Imaging spectroscopy, also known as hyperspectral remote sensing, is based on the characterization of Earth surface materials and processes through spectrally-resolved measurements of the light interacting with matter. The potential of imaging spectroscopy for Earth remote sensing has been demonstrated since the 1980s. However, most of the developments and applications in imaging spectroscopy have largely relied on airborne spectrometers, as the amount and quality of space-based imaging spectroscopy data remain relatively low to date. The upcoming Environmental Mapping and Analysis Program (EnMAP) German imaging spectroscopy mission is intended to fill this gap. An overview of the main characteristics and current status of the mission is provided in this contribution. The core payload of EnMAP consists of a dual-spectrometer instrument measuring in the optical spectral range between 420 and 2450 nm with a spectral sampling distance varying between 5 and 12 nm and a reference signal-to-noise ratio of 400:1 in the visible and near-infrared and 180:1 in the shortwave-infrared parts of the spectrum. EnMAP images will cover a 30 km-wide area in the across-track direction with a ground sampling distance of 30 m. An across-track tilted observation capability will enable a target revisit time of up to four days at the Equator and better at high latitudes. EnMAP will contribute to the development and exploitation of spaceborne imaging spectroscopy applications by making high-quality data freely available to scientific users worldwide.
Imaging spectrometry from aerial or spaceborne platforms, also known as hyperspectral remote sensing, provides dense sampled and fine structured spectral information for each image pixel, allowing the user to identify and characterize Earth surface materials such as minerals in rocks and soils, vegetation types and stress indicators, and water constituents. The recently launched DLR Earth Sensing Imaging Spectrometer (DESIS) installed on the International Space Station (ISS) closes the long-term gap of sparsely available spaceborne imaging spectrometry data and will be part of the upcoming fleet of such new instruments in orbit. DESIS measures in the spectral range from 400 and 1000 nm with a spectral sampling distance of 2.55 nm and a Full Width Half Maximum (FWHM) of about 3.5 nm. The ground sample distance is 30 m with 1024 pixels across track. In this article, a detailed review is given on the applicability of DESIS data based on the specifics of the instrument, the characteristics of the ISS orbit, and the methods applied to generate products. The various DESIS data products available for users are described with the focus on specific processing steps. The results of the data quality and product validation studies show that top-of-atmosphere radiance, geometrically corrected, and bottom-of-atmosphere reflectance products meet the mission requirements. The limitations of the DESIS data products are also subject to a critical examination.
Improving the geo-localization of optical satellite images is an important pre-processing step for many remote sensing tasks like monitoring by image time series or scene analysis after sudden events. These tasks require geo-referenced and precisely co-registered multi-sensor data. Images captured by the high resolution synthetic aperture radar (SAR) satellite TerraSAR-X exhibit an absolute geo-location accuracy within a few decimeters. These images represent therefore a reliable source to improve the geo-location accuracy of optical images, which is in the order of tens of meters. In this paper, a deep learning-based approach for the geo-localization accuracy improvement of optical satellite images through SAR reference data is investigated. Image registration between SAR and optical images requires few, but accurate and reliable matching points. These are derived from a Siamese neural network. The network is trained using TerraSAR-X and PRISM image pairs covering greater urban areas spread over Europe, in order to learn the two-dimensional spatial shifts between optical and SAR image patches. Results confirm that accurate and reliable matching points can be generated with higher matching accuracy and precision with respect to state-of-the-art approaches.
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Haze degrades optical data and reduces the accuracy of data interpretation. Haze detection and removal is a challenging and important task for optical multispectral data correction. This paper presents an empirical and automatic method for inhomogeneous haze detection and removal in medium-and high-resolution satellite optical multispectral images. The dark-object subtraction method is further developed to calculate a haze thickness map, allowing a spectrally consistent haze removal on calibrated and uncalibrated satellite multispectral data. Rare scenes with a uniform and highly reflecting landcover result in limitations of the method. Evaluation on hazy multispectral data (Landsat 8 OLI and WorldView-2) and a comparison to haze-free reference data illustrate the spectral consistency after haze removal.
Spectral unmixing and denoising of hyperspectral images have always been regarded as separate problems. By considering the physical properties of a mixed spectrum, this paper introduces Unmixing-based Denoising, a supervised methodology representing any pixel as a linear combination of reference spectra in a hyperspectral scene. Such spectra are related to some classes of interest, and exhibit negligible noise influences, as they are averaged over areas for which ground truth is available. After the unmixing process, the residual vector is mostly composed by the contributions of uninteresting materials, unwanted atmospheric influences and sensor-induced noise, and is thus ignored in the reconstruction of each spectrum. The proposed method, in spite of its simplicity, is able to remove noise effectively for spectral bands with both low and high Signal-to-Noise Ratio. Experiments show that this method could be used to retrieve spectral information from corrupted bands, such as the ones placed at the edge between Ultraviolet and visible light frequencies, which are usually discarded in practical applications. The proposed method achieves better results in terms of visual quality in comparison to competitors, if the Mean Squared Error is kept constant: this leads to question the validity of Mean Squared Error as a predictor for image quality in remote sensing applications.
Whether for identification and characterization of materials or for monitoring of theenvironment, space-based hyperspectral instruments are very useful. Hyperspectral instrumentsmeasure several dozens up to hundreds of spectral bands. These data help to reconstruct the spectralproperties like reflectance or emission of Earth surface or the absorption of the atmosphere, and toidentify constituents on land, water, and in the atmosphere. There are a lot of possible applications,from vegetation and water quality up to greenhouse gas monitoring. But the actual number ofhyperspectral space-based missions or hyperspectral space-based data is limited. This will be changedin the next years by different missions. The German Aerospace Center (DLR) Earth Sensing ImagingSpectrometer (DESIS) is one of the new currently existing space-based hyperspectral instruments,launched in 2018 and ready to reduce the gap of space-born hyperspectral data. The instrument isoperating onboard the International Space Station, using the Multi-User System for Earth Sensing(MUSES) platform. The instrument has 235 spectral bands in the wavelength range from visible(400 nm) to near-infrared (1000 nm), which results in a 2.5 nm spectral sampling distance and aground sampling distance of 30 m from 400 km orbit of the International Space Station. In this article,the design of the instrument will be described.
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