nils-herrmann/clean_scirepeval huggingface dataset:
Processing Multidimensional SAR and Hyperspectral Images With Binary Partition Tree

The current increase of spatial as well as spectral resolutions of modern remote sensing sensors represents a real opportunity for many practical applications but also generates important challenges in terms of image processing. In particular, the spatial correlation between pixels and/or the spectral correlation between spectral bands of a given pixel cannot be ignored. The traditional pixel-based representation of images does not facilitate the handling of these correlations. In this paper, we discuss the interest of a particular hierarchical region-based representation of images based on binary partition tree (BPT). This representation approach is very flexible as it can be applied to any type of image. Here both optical and radar images will be discussed. Moreover, once the image representation is computed, it can be used for many different applications. Filtering, segmentation, and classification will be detailed in this paper. In all cases, the interest of the BPT representation over the classical pixel-based representation will be highlighted.
Retrieval of the Height of Buildings From WorldView-2 Multi-Angular Imagery Using Attribute Filters and Geometric Invariant Moments

This paper proposes a novel approach to the retrieval of buildings' height from multi-angular high spatial resolution images. To achieve this task, we combined two main concepts: multilevel morphological attribute filters, used for the definition of the objects in the image, and geometric invariant moments exploited for the characterization of the spatial properties of the previously detected shapes. The main concept of this study relies on the spatial properties of very high resolution images acquired from different angles of view. In particular, if we model the urban environment as an ensemble of vertical and horizontal surfaces, we can assume that the shapes related to the horizontal surfaces (i.e. the top of the buildings) do not suffer any relevant spatial distortion if detected from two angles of view, while vertical surfaces present strong changes in shape. Starting from this assumption, once each shape in each angular images has been spatially characterized, it is possible to identify univocally the same horizontal surface (i.e. the roof of a building) in each angular image. Finally, the knowledge of the acquisition angles permits the retrieval of the buildings height using simple trigonometric calculations. In this paper the proposed approach has been successfully applied to a WorldView-2 (WV2) very high resolution dataset composed by five angular images.
Impact of hybrid pansharpening approaches applied to hyperspectral images

Pansharpening techniques can be divided into component substitution (CS) and multi-resolution analysis (MRA) based methods. Generally, the CS methods result in fused images having high spatial quality but the fused images suffer from spectral distortions. On the other hand, images obtained using MRA techniques are not as sharp as CS methods but they are spectrally consistent. Both substitution and filtering approaches are considered adequate when applied to multi-spectral and PAN images, but have many drawbacks when the low-resolution image is a hyperspectral image. Based on these findings, the use of a hybrid approach, combining the better spatial information of CS and the more accurate spectral information of MRA techniques, may result in an improvement in terms of spectral quality, spatial sharpness as well as computational time.
Scalar image processing filters for speckle reduction on synthetic aperture sonar images

High resolution images provided by synthetic aperture sonar (SAS) sensors are of great interest, especially for the detection, location and classification of mines lying on the sea bed. But these data obtained by an active imagery system are highly corrupted by a noise called the speckle. To reduce this noise and suppress the spurious reflections it generates on the images, we propose to use different algorithms based on image processing techniques. Several image processing filters are tested and discussed. A comparative study of the results obtained on real SAS data is presented. The different filters are evaluated both visually and in terms of variance reduction.
Morphological and statistical approaches to improve detection in the presence of reverberation

The detection of a target echo in a sonar image is usually a difficult task since the reverberation, consisting of a large number of spurious echoes, generates a lot of false alarms. In this paper, we propose two new detectors derived from image processing algorithms. These detectors are respectively based on a morphological and a statistical contrast. Each detector only requires setting a few parameters. This setting is done using some prior knowledge about the data (shape of the emitted signal and the used antenna, characteristics of the reverberation). Nevertheless, an extensive statistical study of the detection performances proves that the proposed methods are robust and that even an imprecise setting of the parameters leads to satisfactory results. Applied to the real data, these detectors and their sequential combination lead to a significant improvement on the performances: The false alarm rate is drastically reduced while the detection probability is preserved. Based on different contrasts, these detectors have complementary behaviors. Therefore, a further improvement is achieved by a fusion of the different results to classify the remaining echoes as whether spurious or true detection
Ensemble Methods for Classification of Hyperspectral Data

The classification of hyperspectral data is addressed using a classifier ensemble based on Support Vector Machines (SVM). First of all, the hyperspectral data set is decomposed into few sources according to the spectral bands correlation. Then, each source is treated separately and classified by an SVM classifier. Finally, all outputs are used as inputs for the final decision fusion, performed by an additional SVM classifier. The results of experiments, clearly show that the proposed SVM-based decision fusion outperforms a single SVM classifier in terms of overall accuracies.
Fusion of local statistical detectors in SAS imagery classification

Detection of underwater mines is a present crucial strategic task. The images provided by synthetic aperture sonar (SAS) are then of great interest for the detection and classification of objects lying on the sea floor or buried in the sea bed. This paper proposes a detection method based on data fusion, using local statistical characteristics extracted from the SAS data. These values come from first, second, third, and fourth order statistical properties of the sonar images.
Kernel principal component analysis for the construction of the extended morphological profile

Kernel Principal Component Analysis (KPCA) is investigated for feature extraction from hyperspectral remote-sensing data. Features extracted using KPCA are used to construct the Extended Morphological Profile (EMP). Classification results, in terms of accuracy, are improved in comparison to original approach which used conventional principal component analysis for constructing the EMP. Experimental results presented in this paper confirm the usefulness of the KPCA for the analysis of hyperspectral data. The overall classification accuracy increases from 79% to 96% with the proposed approach.
Dispersion estimation from linear array data in the time-frequency plane

We consider the problem of estimating the dispersion of a wave field from data recorded by a linear array of geophones. The fact that the data we are looking at may contain several propagating waves make this even more challenging. In this paper, a new algorithm is proposed to solve this issue. Currently, there are two methods for estimating wave dispersion described in the literature. The first method estimates the group delay function from the time-frequency representation (TFR) of each sensor separately. It is efficient as long as the patterns of the different waves do not overlap in the time-frequency plane. The second method estimates the dispersion from the two-dimensional (2-D) Fourier transform of the profile (or more generally from a velocity-frequency representation). This assumes that the dispersion is constant along the entire sensor array. It is efficient as long as the patterns of the waves do not overlap in the frequency domain. Our method can be thought of as a hybrid of the above two methods as it is based on the construction of a TFR where the energy of waves that propagate at a selected velocity are amplified. The primary advantage of our algorithm is the use of the velocity variable to separate the patterns of the propagating waves in the time-frequency plane. When applied to both synthetic and real data, this new algorithm gives much improved results when compared with other standard methods.
Evaluation of ICA-Based ICTD for PolSAR Data Analysis Using a Sliding Window Approach: Convergence Rate, Gaussian Sources, and Spatial Correlation

Polarimetric incoherent target decomposition aims at accessing physical parameters of illuminated scatters through the analysis of the target coherence or covariance matrix. In this framework, independent component analysis (ICA) was recently proposed as an alternative method to eigenvector decomposition to better interpret non-Gaussian heterogeneous clutter (inherent to high-resolution synthetic aperture radar systems). Until now, the two main drawbacks reported of the aforementioned method are the greater number of samples required for an unbiased estimation, when compared to the classical eigenvector decomposition, and the inability to be employed in scenarios under the Gaussian clutter assumption. In this paper, both drawbacks are analyzed. First, a Monte Carlo approach is performed in order to investigate the bias in estimating Touzi's target-scattering-vector-model parameters when ICA is employed. Simulated data and a RAMSES X-band image acquired over Brétigny, France, are taken into consideration to investigate the bias estimation under different scenarios. Finally, the performance of the algorithm is also evaluated under the Gaussian clutter assumption and when spatial correlation is introduced in the model.
A graph-based approach for feature extraction and segmentation of multimodal images

In the past few years, graph-based methods have proven to be a useful tool in a wide variety of energy minimization problems . In this paper, we propose a graph-based algorithm for feature extraction and segmentation of multimodal images. By defining a notion of similarity that integrates information from each modality, we merge the different sources at the data level. The graph Laplacian then allows us to perform feature extraction and segmentation on the fused dataset. We apply this method in a practical example, namely the segmentation of optical and lidar images. The results obtained confirm the potential of the proposed method.
Crop Yield Estimation Based on Unsupervised Linear Unmixing of Multidate Hyperspectral Imagery

Hyperspectral imagery, which contains hundreds of spectral bands, has the potential to better describe the biological and chemical attributes on the plants than multispectral imagery and has been evaluated in this paper for the purpose of crop yield estimation. The spectrum of each pixel in a hyperspectral image is considered as a linear combinations of the spectra of the vegetation and the bare soil. Recently developed linear unmixing approaches are evaluated in this paper, which automatically extracts the spectra of the vegetation and bare soil from the images. The vegetation abundances are then computed based on the extracted spectra. In order to reduce the influences of this uncertainty and obtain a robust estimation results, the vegetation abundances extracted on two different dates on the same fields are then combined. The experiments are carried on the multidate hyperspectral images taken from two grain sorghum fields. The results show that the correlation coefficients between the vegetation abundances obtained by unsupervised linear unmixing approaches are as good as the results obtained by supervised methods, where the spectra of the vegetation and bare soil are measured in the laboratory. In addition, the combination of vegetation abundances extracted on different dates can improve the correlations (from 0.6 to 0.7).
Classification of hyperspectral data from urban areas using morphological preprocessing and independent component analysis

Classification of high-resolution hyperspectral data is investigated. Previously, in classification of high-resolution panchromatic data, simple morphological profiles have been constructed with a repeated use of morphological opening and closing operators with a structuring element of increasing size, starting with the original panchromatic image. This approach has recently been extended for hyperspectral data. In the extension, principal components of the hyperspectral imagery have been computed in order to produce an extended morphological profile. In this paper, we investigate the use of independent components instead of principal components in extended morphological profiles, i.e., selected independent components are used as base images for an extended morphological profile. In the proposed approach, the extended morphological profiles based on the independent components are used as inputs to a neural network classifier. In experiments, a hyperspectral data sets from an urban area in Pavia, Italy is classified.
Evaluation of ICA based ICTD for PolSAR data analysis in tropical forest scenario

The Independent Component Analysis (ICA) aims, based on higher order statistical moments, in recovering statistical independent sources and the mixing mechanism, without having any physical background of the latter. Recently proposed as an alternative to Eigenvector decomposition in the analysis of Polarimetric SAR (PolSAR) data, it proved itself to be a very promising tool to better interpret non-Gaussian heterogeneous clutter, being employed in both urban area analysis as well as in snow monitoring applications. In this paper we intend to extend the range of applications of ICA based ICTD by investigating the results and the algorithm performance under tropical forest scenarios. Data from the P-band airborne dataset 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 is taken into consideration to analyse the potential of supplementary information introduced by the ICA approach.
Hyperspectral pansharpening using QNR optimization constraint

This paper presents a method for pansharpening of low resolution Hyperspectral (HS) images. The proposed method is based upon the optimization of both the spectral and spatial quality criteria of the QNR quality assessment index. The simultaneous optimization of the spectral and spatial quality constraints is obtained by means of the Pareto solutions, obtained by making use of an evolutionary algorithm. A selection criteria is defined to select a single solution from among the Pareto solutions and the results obtained show both quantitative and qualitative improvement over the results obtained by some existing pansharpening methods.
Gas plume detection and tracking in hyperspectral video sequences using Binary Partition Trees

Thanks to the fast development of sensors, it is now possible to acquire sequences of hyperspectral images. Those hyperspectral video sequences are particularly suited for the detection and tracking of chemical gas plumes. However, the processing of this new type of video sequences with the additional spectral diversity, is challenging and requires the design of advanced image processing algorithms. In this paper, we present a novel method for the segmentation and tracking of a chemical gas plume diffusing in the atmosphere, recorded in a hyperspectral video sequence. In the proposed framework, the position of the plume is first estimated, using the temporal redundancy of two consecutive frames. Second, a Binary Partition Tree is built and pruned according to the previous estimate, in order to retrieve the real location and extent of the plume in the frame. The proposed method is validated on a real hyperspectral video sequence and compared with a state-of-the-art method.
A comparison between real and complex Schott spherical symmetry test for PolSAR data analysis

Most of the tests proposed in the literature to verify if a given random multivariate dataset fits a spherical or elliptical distribution are designed for real valued data and rely on the estimation of high order moment matrices. Recently, a test that considers complex random vectors, derived based on the Schott spherical symmetry test was proposed aiming in a more proper analysis of PolSAR data. Results showed its effectiveness in discriminating data that fits or not the complex spherically invariant random vector model (product model), inherent to high resolution heterogeneous PolSAR systems. Within this context, this paper further extends the assessment of the referred test efficiency, verifying its performance under different stochastic model assumptions and comparing the results with the ones achieved when the Schott test derived for real random vectors is employed.
Binary partition tree as a hyperspectral segmentation tool for tropical rainforests

Individual tree crown delineation in tropical forests is of great interest for ecological applications. In this paper we propose a method for hyperspectral image segmentation based on binary tree partitioning. The initial partition is obtained from a watershed transformation in order to make the method computationally more efficient. Then we use a non-parametric region model based on histograms to characterize the regions and the diffusion distance to define the region merging order. The pruning strategy is based on the discontinuity of size increment observed when iteratively merging the regions. The segmentation quality is assessed visually and appears to perform well on most cases, but tree delineation could be improved by including structural information derived from LiDAR data.
Variability of the endmembers in spectral unmixing: Recent advances

Endmember variability has been identified as one of the main limitations of the usual Linear Mixing Model, conventionally used to perform spectral unmixing of hyperspectral data. The topic is currently receiving a lot of attention from the community, and many new algorithms have recently been developed to model this variability and take it into account. In this paper, we review state of the art methods dealing with this problem and classify them into three categories: the algorithms based on endmember bundles, the ones based on computational models, and the ones based on parametric physics-based models. We discuss the advantages and drawbacks of each category of methods and list some open problems and current challenges.
Abrupt change detection on multitemporal remote sensing images: a statistical overview of methodologies applied on real cases

In the framework of the International Charter "Space and Major Disasters", charter calls are made to the signing parties every time a natural or technological hazard occurs. Consequently, space data are provided by the partners in order to help local authorities to assess the damages, organize and optimize the use of available resources. In such cases, abrupt change detection algorithms are required and numerous methods have been proposed by the geoscience and remote sensing (GRS) community. In this paper statistics measured on charter calls are compared to statistics measured on change detection methods published in the literature. These statistics aim to give the image processing community a better understanding of the needs and challenges faced in the case of real life disaster scenarios.
Indusion: Fusion of Multispectral and Panchromatic Images Using the Induction Scaling Technique

The fusion of multispectral (MS) and panchromatic (PAN) images is a useful technique for enhancing the spatial quality of low-resolution MS images. Liu recently proposed the smoothing-filter-based intensity modulation (SFIM) fusion technique. This technique upscales MS images using bicubic interpolation and introduces high-frequency information of the PAN image into the MS images. However, this fusion technique is plagued by blurred edges if the upscaled MS images are not accurately coregistered with the PAN image. In the first part of this letter, we propose the use of the Induction scaling technique instead of bicubic interpolation to obtain sharper, better correlated, and hence better coregistered upscaled images. In the second part, we propose a new fusion technique derived from induction, which is named ldquoIndusion.rdquo In this method, the high-frequency content of the PAN image is extracted using a pair of upscaling and downscaling filters. It is then added to an upscaled MS image. Finally, a comparison of SFIM (with both bicubic interpolation and induction scaling) is presented along with the fusion results obtained by IHS, discrete wavelet transform, and the proposed Indusion techniques using Quickbird satellite images.
Using Multiple Subpixel Shifted Images With Spatial–Spectral Information in Soft-Then-Hard Subpixel Mapping

Multiple subpixel shifted images (MSIs) from the same area can be incorporated to improve the accuracy of soft-then-hard subpixel mapping (STHSPM). In this paper, a novel method that derives higher resolution MSIs with more spatial–spectral information (MSI-SS) is proposed. First, coarse MSIs produce two high-resolution MSIs for each class respectively by two paths at the same time. The spatial path produces the high-resolution MSIs by soft classification followed by interpolation. And, the other high-resolution MSIs are derived from the spectral path by interpolation followed by soft classification. Then the higher resolution MSIs with more spatial–spectral information for each class are derived by integrating the aforementioned two kinds of high-resolution MSIs by the appropriate weight. Finally, the integrated higher resolution MSIs for each class are used to allocate hard class labels to subpixels. The proposed method is fast and takes more spatial–spectral information of the original MSIs into account. Experiments on three real hyperspectral remote sensing images show that the proposed method produce higher SPM accuracy result.
Spatio-temporal cellular automata-based filtering for image sequence denoising: Application to fluoroscopic sequences

This work presents a novel spatio-temporal cellular automata-based filtering (STCAF) for image sequence denoising. Most of the methods using cellular automata (CA) for image denoising involve the manual design of the rules that define the behaviour of the automata. This is a complex and not straightforward operation. In order to tackle this problem, this paper proposes to use evolutionary methods to obtain the CA set of rules which produces the best possible denoising under different noise models or/and image sources. This is implemented using a spatio-temporal neighbourhood for each pixel, which significantly improves the results with respect to simple spatio or temporal set of neighbours. The proposed method is tested to reduce the noise in low-dose X-ray image sequences. These data have a severe signal-dependent noise that must be reduced avoiding artifacts while preserving structures of interest for a medical inspection. The proposed method outperforms several state-of-the-art algorithms on both simulated and real sequences.