HypeRvieW: an open source desktop application for hyperspectral remote-sensing data processing

Garea, Alberto S.; Ordóñez, Álvaro; Heras, Dora B.; Argüello, Francisco

doi:10.1080/01431161.2016.1244363

Cited by 6 publications

(3 citation statements)

References 35 publications

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“…We use a new dataset consisting of hyperspectral images depicting part of ancient walls of Saint Nicolas fortress located in the UNESCO Heritage Medieval city of Rhodes, Greece. The 6 images of the dataset were collected using the Hyper-View sensing platform [17] by 3D-one, which combines the information from one Visual (VIS) snap-shot camera and one Near Infrared (NIR) snap-shot camera. Each hyperspectral image consists of 1016 x 1820 pixels and 42 spectral bands.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Automatic Inspection of Cultural Monuments Using Deep and Tensor-Based Learning on Hyperspectral Imagery

Tzortzis

Rallis

Makantasis

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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In Cultural Heritage, hyperspectral images are commonly used since they provide extended information regarding the optical properties of materials. Thus, the processing of such high-dimensional data becomes challenging from the perspective of machine learning techniques to be applied. In this paper, we propose a Rank-R tensor-based learning model to identify and classify material defects on Cultural Heritage monuments. In contrast to conventional deep learning approaches, the proposed high order tensor-based learning demonstrates greater accuracy and robustness against overfitting. Experimental results on real-world data from UN-ESCO protected areas indicate the superiority of the proposed scheme compared to conventional deep learning models.

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Section: Dataset Descriptionmentioning

confidence: 99%

Automatic Inspection of Cultural Monuments Using Deep and Tensor-Based Learning on Hyperspectral Imagery

Tzortzis

Rallis

Makantasis

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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“…In the available ground truth, 75.79% of the total number of pixels are not classified, i.e., do not have any label. These images were coregistered by using the HypeRvieW desktop tool; see [36,37] and Figure 4 for the RGB rendering. The choice of these datasets is not casual as they can be considered typical benchmarks to assess the accuracy of the CD algorithm for three different cases.…”

Section: Numerical Experimentsmentioning

confidence: 99%

RSB: Robust Successive Binarization for Change Detection in Bitemporal Hyperspectral Images

Falini

2022

Algorithms

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The Earth’s observation programs, through the acquisition of remotely sensed hyperspectral images, aim at detecting and monitoring any relevant surface change due to natural or anthropogenic causes. The proposed algorithm, given as input a pair of hyperspectral images, produces as output a binary image denoting in white the changed pixels and in black the unchanged ones. The presented procedure relies on the computation of specific dissimilarity measures and applies successive binarization techniques, which prove to be robust, with respect to the different scenarios produced by the chosen measure, and fully automatic. The numerical tests show superior behavior when other common binarization techniques are used, and very competitive results are achieved when other methodologies are applied on the same benchmarks.

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“…Our proposed dataset consists of 14 final hyperspectral images of the Fort of Saint Nicholas, with 42 channels for each image. These measurements carried out using the HyperView [18] multi sensor hyperspectral sensing platform by 3D-one. This Hyper-View system is a dual head system combining one Visual (VIS) snap-shot camera and one Near Infrared (NIR) snap-shot camera, which are connected on a EP-12 board.…”

Section: The Experimental Setupmentioning

confidence: 99%

Evaluating the Usefulness of Unsupervised Monitoring in Cultural Heritage Monuments

Zafeiropoulos¹,

Tzortzis²,

Rallis³

et al. 2021

Frontiers in Artificial Intelligence and Applications

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In this paper, we scrutinize the effectiveness of various clustering techniques, investigating their applicability in Cultural Heritage monitoring applications. In the context of this paper, we detect the level of decomposition and corrosion on the walls of Saint Nicholas fort in Rhodes utilizing hyperspectral images. A total of 6 different clustering approaches have been evaluated over a set of 14 different orthorectified hyperspectral images. Experimental setup in this study involves K-means, Spectral, Meanshift, DBSCAN, Birch and Optics algorithms. For each of these techniques we evaluate its performance by the use of performance metrics such as Calinski-Harabasz, Davies-Bouldin indexes and Silhouette value. In this approach, we evaluate the outcomes of the clustering methods by comparing them with a set of annotated images which denotes the ground truth regarding the decomposition and/or corrosion area of the original images. The results depict that a few clustering techniques applied on the given dataset succeeded decent accuracy, precision, recall and f1 scores. Eventually, it was observed that the deterioration was detected quite accurately.

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HypeRvieW: an open source desktop application for hyperspectral remote-sensing data processing

Cited by 6 publications

References 35 publications

Automatic Inspection of Cultural Monuments Using Deep and Tensor-Based Learning on Hyperspectral Imagery

Automatic Inspection of Cultural Monuments Using Deep and Tensor-Based Learning on Hyperspectral Imagery

RSB: Robust Successive Binarization for Change Detection in Bitemporal Hyperspectral Images

Evaluating the Usefulness of Unsupervised Monitoring in Cultural Heritage Monuments

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