Hyperspectral Airborne “Viareggio 2013 Trial” Data Collection for Detection Algorithm Assessment

Acito, N.; Matteoli, Stefania; Rossi, Alessandro; Diani, Marco; Corsini, Giovanni

doi:10.1109/jstars.2016.2531747

Cited by 68 publications

(25 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the LRXD, the covariance matrix size depends on the number of bands, and in order to have an accurate sample covariance matrix, the number of captured pixels should be no less than band number + 1. The third real dataset was acquired during the "Viareggio 2013 trial" hyperspectral data collection campaign [35], which can be downloaded from the website [36]. Datasets in this campaign were acquired by a pushbroom hyperspectral "Sistema Iperspettrale Modulare Galileo Avionica" sensor mounted on an ultralight aircraft.…”

Section: Detection Performancementioning

confidence: 99%

Anomaly Detection in Hyperspectral Imagery Based on Low-Rank Representation Incorporating a Spatial Constraint

Tan

Hou²,

Ma³

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Hyperspectral imagery contains abundant spectral information. Each band contains some specific characteristics closely related to target objects. Therefore, using these characteristics, hyperspectral imagery can be used for anomaly detection. Recently, with the development of compressed sensing, low-rank-representation-based methods have been applied to hyperspectral anomaly detection. In this study, novel low-rank representation methods were developed for anomaly detection from hyperspectral images based on the assumption that hyperspectral pixels can be effectively decomposed into a low-rank component (for background) and a sparse component (for anomalies). In order to improve detection performance, we imposed a spatial constraint on the low-rank representation coefficients, and single or multiple local window strategies was applied to smooth the coefficients. Experiments on both simulated and real hyperspectral datasets demonstrated that the proposed approaches can effectively improve hyperspectral anomaly detection performance.

show abstract

Section: Detection Performancementioning

confidence: 99%

Anomaly Detection in Hyperspectral Imagery Based on Low-Rank Representation Incorporating a Spatial Constraint

Tan

Hou²,

Ma³

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The SIM.GA dataset was collected in May 2013 in Viareggio, Italy [62]. It contains 511 spectral bands in the VNIR region.…”

Section: Simga Datasetmentioning

confidence: 99%

Supervised Distance-Based Feature Selection for Hyperspectral Target Detection

Rad

Abkar²,

Mojaradi

2019

Remote Sensing

View full text Add to dashboard Cite

Feature/band selection (FS/BS) for target detection (TD) attempts to select features/bands that increase the discrimination between the target and the image background. Moreover, TD usually suffers from background interference. Therefore, bands that help detectors to effectively suppress the background and magnify the target signal are considered to be more useful. In this regard, three supervised distance-based filter FS methods are proposed in this paper. The first method is based on the TD concept. It uses the image autocorrelation matrix and the target signature in the detection space (DS) for FS. Features that increase the first-norm distance between the target energy and the mean energy of the background in DS are selected as optimal. The other two methods use background modeling via image clustering. The cluster mean spectra, along with the target spectrum, are then transferred into DS. Orthogonal subspace projection distance (OSPD) and first-norm distance (FND) are used as two FS criteria to select optimal features. Two datasets, HyMap RIT and SIM.GA, are used for the experiments. Several measures, i.e., true positives (TPs), false alarms (FAs), target detection accuracy (TDA), total negative score (TNS), and the receiver operating characteristics (ROC) area under the curve (AUC) are employed to evaluate the proposed methods and to investigate the impact of FS on the TD performance. The experimental results show that our proposed FS methods, as compared with five existing FS methods, have improving impacts on common target detectors and help them to yield better results. received less attention in target detection (TD) studies since subtle information regarding the target and the background may be lost in this approach. However, there is a new type of FE methods that is based on the deep learning concept, such as the convolutional neural network (CNN) and deep belief network (DBN), which are used to extract new high-level features in order to improve the classification accuracy [16,17].As for FS methods, there are three main categories: filter, wrapper, and embedded methods [18]. Each of these categories is further divided into supervised and unsupervised methods [19]. Supervised methods rely on training samples and prior information of classes, targets, and background to conduct FS, while unsupervised methods use no prior knowledge. The filter methods select features that are independent of the subsequent image analysis to be conducted, such as classification. The selected features are then given as input to the classifier. Several unsupervised filter methods have been developed, based on the information theory, which use criteria, such as correlation coefficient [18], entropy [20], mutual information [21], linear prediction error (LPE) [22], first/second spectral derivative, contrast, and spectral ratio [20]. Some unsupervised FS methods employ search strategies, such as sequential forward/backward selection (SFS/SBS) and sequential floating forward/backward selection (SFFS/SFBS) [23], which are computi...

show abstract

“…The size of the scene is 79 × 100 pixels; the false color image and the corresponding ground-truth map are shown in Figure 6. The third is the MUUFL Gulfport dataset, which is selected from the free hyperspectral datasets available to the remote sensing community [33][34][35][36][37]. It was collected in November 2010 over the University of Southern Mississippi Gulf Park Campus, located in Long Beach, Mississippi.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Low-Rank and Sparse Matrix Decomposition with Cluster Weighting for Hyperspectral Anomaly Detection

2018

View full text Add to dashboard Cite

Hyperspectral anomaly detection plays an important role in the field of remote sensing. It provides a way to distinguish interested targets from the background without any prior knowledge. The majority of pixels in the hyperspectral dataset belong to the background, and they can be well represented by several endmembers, so the background has a low-rank property. Anomalous targets usually account for a tiny part of the dataset, and they are considered to have a sparse property. Recently, the low-rank and sparse matrix decomposition (LRaSMD) technique has drawn great attention as a method for solving anomaly detection problems. In this letter, a new anomaly detection method based on LRaSMD and cluster weighting is proposed. We concentrate on the sparse part, which contains most of anomaly information, and calculate the initial anomaly matrix based on this part. To suppress background regions and discriminate anomalies from the background more distinctly, a weighting strategy in terms of the clustering result is used, and then the anomaly matrix is updated. The judgement of anomalies is made according to the responses on the matrix. Our proposed method considers the characteristics of anomalies from the spectral dimension and the spatial distribution simultaneously. Experiments on three hyperspectral datasets demonstrate the outstanding performance of the proposed method.

show abstract

Hyperspectral Airborne “Viareggio 2013 Trial” Data Collection for Detection Algorithm Assessment

Cited by 68 publications

References 31 publications

Anomaly Detection in Hyperspectral Imagery Based on Low-Rank Representation Incorporating a Spatial Constraint

Anomaly Detection in Hyperspectral Imagery Based on Low-Rank Representation Incorporating a Spatial Constraint

Supervised Distance-Based Feature Selection for Hyperspectral Target Detection

Low-Rank and Sparse Matrix Decomposition with Cluster Weighting for Hyperspectral Anomaly Detection

Contact Info

Product

Resources

About