A convex formulation for hyperspectral image superresolution via subspace-based regularization

Hyperspectral remote sensing images (HSIs) usually have high spectral resolution and low spatial resolution. Conversely, multispectral images (MSIs) usually have low spectral and high spatial resolutions. The problem of inferring images which combine the high spectral and high spatial resolutions of HSIs and MSIs, respectively, is a data fusion problem that has been the focus of recent active research due to the increasing availability of HSIs and MSIs retrieved from the same geographical area. We formulate this problem as the minimization of a convex objective function containing two quadratic data-fitting terms and an edge-preserving regularizer. The data-fitting terms account for blur, different resolutions, and additive noise. The regularizer, a form of vector Total Variation, promotes piecewise-smooth solutions with discontinuities aligned across the hyperspectral bands. The downsampling operator accounting for the different spatial resolutions, the non-quadratic and non-smooth nature of the regularizer, and the very large size of the HSI to be estimated lead to a hard optimization problem. We deal with these difficulties by exploiting the fact that HSIs generally "live" in a low-dimensional subspace and by tailoring the Split Augmented Lagrangian Shrinkage Algorithm (SALSA), which is an instance of the Alternating Direction Method of Multipliers (ADMM), to this optimization problem, by means of a convenient variable splitting. The spatial blur and the spectral linear operators linked, respectively, with the HSI and MSI acquisition processes are also estimated, and we obtain an effective algorithm that outperforms the state-of-the-art, as illustrated in a series of experiments with simulated and real-life data.Comment: IEEE Trans. Geosci. Remote Sens., to be publishe

Space-based remote imaging spectroscopy of the Aliso Canyon CH_4 superemitter

The Aliso Canyon gas storage facility near Porter Ranch, California, produced a large accidental CH_4 release from October 2015 to February 2016. The Hyperion imaging spectrometer on board the EO-1 satellite successfully detected this event, achieving the first orbital attribution of CH_4 to a single anthropogenic superemitter. Hyperion measured shortwave infrared signatures of CH_4 near 2.3 μm at 0.01 μm spectral resolution and 30 m spatial resolution. It detected the plume on three overpasses, mapping its magnitude and morphology. These orbital observations were consistent with measurements by airborne instruments. We evaluate Hyperion instrument performance, draw implications for future orbital instruments, and extrapolate the potential for a global survey of CH_4 superemitters

Detection of Neolithic Settlements in Thessaly (Greece) Through Multispectral and Hyperspectral Satellite Imagery

Thessaly is a low relief region in Greece where hundreds of Neolithic settlements/tells called magoules were established from the Early Neolithic period until the Bronze Age (6,000 – 3,000 BC). Multi-sensor remote sensing was applied to the study area in order to evaluate its potential to detect Neolithic settlements. Hundreds of sites were geo-referenced through systematic GPS surveying throughout the region. Data from four primary sensors were used, namely Landsat ETM, ASTER, EO1 - HYPERION and IKONOS. A range of image processing techniques were originally applied to the hyperspectral imagery in order to detect the settlements and validate the results of GPS surveying. Although specific difficulties were encountered in the automatic classification of archaeological features composed by a similar parent material with the surrounding landscape, the results of the research suggested a different response of each sensor to the detection of the Neolithic settlements, according to their spectral and spatial resolution

Spectral Super-Resolution of Satellite Imagery with Generative Adversarial Networks

Hyperspectral (HS) data is the most accurate interpretation of surface as it provides fine spectral information with hundreds of narrow contiguous bands as compared to multispectral (MS) data whose bands cover bigger wavelength portions of the electromagnetic spectrum. This difference is noticeable in applications such as agriculture, geosciences, astronomy, etc. However, HS sensors lack on earth observing spacecraft due to its high cost. In this study, we propose a novel loss function for generative adversarial networks as a spectral-oriented and general-purpose solution to spectral super-resolution of satellite imagery. The proposed architecture learns mapping from MS to HS data, generating nearly 20x more bands than the given input. We show that we outperform the state-of-the-art methods by visual interpretation and statistical metrics.Les dades hiperspectrals (HS) són la interpretació més precisa de la superfície, ja que proporciona informació espectral fina amb centenars de bandes contigües estretes en comparació amb les dades multiespectrals (MS) les bandes cobreixen parts de longitud d'ona més grans de l'espectre electromagnètic. Aquesta diferència és notable en àmbits com l'agricultura, les geociències, l'astronomia, etc. No obstant això, els sensors HS manquen als satèl·lits d'observació terrestre a causa del seu elevat cost. En aquest estudi proposem una nova funció de cost per a Generative Adversarial Networks com a solució orientada a l'espectre i de propòsit general per la superresolució espectral d'imatges de satèl·lit. L'arquitectura proposada aprèn el mapatge de dades MS a HS, generant gairebé 20x més bandes que l'entrada donada. Mostrem que superem els mètodes state-of-the-art mitjançant la interpretació visual i les mètriques estadístiques.Los datos hiperspectral (HS) son la interpretación más precisa de la superficie, ya que proporciona información espectral fina con cientos de bandas contiguas estrechas en comparación con los datos multiespectrales (MS) cuyas bandas cubren partes de longitud de onda más grandes del espectro electromagnético. Esta diferencia es notable en ámbitos como la agricultura, las geociencias, la astronomía, etc. Sin embargo, los sensores HS escasean en los satélites de observación terrestre debido a su elevado coste. En este estudio proponemos una nueva función de coste para Generative Adversarial Networks como solución orientada al espectro y de propósito general para la super-resolución espectral de imágenes de satélite. La arquitectura propuesta aprende el mapeo de datos MS a HS, generando casi 20x más bandas que la entrada dada. Mostramos que superamos los métodos state-of-the-art mediante la interpretación visual y las métricas estadísticas

AMELIORATING THE SPATIAL RESOLUTION OF HYPERION HYPERSPECTRAL DATA. THE CASE OF ANTIPAROS ISLAND

In this study seven fusion techniques and more especially the Ehlers, Gram-Schmidt, High Pass Filter, Local Mean Matching (LMM), Local Mean and Variance Matching (LMVM), Pansharp and PCA, were used for the fusion of Hyperion hyperspectral data with ALI panchromatic data. The panchromatic data have a spatial resolution of 10m while the hyperspectral data have a spatial resolution of 30m. All the fusion techniques are designed for use with classical multispectral data. Thus, it is quite interesting to investigate the assessment of the common used fusion algorithms with the hyperspectral data. The study area is Antiparos Island in the Aegean Sea

A comparison of satellite hyperspectral and multispectral remote sensing imagery for improved classification and mapping of vegetation

In recent years the use of remote sensing imagery to classify and map vegetation over different spatial scales has gained wide acceptance in the research community. Many national and regional datasets have been derived using remote sensing data. However, much of this research was undertaken using multispectral remote sensing datasets. With advances in remote sensing technologies, the use of hyperspectral sensors which produce data at a higher spectral resolution is being investigated. The aim of this study was to compare the classification of selected vegetation types using both hyperspectral and multispectral satellite remote sensing data. Several statistical classifiers including maximum likelihood, minimum distance, mahalanobis distance, spectral angular mapper and parallelepiped methods of classification were used. Classification using mahalanobis distance and maximum likelihood methods with an optimal set of hyperspectral and multispectral bands produced overall accuracies greater than 80%.Keywords: hyperspectral, multispectral, satellite data, statistical classifiers, vegetation classificatio

Applications of Remote Sensing to Alien Invasive Plant Studies

Biological invasions can affect ecosystems across a wide spectrum of bioclimatic conditions. Therefore, it is often important to systematically monitor the spread of species over a broad region. Remote sensing has been an important tool for large-scale ecological studies in the past three decades, but it was not commonly used to study alien invasive plants until the mid 1990s. We synthesize previous research efforts on remote sensing of invasive plants from spatial, temporal and spectral perspectives. We also highlight a recently developed state-of-the-art image fusion technique that integrates passive and active energies concurrently collected by an imaging spectrometer and a scanning-waveform light detection and ranging (LiDAR) system, respectively. This approach provides a means to detect the structure and functional properties of invasive plants of different canopy levels. Finally, we summarize regional studies of biological invasions using remote sensing, discuss the limitations of remote sensing approaches, and highlight current research needs and future directions