A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Diffuse light and building history of the galaxy cluster Abell 2667

We have searched for diffuse intracluster light in the galaxy cluster Abell 2667 (z=0.233) from HST images in three filters. We have applied to these images an iterative multi-scale wavelet analysis and reconstruction technique, which allows to subtract stars and galaxies from the original images. We detect a zone of diffuse emission south west of the cluster center (DS1), and a second faint object (ComDif), within DS1. Another diffuse source (DS2) may be detected, at lower confidence level, north east of the center. These sources of diffuse light contribute to 10-15% of the total visible light in the cluster. Whether they are independent entities or are part of the very elliptical external envelope of the central galaxy remains unclear. VLT VIMOS integral field spectroscopy reveals a faint continuum at the positions of DS1 and ComDif but do not allow to compute a redshift. A hierarchical substructure detection method reveals the presence of several galaxy pairs and groups defining a similar direction as the one drawn by the DS1-central galaxy-DS2 axis. The analysis of archive XMM-Newton and Chandra observations shows X-ray emission elongated in the same direction. The X-ray temperature map shows the presence of a cool core, a broad cool zone stretching from north to south and hotter regions towards the north east, south west and north west. This possibly suggests shock fronts along these directions produced by infalling material. These various data are consistent with a picture in which diffuse sources are concentrations of tidal debris and harassed matter expelled from infalling galaxies by tidal stripping and undergoing an accretion process onto the central cluster galaxy; as such, they are expected to be found along the main infall directions.Comment: Accepted for publication in Astronomy and Astrophysic

A multi-level preconditioned Krylov method for the efficient solution of algebraic tomographic reconstruction problems

Classical iterative methods for tomographic reconstruction include the class of Algebraic Reconstruction Techniques (ART). Convergence of these stationary linear iterative methods is however notably slow. In this paper we propose the use of Krylov solvers for tomographic linear inversion problems. These advanced iterative methods feature fast convergence at the expense of a higher computational cost per iteration, causing them to be generally uncompetitive without the inclusion of a suitable preconditioner. Combining elements from standard multigrid (MG) solvers and the theory of wavelets, a novel wavelet-based multi-level (WMG) preconditioner is introduced, which is shown to significantly speed-up Krylov convergence. The performance of the WMG-preconditioned Krylov method is analyzed through a spectral analysis, and the approach is compared to existing methods like the classical Simultaneous Iterative Reconstruction Technique (SIRT) and unpreconditioned Krylov methods on a 2D tomographic benchmark problem. Numerical experiments are promising, showing the method to be competitive with the classical Algebraic Reconstruction Techniques in terms of convergence speed and overall performance (CPU time) as well as precision of the reconstruction.Comment: Journal of Computational and Applied Mathematics (2014), 26 pages, 13 figures, 3 table

GTC OSIRIS transiting exoplanet atmospheric survey: detection of sodium in XO-2b from differential long-slit spectroscopy

We present two transits of the hot-Jupiter exoplanet XO-2b using the Gran Telescopio Canarias (GTC). The time series observations were performed using long-slit spectroscopy of XO-2 and a nearby reference star with the OSIRIS instrument, enabling differential specrophotometric transit lightcurves capable of measuring the exoplanet's transmission spectrum. Two optical low-resolution grisms were used to cover the optical wavelength range from 3800 to 9300{\AA}. We find that sub-mmag level slit losses between the target and reference star prevent full optical transmission spectra from being constructed, limiting our analysis to differential absorption depths over ~1000{\AA} regions. Wider long slits or multi-object grism spectroscopy with wide masks will likely prove effective in minimising the observed slit-loss trends. During both transits, we detect significant absorption in the planetary atmosphere of XO-2b using a 50{\AA} bandpass centred on the Na I doublet, with absorption depths of Delta(R_pl/R_star)^2=0.049+/-0.017 % using the R500R grism and 0.047+/-0.011 % using the R500B grism (combined 5.2-sigma significance from both transits). The sodium feature is unresolved in our low-resolution spectra, with detailed modelling also likely ruling out significant line-wing absorption over an ~800{\AA} region surrounding the doublet. Combined with narrowband photometric measurements, XO-2b is the first hot Jupiter with evidence for both sodium and potassium present in the planet's atmosphere.Comment: 9 pages, 10 figures, 1 table, accepted for publication in MNRA

The VVDS data reduction pipeline: introducing VIPGI, the VIMOS Interactive Pipeline and Graphical Interface

The VIMOS VLT Deep Survey (VVDS), designed to measure 150,000 galaxy redshifts, requires a dedicated data reduction and analysis pipeline to process in a timely fashion the large amount of spectroscopic data being produced. This requirement has lead to the development of the VIMOS Interactive Pipeline and Graphical Interface (VIPGI), a new software package designed to simplify to a very high degree the task of reducing astronomical data obtained with VIMOS, the imaging spectrograph built by the VIRMOS Consortium for the European Southern Observatory, and mounted on Unit 3 (Melipal) of the Very Large Telescope (VLT) at Paranal Observatory (Chile). VIPGI provides the astronomer with specially designed VIMOS data reduction functions, a VIMOS-centric data organizer, and dedicated data browsing and plotting tools, that can be used to verify the quality and accuracy of the various stages of the data reduction process. The quality and accuracy of the data reduction pipeline are comparable to those obtained using well known IRAF tasks, but the speed of the data reduction process is significantly increased, thanks to the large set of dedicated features. In this paper we discuss the details of the MOS data reduction pipeline implemented in VIPGI, as applied to the reduction of some 20,000 VVDS spectra, assessing quantitatively the accuracy of the various reduction steps. We also provide a more general overview of VIPGI capabilities, a tool that can be used for the reduction of any kind of VIMOS data.Comment: 10 pages, submitted to Astronomy and Astrophysic

Recurrent Segmentation for Variable Computational Budgets

State-of-the-art systems for semantic image segmentation use feed-forward pipelines with fixed computational costs. Building an image segmentation system that works across a range of computational budgets is challenging and time-intensive as new architectures must be designed and trained for every computational setting. To address this problem we develop a recurrent neural network that successively improves prediction quality with each iteration. Importantly, the RNN may be deployed across a range of computational budgets by merely running the model for a variable number of iterations. We find that this architecture is uniquely suited for efficiently segmenting videos. By exploiting the segmentation of past frames, the RNN can perform video segmentation at similar quality but reduced computational cost compared to state-of-the-art image segmentation methods. When applied to static images in the PASCAL VOC 2012 and Cityscapes segmentation datasets, the RNN traces out a speed-accuracy curve that saturates near the performance of state-of-the-art segmentation methods