Jakub Bieniarz

Copernicus Sentinel-2A Calibration and Products Validation Status

As part of the Copernicus programme of the European Commission (EC), the European Space Agency (ESA) has developed and is currently operating the Sentinel-2 mission that is acquiring high spatial resolution optical imagery. This article provides a description of the calibration activities and the status of the mission products validation activities after one year in orbit. Measured performances, from the validation activities, cover both Top-Of-Atmosphere (TOA) and Bottom-Of-Atmosphere (BOA) products. The presented results show the good quality of the mission products both in terms of radiometry and geometry and provide an overview on next mission steps related to data quality aspects.

Joint Sparsity Model for Multilook Hyperspectral Image Unmixing

Recent work on hyperspectral image (HSI) unmixing has addressed the use of overcomplete dictionaries by employing sparse models. In essence, this approach exploits the fact that HSI pixels can be associated with a small number of constituent pure materials. However, unlike traditional least-squares-based methods, sparsity-based techniques do not require a preselection of endmembers and are thus able to simultaneously estimate the underlying active materials along with their respective abundances. In addition, this perspective has been extended so as to exploit the spatial homogeneity of abundance vectors. As a result, these techniques have been reported to provide improved estimation accuracy. In this letter, we present an alternative approach that is able to relax, yet exploit, the assumption of spatial homogeneity by introducing a model that captures both similarities and differences between neighboring abundances. In order to validate this approach, we analyze our model using simulated as well as real hyperspectral data acquired by the HyMap sensor.

Restoration of Simulated EnMAP Data through Sparse Spectral Unmixing

This paper proposes the use of spectral unmixing and sparse reconstruction methods to restore a simulated dataset for the Environmental Mapping and Analysis Program (EnMAP), the forthcoming German spaceborne hyperspectral mission. The described method independently decomposes each image element into a set of representative spectra, which come directly from the image and have previously undergone a low-pass filtering in noisy bands. The residual vector from the unmixing process is considered as mostly composed of noise and ignored in the reconstruction process. The first assessment of the results is encouraging, as the original bands taken into account are reconstructed with a high signal-to-noise ratio and low overall distortions. Furthermore, the same method could be applied for the inpainting of dead pixels, which could affect EnMAP data, especially at the end of the satellite’s life cycle.

Hyperspectral image resolution enhancement based on joint sparsity spectral unmixing

Relatively low spatial resolution of the space-borne hyper-spectral images (HSI) is the main drawback to derive value added products. Recently, several techniques have been proposed in order to enhance the spatial resolution HSI by means of fusion with higher spatial resolution multispectral images. This paper presents an alternative approach based on the joint sparsity model for spectral unmixing with the use of a-priori spectral dictionary. To assess the results, we compare our algorithm with the state of the art methods.

Compression-based unsupervised clustering of spectral signatures

This paper proposes to use compression-based similarity measures to cluster spectral signatures on the basis of their similarities. Such universal distances estimate the shared information between two objects by comparing their compression factors, which can be obtained by any standard compressor. Experiments on rocks categorization show that these methods may outperform traditional choices for spectral distances based on vector processing.

Cloud Removal in Image Time Series through Sparse Reconstruction from Random Measurements

In this paper, we propose a cloud removal algorithm for scenes within a satellite image time series based on synthetization of the affected areas via sparse reconstruction. The high spectrotemporal dimensionality of time series allows applying pixel-based sparse reconstruction techniques efficiently, estimating the values below a cloudy area by observing the spectral evolution in time of pixels in cloud-free areas. The process implicitly compensates the overall atmospheric interactions affecting a given image, and it is possible even if only one acquisition is available for a given period of time. The dictionary, on the basis of which the data are reconstructed, is selected randomly from the available image elements in the time series. This increases the degree of automation of the process, if the area containing clouds and their shadows is given. Favorable comparisons with similar methods and applications to supervised classification and change detection show that the proposed algorithm restores images locally contaminated by clouds and their shadows in a satisfactory and efficient way.

Restoration of EnMAP data through sparse reconstruction

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Spectral-spatial joint sparsity unmixing of hyperspectral data using overcomplete dictionaries

Sparse spectral unmixing can be modeled as a linear combination of endmembers contained in an overcomplete dictionary weighted by the corresponding sparse abundance vector. This method exploits the fact that there is only a small number of endmembers inside a pixel compared to the overcomplete endmember spectral dictionary. Since the information contained in hyperspectral pixels is often spatially correlated, in this work we propose to jointly estimate the sparse abundance vectors of neighboring hyperspectral pixels within a local window exploiting joint sparsity with common and noncommon endmembers. To demonstrate the efficiency of our framework, we perform experiments using both simulated and real hyperspectral data.

New approaches on dimensionality reduction in hyperspectral images for classification purposes

This paper presents a quasi-unsupervised methodology to detect endmembers within an hyperspectral scene and to derive a pixel-wise classification on its basis. The endmember detection step takes as input an overcomplete spectral library, and detects the materials within a scene by analyzing derivative features under the sparsity assumption. The purest pixels for each detected material are then fed to a classifier based on synergetics theory, which is able to produce accurate classification maps on the basis of a restricted training dataset. As the classifier projects the image onto a subspace composed by the classes of interest found in the first step, a focused dimensionality reduction is performed in which every dimension is semantically meaningful.

Hyperspectral resolution enhancement using multisensor image data

In this paper we apply the Multi-Look Joint Sparsity Fusion algorithm to multisensor image data. Our algorithm at first performs sparse unmixing of the hyperspectral data and selects pixels for a second unmixing of the multispectral image. This is done by applying a joint sparsity model, which exploits similarities within neighbouring pixels. We test our resolution enhancement method using a hyperspectral and a multispectral image with a spatial resolution of 30 m and 3 m, respectively. To asses the results we evaluate the classification result of the resolution enhanced and original images.