nils-herrmann/clean_scirepeval huggingface dataset:
Improved resolution for the detection of snow with MODIS using wavelet fusion

We propose to implement the ARSIS concept to fuse the high spatial content of the two 250 m spectral bands of MODIS into its five 500 m bands using wavelet based multi- resolution analysis. Our objective was to test the effectiveness of this technique to increase the accuracy of snow mapping in mountainous environments. To assess the performance of this approach, we took advantage of the simultaneity between ASTER and MODIS sensors, both on the TERRA platform. With a 15 m spatial resolution, the ASTER sensor provided reference snow maps that were then compared to MODIS derived snow maps. The benefit of the method was assessed through the investigation of various metrics. The enhanced snow map are therefore of great benefit for environmental and hydrological applications in steep terrain.
Pan-sharpening using induction

Pan-sharpening is the process of improving spatial resolution of multi-spectral (MS) satellite images using the spatial details of a high resolution panchromatic (PAN) image. Pan- sharpening can be divided into scaling and fusion processes. In the first part of this paper we use Induction instead of bicubic interpolation for up-scaling the MS images. SFIM (smoothing filter based intensity modulation) is used to obtain fused MS images for the two different scaling techniques. In the second part, "indusion", a new fusion technique, derived from Induction, is proposed. In this technique the high frequency content of the PAN image is extracted using a pair of up-scaling and down- scaling filters. It is then added to the up-scaled MS images. Finally a comparison of indusion with intensity, hue, saturation (IHS), discrete wavelet transform (DWT) and SFIM fusion techniques is presented for IKONOS satellite images.
A time-distributed phase space histogram for detecting transient signals

Burst-type signals constitute an important class of transient signals, being used especially in the investigation of various physical environments by electric or acoustic means. An important issue in the analysis of this type of signals is their detection in time. In this paper, we propose a detection method that is based on the histogram of the phase space distributed over time. The method consists in representing the analyzed signal in phase space and, then, quantifying the recurrences of the trajectory obtained in this space. In this way, we derive a time - recurrence radius representation for the signal, that allows identification of positions and durations of the transients. Afterwards, we propose a method to obtain a detection curve starting from this representation of the signal. We also present here some results concerning the performance of our method in the presence of noise on both synthetic and real signals.
Data Fusion Contest: Fusion of Panchromatic and Multispectral Images

The IEEE Data Fusion Technical Committee is organizing a “Data Fusion Contest” in conjunction with the IGARS Symposium. For the first issue of the contest, the fusion of multispectral and panchromatic images for optical very high resolution images is considered. A comparative study of the results provided by the participants on common data sets is presented during the conference in the “Data Fusion” session and the “winning” team(s) is (are) granted an IEEE award during the IEEE GRS-S Technical Committee and Chapter Dinner.
Foreword to the Special Issue on Hyperspectral Image and Signal Processing

This special issue presents advances in signal and image processing related to hyperspectral remote sensing (or imaging spectroscopy). We present the seventy-eight papers selected, which distill the state of the art and the advances in hyperspectral image analysis and its applications. This special issue of JSTARS follows the 6 th workshop on hyperspectral image and signal processing (WHISPERS) held in Lausanne (Switzerland) in June 2014. It contains both extended versions of contributions presented in WHISPERS and regular papers submitted to JSTARS dealing with contemporary aspects of hypersepctral image and signal processing. Main topics show continuity. At a first glimpse, the papers contained in this special issue maintain the methodological flavour of both WHISPERS and the previous special issues . The main topics remain classification, unmixing, spectral-spatial methods and sparse methods, as one can see in the word cloud extracted form the titles of the papers published in this issue . This is confirmed by the rough classification of the main topics 1 of the papers, which is reported in the top panel of Fig. 2: classification and unmixing are still the most present ones and, together with the connecting themes of data fusion and feature selection/extraction, cover almost 3/4 of the keywords. However, the rising themes of high performance computing, regression and sparse processing seem to make their way into the favourite topics represented and will surely increase their presence in the future. Hence, the hypersepctral image/signal processing community seems to stick to its favourite themes, while enlarging its vision thanks to the advances in computing power [4] and availability of data . New applications are being tackled, but is it fast enough? From the application summary in the bottom panel of Fig. 2, one can observe that roughly 50% of the papers in the issue validate the proposed methodology on benchmark datasets (e.g. 1 Each paper was assigned to a number of subtopics that matched with its content.
A class of robust estimates for detection in hyperspectral images using elliptical distributions background

When dealing with impulsive background echoes, Gaussian model is no longer pertinent. We study in this paper the class of elliptically contoured (EC) distributions. They provide a multivariate location-scatter family of distributions that primarily serve as long tailed alternatives to the multivariate normal model. They are proven to represent a more accurate characterization of HSI data than models based on the multivariate Gaussian assumption. For data in ℝk, robust proposals for the sample covariance estimate are the M-estimators. We have also analyzed the performance of an adaptive non- Gaussian detector built with these improved estimators. Constant False Alarm Rate (CFAR) is pursued to allow the detector independence of nuisance parameters and false alarm regulation.
Keynote 2: Opportunities and challenges in hyperspectral remote sensing

Hyperspectral imagery, also called imaging spectroscopy, refers to images with a large number (typically a few hundreds) of narrow and contiguous spectral bands, covering a wide range of the electromagnetic spectrum from the visible to the infrared domain. Hyperspectral data is able to provide a very fine description of the chemical components in the sensed materials and ensure their detection, discrimination and characterization. The application of hyperspectral imagery is rapidly growing, especially in the context of space and airborne remote sensing, as well as planetary exploration and astrophysics. Additional applications include, monitoring and management of the environment, physical analysis of materials, biomedical imaging, defense and security, food safety, detection of counterfeit objects (especially in pharmacology), and precision agriculture. Unfortunately, every rose has its thorns and the price to pay for the enhanced spectral diversity is high dimensional data. The challenge is in defining appropriate signal and image processing methods. In this talk, I will review some processing and analysis techniques that explicitly handle the high dimensionality of the data, addressing various tasks, including image denoising, image segmentation, hierarchical analysis, spectral unmixing. Results will be presented on images from a variety of contexts.
Decision Fusion for the Classification of Hyperspectral Data: Outcome of the 2008 GRS-S Data Fusion Contest

The 2008 Data Fusion Contest organized by the IEEE Geoscience and Remote Sensing Data Fusion Technical Committee deals with the classification of high-resolution hyperspectral data from an urban area. Unlike in the previous issues of the contest, the goal was not only to identify the best algorithm but also to provide a collaborative effort: The decision fusion of the best individual algorithms was aiming at further improving the classification performances, and the best algorithms were ranked according to their relative contribution to the decision fusion. This paper presents the five awarded algorithms and the conclusions of the contest, stressing the importance of decision fusion, dimension reduction, and supervised classification methods, such as neural networks and support vector machines.
Band Assignment Approaches for Hyperspectral Sharpening

Classical pansharpening algorithms constitute a class of image fusion methods that have been widely investigated in the literature. They have been developed for combining a single- and a multichannel image (panchromatic (PAN) and multispectral (MS), respectively), but can be adapted to the sharpening of hyperspectral (HS) data, both through companion PAN and MS images. We focus in this letter on the HS/MS fusion, showing that the assignation of the MS channel to each HS band is a key step, and investigate several alternatives to make this choice. The assignment algorithms are tested in conjunction with both component substitution and multiresolution analysis pansharpening methods and assessed on images acquired by the Hyperion and ALI sensors. The numerical evaluation shows that the best results can be obtained by optimizing the spectral angle mapper metric confirming that classical methods represent a reliable basis for the development of novel sharpening algorithms.
On the use of higher order statistics in SAS imagery [synthetic aperture sonar]

Synthetic aperture sonar (SAS) imagery is largely used in detection, location and classification of underwater mines laying on or buried in the sea bed. This paper proposes a detection method using higher order statistics (HOS) on SAS images. The proposed method can be divided into two steps. Firstly, the HOS (skewness and kurtosis) are locally estimated using a square sliding computation window. In a second step, the results are focused by a correlation process. This enables the precise location of the objects. This method is tested on real SAS data containing both underwater mines laying on the sea bed and buried objects.
Multispectral and hyperspectral data fusion based on SAM minimization band assignment approach

The sharpening of hyperspectral (HS) images introduces novel questions that have never been faced by classical pansharpening, which deals with the fusion of multispectral and panchromatic images. In this paper, we focus on the fusion of high resolution MultiSpectral (MS) and low resolution HS data, namely tackling the problem of assigning the optimal MS channel for each HS band through the minimization of the Spectral Angle Mapper (SAM) metric. The performance is assessed on two datasets, both composed by a HS and a MS image acquired by the Hyperion and the ALI sensors, respectively. Several MultiResolution Analysis pansharpening approaches are used for evaluating the performance improvements with respect to existing methods.
A Marker-Based Approach for the Automated Selection of a Single Segmentation From a Hierarchical Set of Image Segmentations

The Hierarchical SEGmentation (HSEG) algorithm, which combines region object finding with region object clustering, has given good performances for multi- and hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. Two classification-based approaches for automatic marker selection are adapted and compared for this purpose. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. Three different implementations of the M-HSEG method are proposed and their performances in terms of classification accuracies are compared. The experimental results, presented for three hyperspectral airborne images, demonstrate that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for remote sensing image analysis.
Semi-supervised dimensionality reduction for hyperspectral remote sensing image classification

Class labels and pairwise constraints are adopted as the prior information to present the semi-supervised dimensionality reduction for hyperspectral image. In this paper, we extend semi-supervised probabilistic principal component analysis (S2PPCA), semi-supervised local fisher discriminant analysis (S2LFDA) and semi-supervised dimensionality reduction with pairwise constraints (S2DRpc) to extract the features of hyperspectral image. These semi-supervised dimensionality reduction approaches are compared with PCA in classification task. Experimental results show that semi-supervised algorithms of S2PPCA and S2DRpc are superior to PCA.
Unsupervised nonlinear spectral unmixing by means of NLPCA applied to hyperspectral imagery

In the literature, for sake of simplicity it is usually assumed that the model ruling spectral mixture in a hyperspectral pixels is basically linear. However, in many real life cases the different materials are usually in intimate association, like sand grains, resulting in a nonlinear mixture. Unfortunately, modeling a nonlinear approach is not trivial, and a general procedure is still up to be found. Aim of this paper is to evaluate the potentialities of Nonlinear Principal Component Analysis (NLPCA) as an approach to perform a nonlinear unmixing for the unsupervised extraction and quantification of the end-members. From this point of view scope of this paper is to demonstrate that the NLPCs derived from the proposed process can be considered as end-members. To perform an accurate evaluation, the proposed algorithm has been tested on two different hyperspectral datasets and compared with other approaches found in the literature.
Real time vector median like filter: FPGA design and application to color image filtering

In this paper, the hardware implementation of a vector median like filter is proposed. Firstly, the extension of median filtering to the case of multicomponent images is addressed. It requires the definition of an appropriate vector ordering scheme, which is not straightforward. The proposed solution is based on the bit mixing approach, which consists in a revertible scalar coding of the vector input data, followed by a simple scalar processing of the encoded data. The filtered multicomponent image is obtained after decoding. An application to color images is presented. The second part of this work is the real time implementation of this filter. An FPGA design is proposed and the detailed structure of this implementation is presented.
Challenges and Opportunities of Multimodality and Data Fusion in Remote Sensing

Remote sensing is one of the most common ways to extract relevant information about Earth and our environment. Remote sensing acquisitions can be done by both active (synthetic aperture radar, LiDAR) and passive (optical and thermal range, multispectral and hyperspectral) devices. According to the sensor, a variety of information about the Earth's surface can be obtained. The data acquired by these sensors can provide information about the structure (optical, synthetic aperture radar), elevation (LiDAR), and material content (multispectral and hyperspectral) of the objects in the image. Once considered together their complementarity can be helpful for characterizing land use (urban analysis, precision agriculture), damage detection (e.g., in natural disasters such as floods, hurricanes, earthquakes, oil spills in seas), and give insights to potential exploitation of resources (oil fields, minerals). In addition, repeated acquisitions of a scene at different times allows one to monitor natural resources and environmental variables (vegetation phenology, snow cover), anthropological effects (urban sprawl, deforestation), climate changes (desertification, coastal erosion), among others. In this paper, we sketch the current opportunities and challenges related to the exploitation of multimodal data for Earth observation. This is done by leveraging the outcomes of the data fusion contests, organized by the IEEE Geoscience and Remote Sensing Society since 2006. We will report on the outcomes of these contests, presenting the multimodal sets of data made available to the community each year, the targeted applications, and an analysis of the submitted methods and results: How was multimodality considered and integrated in the processing chain? What were the improvements/new opportunities offered by the fusion? What were the objectives to be addressed and the reported solutions? And from this, what will be the next challenges?
Identification of agricultural crops in early stages using remote sensing images

Real-time monitoring of agricultural crops is increasingly important because of the involved huge economic impact. The automatic identification of crops, as early as possible during the agricultural season, is an important issue supporting agricultural policies. In this context, the objective of this article is to evaluate the possibilities of remote sensing data to identify corn and soybean crops in the early growing season. The proposed study evaluates the potential of hyperspectral data and multispectral NDVI times series. Experimental results illustrate the challenges of crop detection in early stages.
Segmentation of High Resolution Satellite images SPOT applied to Lake Detection.

High resolution satellite imagery are widely used in many applications. One of the main applications is planet monitoring for hazard assessment. The objective of our study is to develop segmentation methods of spaceborne remote sensing images capable of detecting lakes over large areas in high mountainous regions. In this context three different methods are investigated on a SPOT2 image, namely: the spectral angle mapper, the irregular pyramid and the watershed with markers. Each method having its own characteristics, we propose to aggregate the three algorithms and thus improve the segmentation result. In this paper, first we present each one of these methods and the results obtained, then we present the fusion architecture and the result obtained by the comibation of the three methods.
SVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images

The high number of spectral bands acquired by hyperspectral sensors increases the capability to distinguish physical materials and objects, presenting new challenges to image analysis and classification. This letter presents a novel method for accurate spectral-spatial classification of hyperspectral images. The proposed technique consists of two steps. In the first step, a probabilistic support vector machine pixelwise classification of the hyperspectral image is applied. In the second step, spatial contextual information is used for refining the classification results obtained in the first step. This is achieved by means of a Markov random field regularization. Experimental results are presented for three hyperspectral airborne images and compared with those obtained by recently proposed advanced spectral-spatial classification techniques. The proposed method improves classification accuracies when compared to other classification approaches.
Spatio-Temporal Multiscale Denoising of Fluoroscopic Sequence

In the past 20 years, a wide range of complex fluoroscopically guided procedures have shown considerable growth. Biologic effects of the exposure (radiation induced burn, cancer) lead to reduce the dose during the intervention, for the safety of patients and medical staff. However, when the dose is reduced, image quality decreases, with a high level of noise and a very low contrast. Efficient restoration and denoising algorithms should overcome this drawback. We propose a spatio-temporal filter operating in a multi-scales space. This filter relies on a first order, motion compensated, recursive temporal denoising. Temporal high frequency content is first detected and then matched over time to allow for a strong denoising in the temporal axis. We study this filter in the curvelet domain and in the dual-tree complex wavelet domain, and compare those results to state of the art methods. Quantitative and qualitative analysis on both synthetic and real fluoroscopic sequences demonstrate that the proposed filter allows a great dose reduction.
Geometrical features for the classification of very high resolution multispectral remote-sensing images

In order to extract geometrical features from a multispectral image and derive a classification, an approach based on the topographic map of the image is proposed. For each pixel, the most significant structure containing it is extracted. The classification of this pixel is based on its spectral information and the geometrical features of the corresponding structure (its area and perimeter). The results obtained on multispectral remote sensing images taken by two different sensors show the efficiency of the extracted geometrical features for separating some classes with very similar spectral attributes but of different semantic meanings.
Classification of remote sensing images from urban areas using a fuzzy model

The problem of classification of 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 respectively opening and closing operators. For each pixel, this DMP constitutes the feature vector on which the classification is based. In this paper, this vector is considered as a fuzzy measurement of the size of the structure. Compared with some possibility distributions, a membership degree is computed for each class. The decision is taken by selecting the class with the highest membership degree.
Fusion of Hyperspectral and panchromatic images using multiresolution analysis and nonlinear PCA band reduction

This paper presents a novel method for the enhancement of spatial quality of Hyperspectral (HS) images while making use of a high resolution panchromatic (PAN) image. Due to the high number of bands the application of a pansharpening technique to HS images may result in an increase of the computational load and complexity. Thus a dimensionality reduction preprocess, compressing the original number of measurements into a lower dimensional space, becomes mandatory. To solve this problem we propose a pansharpening technique combining both dimensionality reduction and fusion, exploited by non-linear Principal Component Analysis (NLPCA) and Indusion respectively, to enhance the spatial resolution of a hyperspectral image.
GAS plume detection in hyperspectral video sequence using low rank representation

Thanks to the fast development of sensors, it is now possible to acquire sequences of hyperspectral images. Those hyperspectral video sequences (HVS) are particularly suited for the detection and tracking of chemical gas plumes. In this paper, we present a novel gas plume detection method. It is based on the decomposition of the sequence into a low-rank and a sparse term, corresponding to the background and the plume, respectively, and incorporating temporal consistency. To introduce spatial continuity, a post processing is added using the Total Variation (TV) regularized model. Experimental results on real hyperspectral video sequences validate the effectiveness of the proposed method.