (GBM), which considers the second-order scattering of photons in a spectral mixture model. Semi-nonnegative matrix factorization (semi-NMF) is used for the optimization to process a whole image in matrix form. When endmember spectra are given, the optimization of abundance and interaction abundance fractions converge to a local optimum by alternating update rules with simple implementation. The proposed method is evaluated using synthetic datasets considering its robustness for the accuracy of endmember extraction and spectral complexity, and shows smaller errors in abundance fractions rather than conventional methods. GBM-based unmixing using semi-NMF is applied to the analysis of an airborne hyperspectral image taken over an agricultural field with many endmembers, and it visualizes the impact of a nonlinear interaction on abundance maps at reasonable computational cost.
Support Vector Reduction in SVM Algorithm for Abrupt Change Detection in Remote Sensing

Satellite imagery classification using the support vector machine (SVM) algorithm may be a time-consuming task. This may lead to unacceptable performances for risk management applications that are very time constrained. Hence, methods for accelerating the SVM classification are mandatory. From the SVM decision function, it can be noted that the classification time is proportional to the number of support vectors (SVs) in the nonlinear case. In this letter, four different algorithms for reducing the number of SVs are proposed. The algorithms have been tested in the frame of a change detection application, which corresponds to a change-versus-no-change classification problem, based on a set of generic change criteria extracted from different combinations of remote sensing imagery.
Combining optical and SAR data to monitor temperate glaciers

Monitoring temperate glaciers activity becomes more and more necessary for economical and security reasons and as an indicator of the local effects of global changes. This paper presents the beginning of a three year project which regroups four laboratories to develop and combine specific methods to extract information from optical and radar remote sensing data. Preliminary results are presented on three different information sources: airborne photography, space-borne multi-spectral images and SAR interferometry, which respectively allow the compution of high resolution DTM, the detection of glacial lakes and the measurement of glacier surface velocity. Results obtained on two glaciers located in the French Alps are compared and validated with ground measurments.