Existing red tide detection methods have mainly been developed for ocean color satellite data with low spatial resolution and high spectral resolution. Higher spatial resolution satellite images are required for red tides with fine scale and scattered distribution. However, red tide detection methods for ocean color satellite data cannot be directly applied to medium–high spatial resolution satellite data owing to the shortage of red tide responsive bands. Therefore, a new red tide detection method for medium–high spatial resolution satellite data is required. This study proposes the red tide detection U−Net (RDU−Net) model by considering the HY−1D Coastal Zone Imager (HY−1D CZI) as an example. RDU−Net employs the channel attention model to derive the inter−channel relationship of red tide information in order to reduce the influence of the marine environment on red tide detection. Moreover, the boundary and binary cross entropy (BBCE) loss function, which incorporates the boundary loss, is used to obtain clear and accurate red tide boundaries. In addition, a multi−feature dataset including the HY−1D CZI radiance and Normalized Difference Vegetation Index (NDVI) is employed to enhance the spectral difference between red tides and seawater and thus improve the accuracy of red tide detection. Experimental results show that RDU−Net can detect red tides accurately without a precedent threshold. Precision and Recall of 87.47% and 86.62%, respectively, are achieved, while the F1−score and Kappa are 0.87. Compared with the existing method, the F1−score is improved by 0.07–0.21. Furthermore, the proposed method can detect red tides accurately even under interference from clouds and fog, and it shows good performance in the case of red tide edges and scattered distribution areas. Moreover, it shows good applicability and can be successfully applied to other satellite data with high spatial resolution and large bandwidth, such as GF−1 Wide Field of View 2 (WFV2) images.

Sea surface salinity (SSS) plays an important role in global water cycle. In recent years, satellite based remote sensing has proven to be a promising approach for global SSS observation. A new payload concept, named MICAP (microwave imager combined active and passive), has been introduced in this paper. MICAP is a suit of active/passive instrument package, which includes L/C/K band one-dimensional MIR (microwave interferometric radiometer) and L-band DBF (digital beamforming) scatterometer, sharing a parabolic cylinder reflector. MICAP has been selected to be a candidate payload for future Chinese ocean salinity mission. In this paper, the MICAP instrument concept, specification and preliminary system design will be introduced.

For earthquake disaster assessment using remote sensing (RS), multisource image registration is an important step. However, severe earthquakes will increase the deformation between the remote sensing images acquired before and after the earthquakes on different platforms. Traditional image registration methods can hardly meet the requirements of accuracy and efficiency of image registration of post-earthquake RS images used for disaster assessment. Therefore, an improved image registration method was proposed for the registration of multisource high-resolution remote sensing images. The proposed method used the combination of the Shi_Tomasi corner detection algorithm and scale-invariant feature transform (SIFT) to detect tie points from image patches obtained by an image partition strategy considering geographic information constraints. Then, the random sample consensus (RANSAC) and greedy algorithms were employed to remove outliers and redundant matched tie points. Additionally, a pre-earthquake RS image database was constructed using pre-earthquake high-resolution RS images and used as the references for image registration. The performance of the proposed method was evaluated using three image pairs covering regions affected by severe earthquakes. It was shown that the proposed method provided higher accuracy, less running time, and more tie points with a more even distribution than the classic SIFT method and the SIFT method using the same image partitioning strategy.

Frequency Hopping (FH) is a significant anti-interception and anti-interference, so some wireless communication systems extensively use it. Then it can offer better security performance of the system via combining cryptographic algorithms with FH. This paper presents a detailed theoretical design and hardware implementation of an evolutionary ZUC stream cipher algorithm on FPGA. Using some encryption algorithms can improve the security of the FH system by increasing its complexity, including SNOW3g, ZUC algorithm, Chaos system, and other cipher methods. ZUC (named for Zu Chongzhi, a famous mathematician in ancient China) is bitstream encryption that builds the core of the 3GPP integrity algorithm 128-EIA3 and the 3GPP confidentiality algorithm 128-EEA3, providing authentic security services in 4G, even in 5G (IMT-2020) to some extent. The article presented an evolutionary three-layers structure of the ZUC FH system, which innovatively applied the permutation polynomial algorithm and an evolutionary DES cipher algorithm. The introduction of that new DES and the permutation polynomial can efficiently increase the security performance of the ZUC system but also avoid some shortcomings of the UHF (Ultra High Frequency) communication. The experiment of this research designed software by coding VHDL language and implemented hardware using a XILINX Virtex-5 FPGA. The finished experiment gave, analyzed, and compared all results according to the performance of the proposed system and hardware resources. The tests for evaluating the randomness and security denote that the constructed sequences have better performance, including the randomness, linear complexity, and hamming correlations, and pass the NIST test finally. The experimental results show that this improved ZUC cipher stream algorithm proposed in the article can provide a high throughput of 2.08 Gbps under a 65MHz clock frequency. The authors will focus their future work on applying the evolutionary S-box and the algorithm with better security performance.

Atmospheric correction (AC) is the basis for quantitative water remote sensing, and adjacency effects form an important part of AC for medium- and high-spatial-resolution optical satellite images. The 6S radiative transfer model is widely used, but its background reflectance function does not take the radiance changes at water–land boundaries into account. If the observed land possesses bright features, the radiance of the adjacent water will be affected, leading to deviations in the AC results and increasing the uncertainty of water depth-based optical quantitative remote sensing. In this paper, we propose a model named WL-AE (a correction model for water–land boundary adjacency effects), which is based on the obvious radiance differences at water–land boundaries. This model overcomes the problem by which the background reflectance calculation is not terminated due to the highlighting pixel. We consider the influences of different $R_{ns}$ (neighborhood space) on the target pixel. The effective calculation of the equivalent background reflectance of the target pixel is realized, and the influence of the land area anomaly highlighting the pixel on the adjacent water is avoided. The results show that WL-AE can effectively improve the entropy and contrast of the input image and that the water–land boundary is greatly affected by adjacency effects, especially in the green and near-infrared bands, where the $M_{rc}$ (mean rate of change) are as high as 14.2% and 20.1%, respectively. In the visible wavelength, the $Sd$ of $R_{rc}$ (the relative rate of change) is positively correlated with $R_{ns}$, and the $Sd$ reaches 16.9%. Although the adjacency effect is affected by ground object types, its influence area remains within 3 km offshore. Based on the WL-AE and 6S results, the comparative test regarding bathymetric inversion shows that the influence is significant in the 0–5 m depth section. In Penang, the MRE of the 0–4 m inversion results is 31.4%, which is 10.5% lower than that of the 6S model.

An improved image denoising method based on Curvelet transform is proposed in this article. Considering characteristic of Wavelet transform and traditional Curvelet transform, this method divides image up via window neighbourhood processing. It fuses two denoising images based Wavelet and Curvelet transform according to segmentation information and obtains higher quality and better effect image. Experiments show that with noise variance, the method in this paper can not only obtain higher PSNR but also keep more edge details. And it reduces “nick” and “ring” of restoration images.