nils-herrmann/clean_scirepeval huggingface dataset:
Combining Morphological Attribute Profiles via an Ensemble Method for Hyperspectral Image Classification

Morphological attribute profiles (APs) are discriminant features in the spectral-spatial classification of hyperspectral data. However, the optimal range of parameters in each filter is always a challenging yet important task, since an unsuitable range of parameters likely leads to inferior results. In order to alleviate this problem, we propose an ensemble method, which integrates multiple classification results based on a series of APs. The APs are obtained by using different filters with thresholds that are randomly selected from an arbitrarily defined range of parameters. Experimental results conducted on two hyperspectral images demonstrate the robustness and effectiveness of the proposed method.
Combining Rotation Forest and Multiscale Segmentation for the Classification of Hyperspectral Data

In order to tackle two key issues, e.g., high-accuracy classifier and spatial information model, in the classification of hyperspectral images, a new classification scheme, which combines two powerful techniques, i.e., rotation forest (RoF) and multiscale (MS) segmentation, is proposed in this paper. MS segmentation is used to obtain the spatial information from different levels. Then, the objects produced by MS segmentation are treated as the input of the RoF classifier. Furthermore, multiple classification results generated by the RoF classifiers and MS segmentation are combined using a majority voting rule to generate the final result. Experimental results on two real hyperspectral datasets demonstrate that the proposed method performs particularly well in terms of overall and class-specific accuracies and generates the classification map with much more homogeneous regions than traditional methods. Moreover, the impacts of parameters on the classification performances are also analyzed.
3D snakes for the segmentation of buried mines in 3D acoustic images

In this paper, we describe some image processing techniques for the analysis of 3D acoustical data. More specifically, the 3D images are segmented using a deformable template (3D snake). This iterative algorithm provides a triangulated surface of the echo generated by buried underwater mines. The segmentation result can then be used for recognition/classification of the detected object purpose. The proposed algorithm consists in iteratively deforming a triangulated 3D surface until it fits the shape and boundaries of the object of interest. In the first section, we briefly review the classical technique for the segmentation and reconstruction of volumetric data (marching cube algorithm, enabling the fast extraction of a triangulated model of an object). Then, the proposed deformable model is described. Results obtained on real data sets provided by the GESMA are presented, demonstrating the interest of 3D deformable models for the analysis of 3D acoustical images.
Collaborative total variation for hyperspectral pansharpening

Variational methods are widely used in image processing for problems ranging from denoising to data fusion. In this paper we focus on a recent regularization method, called Collaborative Total Variation, applied to the hyperspectral pansharpening, which deals with the fusion of low resolution hyperspectral and high resolution panchromatic images. The effectiveness of this novel approach is evaluated for different Collaborative Norms and the assessment is performed on the Pavia University dataset.
Random Subspace Ensembles for Hyperspectral Image Classification With Extended Morphological Attribute Profiles

Classification is one of the most important techniques to the analysis of hyperspectral remote sensing images. Nonetheless, there are many challenging problems arising in this task. Two common issues are the curse of dimensionality and the spatial information modeling. In this paper, we present a new general framework to train series of effective classifiers with spatial information for classifying hyperspectral data. The proposed framework is based on the two key observations: 1) the curse of dimensionality and the high feature-to-instance ratio can be alleviated by using random subspace (RS) ensembles; and 2) the spatial-contextual information is modeled by the extended multiattribute profiles (EMAPs). Two fast learning algorithms, i.e., decision tree (DT) and extreme learning machine (ELM), are selected as the base classifiers. Six RS ensemble methods, namely, RS with DT, random forest (RF), rotation forest, rotation RF (RoRF), RS with ELM (RSELM), and rotation subspace with ELM (RoELM), are constructed by the multiple base learners. Experimental results on both simulated and real hyperspectral data verify the effectiveness of the RS ensemble methods for the classification of both spectral and spatial information (EMAPs). On the University of Pavia Reflective Optics Spectrographic Imaging System image, our proposed approaches, i.e., both RSELM and RoELM with EMAPs, achieve the state-of-the-art performances, which demonstrates the advantage of the proposed methods. The key parameters in RS ensembles and the computational complexity are also investigated in this paper.
Soft-Then-Hard Subpixel Land Cover Mapping Based on Spatial-Spectral Interpolation

In this letter, a novel subpixel sharpening for soft-then-hard subpixel mapping (SPM) is proposed. First, the fractional images for each class are, respectively, derived by spectral unmixing followed by spatial interpolation and by spectral interpolation followed by spectral unmixing. Bilinear and bicubic interpolation is used as the spatial and spectral interpolation methods. The fractional images for each class are then integrated together using the appropriate weighting parameter. Finally, the integrated finer fractional images are used to allocate hard class labels to subpixels. The proposed method is fast and does not need any prior spatial structure information. Experiments on two actual hyperspectral images show that the proposed method produces higher accuracy results than the existing algorithms. Moreover, both the spatial and spectral information is fully utilized to improve the accuracy of the SPM results.
Comparison of Pansharpening Algorithms: Outcome of the 2006 GRS-S Data-Fusion Contest

In January 2006, the Data Fusion Committee of the IEEE Geoscience and Remote Sensing Society launched a public contest for pansharpening algorithms, which aimed to identify the ones that perform best. Seven research groups worldwide participated in the contest, testing eight algorithms following different philosophies [component substitution, multiresolution analysis (MRA), detail injection, etc.]. Several complete data sets from two different sensors, namely, QuickBird and simulated Pleiades, were delivered to all participants. The fusion results were collected and evaluated, both visually and objectively. Quantitative results of pansharpening were possible owing to the availability of reference originals obtained either by simulating the data collected from the satellite sensor by means of higher resolution data from an airborne platform, in the case of the Pleiades data, or by first degrading all the available data to a coarser resolution and saving the original as the reference, in the case of the QuickBird data. The evaluation results were presented during the special session on data fusion at the 2006 international geoscience and remote sensing symposium in Denver, and these are discussed in further detail in this paper. Two algorithms outperform all the others, the visual analysis being confirmed by the quantitative evaluation. These two methods share the same philosophy: they basically rely on MRA and employ adaptive models for the injection of high-pass details.
Adaptive anisotropic diffusion for speckle filtering in SAS imagery

Thanks to their high resolution, the images provided by synthetic aperture sonar (SAS) are of great interest for the detection and classification of underwater mines lying on the sea bed or buried in the sea floor. As with any acoustic imaging systems, SAS data are highly corrupted by a multiplicative noise, called the speckle, which can be very disturbing for the interpretation and the automatic analysis of the images. In order to reduce this noise and smooth the data, the properties of an anisotropic diffusion filter (ADF) are interesting. In this paper, we propose to adapt the setting of this filter parameters thanks the knowledge provided by an other process. This allows to smooth the speckle noise in the background, while the regions likely to contain any object of interest are preserved.
(Semi-) Supervised Probabilistic Principal Component Analysis for Hyperspectral Remote Sensing Image Classification

In this paper, we have applied supervised probabilistic principal component analysis (SPPCA) and semi-supervised probabilistic principal component analysis (S2PPCA) for feature extraction in hyperspectral remote sensing imagery. The two models are all based on probabilistic principal component analysis (PPCA) using EM learning algorithm. SPPCA only relies on the labeled samples into the projection phase, while S2PPCA is able to incorporate both the labeled and unlabeled information. Experimental results on three real hyperspectral images demonstrate the SPPCA and S2PPCA outperform some conventional feature extraction methods for classifying hyperspectral remote sensing image with low computational complexity.
Foreword to the Special Issue on Hyperspectral Remote Sensing and Imaging Spectroscopy

The twenty six papers in this special issue focus on the technologies of hyperspectral remote sensing (HRS)and imaging spectroscopy. HRS has emerged as a powerful tool to understand phenomena at local and global scales by virtue of imaging through a diverse range of platforms, including terrestrial in-situ imaging platforms, unmanned and manned aerial vehicles, and satellite platforms. By virtue of imaging over a wide range of spectral wavelengths, it is possible to characterize object specific properties very accurately. As a result, hyperspectral imaging (also known as imaging spectroscopy) has gained popularity for a wide variety of applications, including environment monitoring, precision agriculture, mineralogy, forestry, urban planning, and defense applications. The increased analysis capability comes at a cost—there are a variety of challenges that must be overcome for robust image analysis of such data, including high dimensionality, limited sample size for training supervised models, noise and atmospheric affects, mixed pixels, etc. The papers in this issue represent some of the recent developments in image analysis algorithms and unique applications of hyperspectral imaging data.
Fusion of Morphological and Spectral Information for Classification of Hyperspectal Urban Remote Sensing Data

Classification of hyperspectral data with high spatial resolution from urban areas is discussed. An approach has been proposed which is based on using several principal components from the hyperspectral data and build morphological profiles. These profiles are used all together in one extended morphological profile, which is then classified by a neural network. A shortcoming of the approach is that it is primarily designed for classification of urban structures and it does not fully utilize the spectral information in the data. An extension is proposed in this paper in order to overcome the problems with the extended morphological profile approach. The proposed method is based on the data fusion of the morphological information and the original data. The proposed approach is tested in experiments on two different high resolution remote sensing data sets from urban areas. The results are excellent and significant improvements are achieved in terms of accuracies when compared to results of approaches based on the use of morphological profiles based on PCs only and conventional statistical approaches.
Decision-Based Fusion for Pansharpening of Remote Sensing Images

Pansharpening may be defined as the process of synthesizing multispectral images at a higher spatial resolution. A wide range of pansharpening methods are available, each producing images with different characteristics. To compare the performances and characteristics of different methods, a contest was held in 2006 by the IEEE Data Fusion Technical Committee. In this contest, À trous wavelet transform-based pansharpening (AWLP) and Laplacian pyramid-based context adaptive (CBD) pansharpening methods were declared as joint winners. While assessing the quantitative quality of the pansharpened images, we observed that the two methods outperform each other depending upon the local content of the scene. Hence, it is interesting to design a method taking advantage of both methods by locally selecting the best one. This adaptive decision fusion is performed based on the local scale of the structure. The interest of the proposed method is verified using both visual and quantitative analyses for different Pléiades data sets.
Foreword to the Special Issue on Hyperspectral Remote Sensing and Imaging Spectroscopy

HYPERSPECTRAL remote sensing has emerged as a powerful tool to understand phenomena at local and global scales by virtue of imaging through a diverse range of platforms, including terrestrial in-situ imaging platforms, unmanned and manned aerial vehicles, and satellite platforms. By virtue of imaging over a wide range of spectral wavelengths, it is possible to characterize object specific properties very accurately. As a result, hyperspectral imaging (also known as imaging spectroscopy) has gained popularity for a wide variety of applications, including environment monitoring, precision agriculture, mineralogy, forestry, urban planning, and defense applications. The increased analysis capability comes at a cost—there are a variety of challenges that must be overcome for robust image analysis of such data, including high dimensionality, limited sample size for training supervised models, noise and atmospheric affects, mixed pixels, etc. This special issue (SI) presents 26 papers that represent some of the recent developments in image analysis algorithms and unique applications of hyperspectral imaging data. Specifically, this SI represents the following broad topics. 1) Contemporary and emerging machine learning architectures for image analysis. 2) Advances in spectral unmixing for image analysis. 3) Real-time compression and compressive sensing. 4) Denoising. 5) Applications leveraging the information provided by hyperspectral earth observations. In Wang et al. a group low-rank nonnegative matrix factorization approach is proposed for spectral unmixing. In Zhang et al. image fusion of multispectral and hyperspectral imagery is undertaken via a spatial-spectral graph-regularized low-rank tensor decomposition. Al-Suwaidi et al. propose feature ensemble based novelty detection for analysis of plant hyperspectral datasets. Matteoli et al. present a target recognition approach within anomalous regions of interest in hyperspectral images. In Du et al. a low-rank matrix factorization based approach is paired with a band-specific noise model for hyperspectral denoising. In Zhang et al. cascaded random forests are proposed for hyperspectral image classification. In Yu et al. a mixed pixel hyperspectral classification approach is developed and presented. In Gan et al. a weighted kernel sparse representation model is developed for hyperspectral classification. Wu et al. present a GPU parallel implementation for hyperspectral image classification that utilizes spatial information. Liu et al. undertake hyperspectral classification via least-square support vector machines. Bascones et al. present an FPGA implementation for real-time hyperspectral lossless compression. Kang et al.
Kernel Principal Component Analysis for Feature Reduction in Hyperspectrale Images Analysis

Feature extraction of hyperspectral remote sensing data is investigated. Principal component analysis (PCA) has shown to be a good unsupervised feature extraction. On the other hand, this methods only focus on second orders statistics. By mapping the data onto another feature space and using nonlinear function, Kernel PCA (KPCA) can extract higher order statistics. Using kernel methods, all computation are done in the original space, thus saving computing time. In this paper, KPCA is used has a preprocessing step to extract relevant feature for classification and to prevent from the Hughes phenomenon. Then the classification was done with a back-propagation neural network on real hyperspectral ROSIS data from urban area. Results were positively compared to the linear version (PCA) and to a version of a algorithm specially designed to be use with neural network (DBFE)
Multiple Spectral–Spatial Classification Approach for Hyperspectral Data

A new multiple-classifier approach for spectral-spatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region with a corresponding class label. We propose to use spectral-spatial classifiers at the preliminary step of the marker-selection procedure, each of them combining the results of a pixelwise classification and a segmentation map. Different segmentation methods based on dissimilar principles lead to different classification results. Furthermore, a minimum spanning forest is built, where each tree is rooted on a classification-driven marker and forms a region in the spectral-spatial classification map. Experimental results are presented for two hyperspectral airborne images. The proposed method significantly improves classification accuracies when compared with previously proposed classification techniques.
Object Tracking by Hierarchical Decomposition of Hyperspectral Video Sequences: Application to Chemical Gas Plume Tracking

It is now possible to collect hyperspectral video sequences at a near real-time frame rate. The wealth of spectral, spatial, and temporal information of those sequences is appealing for various applications, but classical video processing techniques must be adapted to handle the high dimensionality and huge size of the data to process. In this paper, we introduce a novel method based on the hierarchical analysis of hyperspectral video sequences to perform object tracking. This latter operation is tackled as a sequential object detection process, conducted on the hierarchical representation of the hyperspectral video frames. We apply the proposed methodology to the chemical gas plume tracking scenario and compare its performances with state-of-the-art methods, for two real hyperspectral video sequences, and show that the proposed approach performs at least equally well.
QNR Optimization based Pansharpening

Quality Without Reference (QNR) index can be used to globally assess the quality of pansharpened images without the need of a reference high resolution multispectral (MS) image. The QNR index relies on local calculation of the Q4 index. Exploiting the local Q4 calculation property of the QNR index, we propose an injection model for pansharpening. The injection model determines the weight of extracted panchromatic (Pan) details that are to be added into the upscaled MS images to obtain the best QNR index. The QNR index calculates spectral distortion of the fused images with respect to the low resolution MS images and spatial distortion of the fused images with respect to the high resolution Pan image. Hence, the QNR optimized fused image is spectrally consistent with the low resolution MS image and spatially consistent with the high resolution Pan image.
Fluorosocopic sequence denoising using a motion compensated multi-scale temporal filtering

We propose a temporal motion-compensated filter operating on dual-tree complex wavelet coefficients to denoise low-dose X-ray sequences. This filter allows for a great noise reduction while preserving moving objects and structures. We take advantage of the properties of multi-scale spaces to perform a fast and robust motion tracking. The result of this step is then used in the temporal filter for denoising purpose. Quantitative and qualitative analysis on real fluoroscopic sequences show that our method outperforms state-of-the-art VBM3D method and allows for a dose reduction as high as 80%.
A Bayesian Procedure for Full-Resolution Quality Assessment of Pansharpened Products

Pansharpening regards the fusion of a high-spatial resolution panchromatic image with a low-spatial resolution multispectral image. One of the most debated topics about pansharpening is related to the quality assessment of fused products. Two main assessment procedures are usually exploited in the literature: the reduced resolution validation and the full-resolution (FR) validation. The former has the advantage to be accurate, but the hypothesis of invariance among scales has to be assumed. The latter overcomes this limitation but paying it with a lower accuracy. In this paper, we will focus on the FR assessment proposing an approach for estimating an overall quality index at FR by using multiscale FR measurements. The problem is recast into the sequential Bayesian framework exploiting a Kalman filter to find its solution. The proposed procedure for quality evaluation has been tested on four real data sets acquired by the Pléiades, the GeoEye-1, the WorldView-3, and the WorldView-4 sensors assessing the quality of 19 pansharpened methods. The proposed approach has demonstrated its superiority with respect to the benchmark consisting of state-of-the-art quality assessment procedures.
Spectral–Spatial Classification for Hyperspectral Data Using Rotation Forests With Local Feature Extraction and Markov Random Fields

In this paper, we propose a new spectral-spatial classification strategy to enhance the classification performances obtained on hyperspectral images by integrating rotation forests and Markov random fields (MRFs). First, rotation forests are performed to obtain the class probabilities based on spectral information. Rotation forests create diverse base learners using feature extraction and subset features. The feature set is randomly divided into several disjoint subsets; then, feature extraction is performed separately on each subset, and a new set of linear extracted features is obtained. The base learner is trained with this set. An ensemble of classifiers is constructed by repeating these steps several times. The weak classifier of hyperspectral data, classification and regression tree (CART), is selected as the base classifier because it is unstable, fast, and sensitive to rotations of the axes. In this case, small changes in the training data of CART lead to a large change in the results, generating high diversity within the ensemble. Four feature extraction methods, including principal component analysis (PCA), neighborhood preserving embedding (NPE), linear local tangent space alignment (LLTSA), and linearity preserving projection (LPP), are used in rotation forests. Second, spatial contextual information, which is modeled by MRF prior, is used to refine the classification results obtained from the rotation forests by solving a maximum a posteriori problem using the α-expansion graph cuts optimization method. Experimental results, conducted on three hyperspectral data with different resolutions and different contexts, reveal that rotation forest ensembles are competitive with other strong supervised classification methods, such as support vector machines. Rotation forests with local feature extraction methods, including NPE, LLTSA, and LPP, can lead to higher classification accuracies than that achieved by PCA. With the help of MRF, the proposed algorithms can improve the classification accuracies significantly, confirming the importance of spatial contextual information in hyperspectral spectral-spatial classification.