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Optical imaging remote sensing technology is an important technical means to obtain information of ground objects, but it is restricted by bad weather such as clouds, rain, and haze. In haze weather condition, optical images often have poor contrast and blurred details, which has a great impact on subsequent applications and interpretation. If the acquired images are processed, not only their quality can be improved, but also their visual effect and utilization value can be improved, so as to reduce the impact brought by haze. In general, the haze is removed from the view of image processing. In this paper, through in-depth analysis and study of existing algorithms and characteristics of optical remote sensing images, a new idea is proposed to solve this problem from the view of combination of image content and auxiliary information. Furthermore, a new haze removal algorithm is proposed based on the Retinex multi-scale model and the histogram characteristics of remote sensing images. Because the new method combines multi-scale model (MSM) and histogram characteristics (HC), it is referred to as MSMHC algorithm in this paper. The advantage of the new method is that the content and type of image are considered in the whole process, and then two processing schemes are set for haze removal. In the test experiments, one hundred groups of image data were used to carry out comparative experiments. At the same time, single-scale Retinex (SSR) algorithm, multi-scale Retinex (MSR) algorithm, dark channel priori (DCP) method, brightness preserving dynamic fuzzy histogram equalization (BPDFHE) algorithm, histogram equalization (HE) method, and homomorphic filter (HF) algorithm were used for comparative experiments with the MSMHC method. Five parameters, including standard deviation (SD), information entropy (IE), peak signal to noise ratio (PSNR), structural similarity (SSIM), and image contrast (IC), were used to quantitatively evaluate the test results. The experimental results and the parameter values showed that the MSMHC algorithm could not only effectively remove haze from remote sensing images, obtain high contrast and high definition images, but also have better generalization ability.INDEX TERMS Haze removal, multi scale model, histogram characteristics, Retinex theory, dark channel prior method, remote sensing image.

The most distinctive characteristic of synthetic aperture radar (SAR) is that it can acquire data under all weather conditions and at all times. However, its coherent imaging mechanism introduces a great deal of speckle noise into SAR images, which makes the segmentation of target and shadow regions in SAR images very difficult. This paper proposes a new SAR image segmentation method based on wavelet decomposition and a constant false alarm rate (WD-CFAR). The WD-CFAR algorithm not only is insensitive to the speckle noise in SAR images but also can segment target and shadow regions simultaneously, and it is also able to effectively segment SAR images with a low signal-to-clutter ratio (SCR). Experiments were performed to assess the performance of the new algorithm on various SAR images. The experimental results show that the proposed method is effective and feasible and possesses good characteristics for general application.

An ultra-wideband and high-efficiency reflective linear-to-circular polarization conversion metasurface is proposed. The proposed metasurface is composed of a square array of a corner-truncated square patch printed on grounded dielectric substrate and covered with a dielectric layer, which is an orthotropic anisotropic structure with a pair of mutually perpendicular symmetric axes u and v along the directions with the tilt angles of ±45° with respect to the vertical y axis. When the u- and v-polarized waves are incident on the proposed metasurface, the phase difference between the two reflection coefficients is close to –90° in an ultra-wide frequency band, so it can realize high-efficiency and ultra-wideband LTC polarization conversion under both x- and y-polarized incidences in this band. The proposed polarization conversion metasurface is simulated and measured. Both the simulated and measured results show that the axial ratio (AR) of the reflected wave is kept below 3 dB in the ultra-wide frequency band of 5.87 GHz–21.13 GHz, which is corresponding to a relative bandwidth of 113%; moreover, the polarization conversion rate (PCR) can be kept larger than 99% in a frequency range of 8.08 GHz–20.92 GHz.

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