Yitzhak August

Compressive hyperspectral imaging by random separable projections in both the spatial and the spectral domains

An efficient method and system for compressive sensing of hyperspectral data is presented. Compression efficiency is achieved by randomly encoding both the spatial and the spectral domains of the hyperspectral datacube. Separable sensing architecture is used to reduce the computational complexity associated with the compressive sensing of a large volume of data, which is typical of hyperspectral imaging. The system enables optimizing the ratio between the spatial and the spectral compression sensing ratios. The method is demonstrated by simulations performed on real hyperspectral data.

Compressive sensing spectrometry based on liquid crystal devices

We present a new type of compressive spectroscopy technique employing a liquid crystal (LC) phase retarder. A tunable LC cell is used in a manner compliant with the compressive sensing (CS) framework to significantly reduce the spectral scanning effort. The presented optical spectrometer consists of a single LC phase retarder combined with a single photo detector, where the LC phase retarder is used to modulate the input spectrum and the photodiode is used to measure the transmitted spectral signal. Sequences of measurements are taken, where each measurement is done with a different state of the retarder. Then, the set of photodiode measurements is used as input data to a CS solver algorithm. We demonstrate numerally compressive spectral sensing with approximately ten times fewer measurements than with an equivalent conventional spectrometer.

Evaluating subpixel target detection algorithms in hyperspectral imagery

Our goal in this work is to demonstrate that detectors behave differently for different images and targets and to propose a novel approach to proper detector selection. To choose the algorithm, we analyze image statistics, the target signature, and the targets physical size, but we do not need any type of ground truth. We demonstrate our ability to evaluate detectors and find the best settings for their free parameters by comparing our results using the following stochastic algorithms for target detection: the constrained energy minimization (CEM), generalized likelihood ratio test (GLRT), and adaptive coherence estimator (ACE) algorithms. We test our concepts by using the dataset and scoring methodology of the Rochester Institute of Technology (RIT) Target Detection Blind Test project. The results show that our concept correctly ranks algorithms for the particular images and targets including in the RIT dataset.

Spatial versus spectral compression ratio in compressive sensing of hyperspectral imaging

Compressive hyperspectral imaging is based on the fact that hyperspectral data is highly redundant. However, there is no symmetry between the compressibility of the spatial and spectral domains, and that should be taken into account for optimal compressive hyperspectral imaging system design. Here we present a study of the influence of the ratio between the compression in the spatial and spectral domains on the performance of a 3D separable compressive hyperspectral imaging method we recently developed.

Super-resolution compressive imaging with anamorphic optics

A new imaging technique that combines compressive sensing and super-resolution techniques is presented. Compressive sensing is accomplished by capturing optically a set of Radon projections. Super-resolution measurements are simply taken by introducing a slanted two-dimensional array in the optical system. The goal of the technique is to overcome resolution limitation that occurs in imaging scenarios where dense pixels sensors with large number of pixels are not available or cannot be used. With the presented imaging technique, owing to the compressive sensing approach, we were able to reconstruct images with significantly more number of pixels than measured, and owing to the super-resolution design we have been able to achieve resolution significantly beyond that limited by the sensors pixels size.

Reconstruction algorithms for compressive hyperspectral imaging systems with separable spatial and spectral operators

Recently we introduced a hyperspectral compressive sensing scheme that uses separable projections in the spatial and spectral domains. The separable encoding schemes facilitates the optical implementation, reduces the computational burden dramatically, and storage requirements. Owing to these benefits we have been able to encode the hyperspectral cube in all three dimensions. In this work we present a comparison between various reconstructions methods applied to the hyperspectral data captured with our separable compressive sensing systems.

Hyperspectral compressive imaging based on spectral modulation in the spectral domain

Recently we have proposed a new compressive spectral sensing method based on modulation in the spectral domain, without the need of diffractive or dispersive elements. Here, we expand the compressive sensing spectrometry to hyperspectral imaging.

Identifying low reflection amplitude and low level phase noise points for permanent scatterer (PS) interferometry

The PSI (Persistent Scatterers Interferometry) method relies on identifying a small group of scatterers that maintain high phase reliability over a relatively long period of time. This study demonstrates a new algorithm to identify natural PSC (persistent scatterer candidates) targets in non-inhabited areas. The application of our PSC selection process is conducted for a natural arid scene as opposed to the more common use of the PS technique, which is done mostly for urban areas with structures exhibiting strong reflection (manmade objects). We present a novel robust method to identify PSC in open fields and in places of low backscattering (natural areas). Our method is based on the amplitude time history signature of each point. The main difference between urban areas and open field areas is the low reflectance and less deterministic behavior of the scatterer; hence it is a challenge to detect these low reflection and stable points. Conventional methods for PSC detection require a preprocessing with fine calibration and are mainly suitable to use in urban areas, but may fail when used in the open fields. One of the advantages of our method is the use of a simple process of calibration which is based only on the flight geometry and gain factors without any auxiliary data or assumptions. Consider a vector consisting of the measurement of a PS point as a function of time. We can express this signal as an amplitude times a phase. The amplitude differs between PS points; however potential PS points should correlate spatially and temporally in terms of the phase, independent of their amplitude. Our method improves locates several candidate points with a narrow phase distribution and thus, enables the location of PSCs in natural open areas.

Persistent scatterers detection in open area in high resolution SAR imagery — Case study: Sendai, Japan

The PS-InSAR method results are highly depending on the Persistent Scatters Candidate (PSC) selection process. This study implements a new algorithm for detection of PSC in open fields and natural areas based on high resolution TerraSAR-X images. The main challenge in PSC detection in natural areas is the lack of strong reflected targets in these areas. This cause high number of diverted targets, or low number of true PS targets (depend on the thresholds). Conventional methods for PS detection are highly depending on the fine calibration, and on the scaling of the target within the resolution cell. Our method is more robust since it less depends on fine calibration, and not depends on the target gain. The method consists of two main steps. The first step is definition of a PSC target character i.e. its amplitude time signature (ATS). The second step is detection of pixels with the same (close enough) temporal signature cone, this way strong and weak reflectors get the same chance to be a mark as a target.