Web-Based Visualization of Very Large Scientific Astronomy Imagery

Visualizing and navigating through large astronomy images from a remote location with current astronomy display tools can be a frustrating experience in terms of speed and ergonomics, especially on mobile devices. In this paper, we present a high performance, versatile and robust client-server system for remote visualization and analysis of extremely large scientific images. Applications of this work include survey image quality control, interactive data query and exploration, citizen science, as well as public outreach. The proposed software is entirely open source and is designed to be generic and applicable to a variety of datasets. It provides access to floating point data at terabyte scales, with the ability to precisely adjust image settings in real-time. The proposed clients are light-weight, platform-independent web applications built on standard HTML5 web technologies and compatible with both touch and mouse-based devices. We put the system to the test and assess the performance of the system and show that a single server can comfortably handle more than a hundred simultaneous users accessing full precision 32 bit astronomy data.Comment: Published in Astronomy & Computing. IIPImage server available from http://iipimage.sourceforge.net . Visiomatic code and demos available from http://www.visiomatic.org

Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution

In many computer vision applications, obtaining images of high resolution in both the spatial and spectral domains are equally important. However, due to hardware limitations, one can only expect to acquire images of high resolution in either the spatial or spectral domains. This paper focuses on hyperspectral image super-resolution (HSI-SR), where a hyperspectral image (HSI) with low spatial resolution (LR) but high spectral resolution is fused with a multispectral image (MSI) with high spatial resolution (HR) but low spectral resolution to obtain HR HSI. Existing deep learning-based solutions are all supervised that would need a large training set and the availability of HR HSI, which is unrealistic. Here, we make the first attempt to solving the HSI-SR problem using an unsupervised encoder-decoder architecture that carries the following uniquenesses. First, it is composed of two encoder-decoder networks, coupled through a shared decoder, in order to preserve the rich spectral information from the HSI network. Second, the network encourages the representations from both modalities to follow a sparse Dirichlet distribution which naturally incorporates the two physical constraints of HSI and MSI. Third, the angular difference between representations are minimized in order to reduce the spectral distortion. We refer to the proposed architecture as unsupervised Sparse Dirichlet-Net, or uSDN. Extensive experimental results demonstrate the superior performance of uSDN as compared to the state-of-the-art.Comment: Accepted by The IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018, Spotlight

A novel feature fusion approach for VHR remote sensing image classification

6openInternationalInternational coauthor/editorThis article develops a robust feature fusion approach to enhance the classification performance of very high resolution (VHR) remote sensing images. Specifically, a novel two-stage multiple feature fusion (TsF) approach is proposed, which includes an intragroup and an intergroup feature fusion stages. In the first fusion stage, multiple features are grouped by clustering, where redundant information between different types of features is eliminated within each group. Then, features are pairwisely fused in an intergroup fusion model based on the guided filtering method. Finally, the fused feature set is imported into a classifier to generate the classification map. In this work, the original VHR spectral bands and their attribute profiles are taken as examples as input spectral and spatial features, respectively, in order to test the performance of the proposed TsF approach. Experimental results obtained on two QuickBird datasets covering complex urban scenarios demonstrate the effectiveness of the proposed approach in terms of generation of more discriminative fusion features and enhancing classification performance. More importantly, the fused feature dimensionality is limited at a certain level; thus, the computational cost will not be significantly increased even if multiple features are considered.openLiu, S.; Zheng, Y.; Du, Q.; Samat, A.; Tong, X.; Dalponte, M.Liu, S.; Zheng, Y.; Du, Q.; Samat, A.; Tong, X.; Dalponte, M

Investigation on the potential of hyperspectral and Sentinel-2 data for land-cover / land-use classification

The automated analysis of large areas with respect to land-cover and land-use is nowadays typically performed based on the use of hyperspectral or multispectral data acquired from airborne or spaceborne platforms. While hyperspectral data offer a more detailed description of the spectral properties of the Earth’s surface and thus a great potential for a variety of applications, multispectral data are less expensive and available in shorter time intervals which allows for time series analyses. Particularly with the recent availability of multispectral Sentinel-2 data, it seems desirable to have a comparative assessment of the potential of both types of data for land-cover and land-use classification. In this paper, we focus on such a comparison and therefore involve both types of data. On the one hand, we focus on the potential of hyperspectral data and the commonly applied techniques for data-driven dimensionality reduction or feature selection based on these hyperspectral data. On the other hand, we aim to reason about the potential of Sentinel-2 data and therefore transform the acquired hyperspectral data to Sentinel-2-like data. For performance evaluation, we provide classification results achieved with the different types of data for two standard benchmark datasets representing an urban area and an agricultural area, respectively

Deep Learning for semantic segmentation of airplane hyperspectral imaging

Given their success, both qualitative and quantitative, Deep Neural Networks have been used to approach classification and segmentation problems for images, especially during these last few years where it has been possible to design computers with sufficient capacity to make quick and efficient experiments. In this work, we will study the use of two Convolutional Neural Networks (CNNs) to segment the ground of a land section of Maspalomas' Park using an image taken by the flight of an airplane. The comparison will be made in terms of computational cost, complexity and results that will be obtained while testing different algorithms, loss functions or optimizers and also while tuning some other parameters. The results will also be compared with a past work [10] done with the same dataset but another methodology (SVM).Teniendo en cuenta su éxito, tanto cualitativo como cuantitativo, se han utilizado Redes Neuronales Profundas para abordar problemas de clasificación y segmentación de imágenes, especialmente durante estos últimos años donde se han podido diseñar ordenadores con capacidad suficiente para hacer experimentos rápidos y eficientes. En este trabajo, estudiaremos el uso de dos redes neuronales convolucionales (CNNs) para segmentar el suelo de una sección del Parque de Maspalomas mediante una imagen tomada por el vuelo de un avión. La comparación se hará en términos de coste computacional, complejidad y resultados que se obtendrán en probar diferentes algoritmos, funciones de pérdida o optimizadores y, además, ajustando algunos otros parámetros. Los resultados también se compararán con un trabajo anterior [10] realizado con el mismo conjunto de datos, pero con otra metodología (SVM).Tenint en compte el seu èxit, tant qualitatiu com quantitatiu, s'han utilitzat Xarxes Neuronals Profundes per abordar problemes de classificació i segmentació d'imatges, especialment durant aquests últims anys on s'han pogut dissenyar ordinadors amb capacitat suficient per fer experiments ràpids i eficients. En aquest treball, estudiarem l'ús de dues xarxes neuronals convolucionals (CNNs) per segmentar el sòl d'una secció del Parc de Maspalomas mitjançant una imatge presa amb el vol d'un avió. La comparació es farà en termes de cost computacional, complexitat i resultats que s'obtindran en provar diferents algorismes, funcions de pèrdua o optimitzadors i, a més, ajustant alguns altres paràmetres. Els resultats també es compararan amb un treball anterior realitzat [10] amb el mateix conjunt de dades, però amb una altra metodologia (SVM)