Processing of Sliding Spotlight and TOPS SAR Data Using Baseband Azimuth Scaling

This paper presents an efficient phase preserving processor for the focusing of data acquired in sliding spotlight and TOPS (Terrain Observation by Progressive Scans) imaging modes. They share in common a linear variation of the Doppler centroid along the azimuth dimension, which is due to a steering of the antenna (either mechanically or electronically) throughout the data take. Existing approaches for the azimuth processing can become inefficient due to the additional processing to overcome the folding in the focused domain. In this paper a new azimuth scaling approach is presented to perform the azimuth processing, whose kernel is exactly the same for sliding spotlight and TOPS modes. The possibility to use the proposed approach to process ScanSAR data, as well as a discussion concerning staring spotlight, are also included. Simulations with point-targets and real data acquired by TerraSAR-X in sliding spotlight and TOPS modes are used to validate the developed algorithm

Effects of foam on ocean surface microwave emission inferred from radiometric observations of reproducible breaking waves

Includes bibliographical references.WindSat, the first satellite polarimetric microwave radiometer, and the NPOESS Conical Microwave Imager/Sounder both have as a key objective the retrieval of the ocean surface wind vector from radiometric brightness temperatures. Available observations and models to date show that the wind direction signal is only 1-3 K peak-to-peak at 19 and 37 GHz, much smaller than the wind speed signal. In order to obtain sufficient accuracy for reliable wind direction retrieval, uncertainties in geophysical modeling of the sea surface emission on the order of 0.2 K need to be removed. The surface roughness spectrum has been addressed by many studies, but the azimuthal signature of the microwave emission from breaking waves and foam has not been adequately addressed. RECENtly, a number of experiments have been conducted to quantify the increase in sea surface microwave emission due to foam. Measurements from the Floating Instrumentation Platform indicated that the increase in ocean surface emission due to breaking waves may depend on the incidence and azimuth angles of observation. The need to quantify this dependence motivated systematic measurement of the microwave emission from reproducible breaking waves as a function of incidence and azimuth angles. A number of empirical parameterizations of whitecap coverage with wind speed were used to estimate the increase in brightness temperatures measured by a satellite microwave radiometer due to wave breaking in the field of view. These results provide the first empirically based parameterization with wind speed of the effect of breaking waves and foam on satellite brightness temperatures at 10.8, 19, and 37 GHz.This work was supported in part by the Department of the Navy, Office of Naval Research under Awards N00014-00-1-0615 (ONR/YIP) and N00014-03-1-0044 (Space and Remote Sensing) to the University of Massachusetts Amherst, and N00014-00-1-0152 (Space and Remote Sensing) to the University of Washington. The National Polar-orbiting Operational environmental Satellite System Integrated Program Office supported the Naval Research Laboratory's participation through Award NA02AANEG0338 and supported data analysis at Colorado State University and the University of Washington through Award NA05AANEG0153

Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing

Over the past decades, enormous efforts have been made to improve the performance of linear or nonlinear mixing models for hyperspectral unmixing (HU), yet their ability to simultaneously generalize various spectral variabilities (SVs) and extract physically meaningful endmembers still remains limited due to the poor ability in data fitting and reconstruction and the sensitivity to various SVs. Inspired by the powerful learning ability of deep learning (DL), we attempt to develop a general DL approach for HU, by fully considering the properties of endmembers extracted from the hyperspectral imagery, called endmember-guided unmixing network (EGU-Net). Beyond the alone autoencoder-like architecture, EGU-Net is a two-stream Siamese deep network, which learns an additional network from the pure or nearly pure endmembers to correct the weights of another unmixing network by sharing network parameters and adding spectrally meaningful constraints (e.g., nonnegativity and sum-to-one) toward a more accurate and interpretable unmixing solution. Furthermore, the resulting general framework is not only limited to pixelwise spectral unmixing but also applicable to spatial information modeling with convolutional operators for spatial–spectral unmixing. Experimental results conducted on three different datasets with the ground truth of abundance maps corresponding to each material demonstrate the effectiveness and superiority of the EGU-Net over state-of-the-art unmixing algorithms. The codes will be available from the website: https://github.com/danfenghong/IEEE_TNNLS_EGU-Net

Blind hyperspectral unmixing using an Extended Linear Mixing Model to address spectral variability

International audienceSpectral Unmixing is one of the main research topics in hyperspectral imaging. It can be formulated as a source separation problem whose goal is to recover the spectral signatures of the materials present in the observed scene (called endmembers) as well as their relative proportions (called fractional abundances), and this for every pixel in the image. A Linear Mixture Model is often used for its simplicity and ease of use but it implicitly assumes that a single spectrum can be completely representative of a material. However, in many scenarios, this assumption does not hold since many factors, such as illumination conditions and intrinsic variability of the endmembers, induce modifications on the spectral signatures of the materials. In this paper, we propose an algorithm to unmix hyperspectral data using a recently proposed Extended Linear Mixing Model. The proposed approach allows a pixelwise spatially coherent local variation of the endmembers, leading to scaled versions of reference endmembers. We also show that the classic nonnegative least squares, as well as other approaches to tackle spectral variability can be interpreted in the framework of this model. The results of the proposed algorithm on two different synthetic datasets, including one simulating the effect of topography on the measured reflectance through physical modelling, and on two real datasets, show that the proposed technique outperforms other methods aimed at addressing spectral variability, and can provide an accurate estimation of endmember variability along the scene thanks to the scaling factors estimation

The 2-Look TOPS Mode: Design and Demonstration with TerraSAR-X

Burst-mode acquisition schemes achieve wide coverage at the expense of a degraded azimuth resolution, reducing therefore the performance on the retrieval of ground displacements in the azimuth direction, when interferometric acquisitions are combined. Moreover the azimuth varying line-of-sight can induce discontinuities in the interferometric phase when local azimuth displacements are present, e.g., due to ground deformation. In this contribution we propose the interferometric 2-look TOPS mode, a sustaining innovation, which records bursts of radar echoes of two separated slices of the Doppler spectrum. The spectral separation allows to exploit spectral diversity techniques, achieving sensitivities to azimuth displacements better than with StripMap, and eliminating discontinuities in the interferometric phase. Moreover some limitations of the TOPS mode to compensate ionospheric perturbations, in terms of data gaps or restricted sensitivity to azimuth shifts, are overcome. The design of 2-look TOPS acquisitions will be provided, taking the TerraSAR-X system as reference to derive achievable performances. The methodology for the retrieval of the azimuth displacement is exposed for the case of using pairs of images, as well as for the calculation of mean azimuth velocities when working with stacks. We include results with experimental TerraSAR-X acquisitions demonstrating its applicability for both scenarios