Compressive full-Stokes spectropolarimetric imaging (SPI), integrating passive polarization modulator (PM) into general imaging spectrometer, is powerful enough to capture high-dimensional information via incomplete measurement; a reconstruction algorithm is needed to recover 3D data cube (x, y, and λ) for each Stokes parameter. However, existing PMs usually consist of complex elements and enslave to accurate polarization calibration, current algorithms suffer from poor imaging quality and are subject to noise perturbation. In this work, we present a single multiple-order retarder followed a polarizer to implement passive spectropolarimetric modulation. After building a unified forward imaging model for SPI, we propose a deep image prior plus sparsity prior algorithm for high-quality reconstruction. The method based on untrained network does not need training data or accurate polarization calibration and can simultaneously reconstruct the 3D data cube and achieve self-calibration. Furthermore, we integrate the simplest PM into our miniature snapshot imaging spectrometer to form a single-shot SPI prototype. Both simulations and experiments verify the feasibility and outperformance of our SPI scheme. It provides a paradigm that allows general spectral imaging systems to become passive full-Stokes SPI systems by integrating the simplest PM without changing their intrinsic mechanism.
Usually, the practical analysis states of an imaging polarimeter needs to be calibrated, with a set of standard polarization states, for the accurate reconstruction of Stokes parameters. However, it is really challenged to get the standard elements over wide field of view (FOV), broad waveband, large aperture, or other non-trivial conditions. Even if the system is well calibrated, the calibrated system will be disturbed in the vibration environment. To avoid the difficult from the standard polarization states, an iterative reconstruction method is presented at the first time to recover the polarization parameters from the data acquired by linear-Stokes polarimeters without polarimetric calibrations. Inspired from phase shifting interferometry, the method employs two least-squares iterative procedure and requires no any extra element for assistant. And we extend the method to a channeled linear imaging spectropolarimeter, channeled linear imaging spectropolarimeter can measure a two-dimensional distribution of spectrally-resolved linear Stokes parameters in a single-shot polarization modulation. However, the state-of-art reconstruction method, Fourier transform method (FTM), usually transforms the modulated spectrum into the frequency domain for further processing. As a result, there is channel crosstalk issue that limits available frequency bandwidth. In addition, FTM needs extra phase calibration to decode final spectra. We present a continuous slide iterative method (CSIM) in the spectral domain to avoid the use of the Fourier transform and phase calibration. It combines a sliding unit cell kernel in the spectral domain that provides unit cell tracking and a loop of twostep least-squares fit that estimates spatially-resolved polarized spectra.
Classification is the focus and difficulty of hyperspectral imaging technology. Hyperspectral data have twodimensional spatial information and one-dimensional spectral information, which are presented as three-dimensional data blocks with large amount of information, meanwhile high-dimension, high nonlinearity and limited training samples bring great challenges. Deep learning can extract and analyze the features of target data step by step by building multi-layer deep nonlinear structure. The advanced feature, multi scale abstract information extracted by convolution neural network applied to image processing can improve the classification accuracy of complex hyperspectral data. We regard pixel level hyperspectral classification as semantic segmentation network, and creatively introduce squeeze-and-excitation network and pyramid pooling network into hyperspectral classification network and proposed a model based on the structure of 2D-3D hybrid convolution neural network, it can learn deeper spatial spectral features and fusion to improve the accuracy and speed of hyperspectral classification.
Change detection (CD) is the process of identifying differences in the state of an object or phenomenon by observing it at different times. CD is one of the earliest and most important applications of remote sensing technology. The hyperspectral image (HSI) of remote sensing satellite provides an important and unique data source for CD, but its high dimension, noise and limited data set make the task of CD very challenging. Traditional algorithms are no longer suitable for hyperspectral data processing. Recently, the success of deep convolutional neural networks (CNN) has widely spread across the whole field of computer vision for their powerful representation abilities. Therefore, this paper combines traditional algorithms and deep learning techniques to solve the CD task of hyperspectral remote sensing images. The proposed two-branch Unet network with feature fusion (Unet-ff) model in this paper uses neural networks to automatically extract features to achieve end-to-end change information detection. In order to improve the degree of automation in the application, we select the most effective results as the training sample for the neural network which obtained by various traditional algorithms, and use ground truth to evaluate the detection results. For the characteristics of hyperspectral data, we use effective dimensionality reduction methods and rich data amplification methods to improve the detection accuracy. Experimental results show that our method can achieve better results on the existing classical datasets.
For passive polarization detection, limited by the scattering medium and the sensitivity of the detector, the detector usually can only receive the polarization information of the weak target. Moreover, nonlinear operators usually amplify the influence of noise in polarization parameter images. These factors result in low SNR of polarization images, which affects the application of polarization technology in different fields. This paper proposes a polarization image layering algorithm based on the principle of biological vision, which uses the statistical characteristics of the angle of polarization (AoP) as the weight parameter to perform contrast enhancement and denoising operations on the degree of polarization (DoP) image. And the final result can be obtained by simple fusion. Experimental results demonstrate that the algorithm is capable of improving the DOP of the target while suppressing background noise, which may provide new ideas for the application of polarized target detection and polarization visualization in interdisciplinary fields.
Snapshot spectral imaging is a cutting-edge parallel acquisition technology for mapping the 3D datacube (2D spectral and 1D spatial information) of a scene in real time. Herein, we present a compact, miniature, snapshot Optically Replicating and Remapping Imaging Spectrometer (ORRIS). Its principle is based on the shifting of subimages replicated by a specially organized lenslet array and the filtering of each subimage by a continuous variable filter (CVF). The 3D datacube is recovered just using a simple image remapping process. The use of the lenslet array and the CVF makes the system very compact and miniature. A handheld proof-of-principle prototype is built in our laboratory by just using commercial-off-the-shelf products. It covers a wavelength region 360 nm to 860 nm with 80 spectral channels with a spatial resolution of 400 × 400 pixels. The volume of prototype is about 230 mm (length) × 70 mm (width) × 70 mm (height) and the weight is about 1.0 kg for finite imaging, and it will become 50 mm (length) × 70 mm (width) × 70 mm (height) and 0.5 kg for infinite imaging. The prototype is verified by measuring outdoor static and dynamic scenes.
The theoretical operation and experimental demonstration of a Fourier-transform Stokes imaging spectropolarimeter are presented. It is composed of two birefringent crystal retarders with equal thickness (the frontal retarder is rotatable) and a Fourier-transform spectrometer based on Savart polariscope. The polarized light enters the spectrometer to create three sets of interferograms, where the spectral Stokes parameters can be calculated and acquired. Compared with previous instruments, the significant advantages of the described sensor are no spatial aliasing in the polarized spectra and it can be used in wider spectral coverage with low cost, ultra-compact size and a simpler common-path configuration.
Reflection Z-scan technique allows the measurements of optical nonlinearities of highly absorbing media and surface of transparent media, when transmission Z-scan can not be used. However, Reflection Z-scan needs multiple measurements under strong laser pulse excitation in the scanning process. This can induce damage in the sample in some cases. In this paper, a Non-scanning Reflection Technique (NRT) for measurement of optical nonlinearities is presented to overcome this drawback. Both the nonlinear refraction index and nonlinear absorption coefficient can be determined by measuring the reflection in combination of variable attenuator and an aperture. Based on the Fresnel theory, a theoretical analysis of Non-scanning Reflection Technique (NRT) demonstrates the feasibility of this approach is given and a general expression for the normalized reflectance is derived. In order to illustrate our analytical results, we performed a numerical simulation of the normalized reflectance. Besides, retardance and size of the induced phase plate also make contributions to the normalized reflectance. Moreover, this technique shows a higher sensitivity property compared with traditional reflection Z-scan method.
Snapshot imaging spectropolarimetry is emerging as a powerful tool for mapping the spectral dependent state of
polarization across most of scenarios (stable and variable), owing to its capability of real-time parallel acquisition. In this
paper, two schema of snapshot full-Stokes imaging polarimeters (SFSIP) based on division-of-aperture polarimetry are
presented firstly. In compliance with the definition of Stokes parameters, the first SFSIP consists of three Wollaston
prisms with superior extinction ratio and simultaneously measures six polarimetric intensities (I0, I90, I45, I135, IL and IR)
of scene. However, the spatial resolution of each polarimetric image only occupy one-six of detector. To increase the
spatial resolution, the second SFSIP comprises a optimal four-quadrant polarization array and a pyramid prism is used to
simultaneously acquire four polarimetric intensities. Since the optimal four-quadrant polarization array consists of a
uniform linear polarizer and four 132º retarders with different azimuth of fast axis, the signal-to-noise ratio for each of
the recovered Stokes parameters will be balanced and enhanced. Finally, the four-quadrant polarization array and
pyramid prism are integrated into a integral field spectroscopy to construct a snapshot full-Stokes imaging
spectropolarimetry (SFSISP). It is used to map the spectral dependent full Stokes parameters across a scene in real time.
Three compact and static birefringent Fourier transform imaging spectropolarimeters are presented. They based on the different combinations of birefringent elements, including Savart polariscope, Wollaston prism, achromatic half-wave plate and quarter-wave plate. After acquiring several interferograms simultaneously for different polarization states with a single CCD, the spectral dependence of polarization states are recovered with Fourier transformation. The interference models are described theoretically, and the performances are demonstrated through numerical simulations and experiments. In contrast to the well-known channeled spectropolarimetry, the most important advantages are that the sampling interferograms have no channel aliasing and directly correspond to the maximum optical path difference of birefringent interferometer. That is say, they can recover the spectral variation of polarization state with the interferometer’s maximum spectral resolution.
A static dual-channel polarization imaging spectrometer which can simultaneously acquire inphase and antiphase
interference images by using a single CCD camera is presented theoretically. The increase of interference signal together
with the extractions of the pure image and the pure fringes can be achieved from the summation and the difference of the
two interference images respectively. The polarization interferometer is based on the combination of a linear polarizer, a
Savart polariscope and a Wollaston prism. To get straight fringes over a relative large field of view, a combined Savart
polariscope made of the positive and negative uniaxial crystals can be employed. The principle and the configuration of
the system are described.
Herein, we sincerely make an apology to readers for our misguidance. We have made a mistake on the analysis of the optical path difference produced by the Savart polariscope which resulted in the improper design of a wide-field-of-view Savart polariscope. The correct statements are presented.
Several novel designs of wide-field-of-view polarization imaging spectrometers based on combined Savart polariscopes are presented. By numerical modeling and analysis, we show that the field of view can be extended when the polariscopes are made of the same uniaxial crystal or positive and negative uniaxial crystals are combined. The designs with increased fields of view enable the acquisition of undistorted interferogram and high étendue for the spectrometer systems.
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