Oceanic LiDAR (hereafter referred to as O-LiDAR) is an important remote sensing device for measuring the near-coastal water depth and for studying the optical properties of water bodies. With the commercialization of LiDAR, the theoretical research on the underwater transmission characteristics of LiDAR has been intensified worldwide. Primary research interests include the simulation and modeling of LiDAR underwater echo signals and the inversion of optical parameters using LiDAR water echo signals. This article provides an overview of the principle of LiDAR echo signal formation, and comprehensively summarizes the LiDAR echo signal simulation modeling methods and the corresponding factors that affect modeling accuracy by focusing on the characteristics of different methods. We found that the current simulation methods of LiDAR underwater transmission echo signals primarily include an analytical method based on the radiation transfer equation and a statistical method based on the Monte Carlo (MC) model. The radiation transport equation needs to be appropriately simplified using the analytical method, usually using the quasi-single-small-angle approximation principle. The analytical method has high calculation efficiency but its accuracy is dependent to the quasi-single small-angle approximation. The statistical method can analyze the influence of various factors on echo signals by controlling the variables, but it has poor calculation efficiency. Finally, the semianalytical MC model was used to quantitatively analyze the three main factors (LiDAR system parameters, water body optical parameters, and environmental parameters) affecting underwater LiDAR transmission characteristics, and summarizes the mechanism and results of different factors.

For real-time monitoring of natural disasters, such as fire, volcano, flood, landslide, and coastal inundation, highly-accurate georeferenced remotely sensed imagery is needed. Georeferenced imagery can be fused with geographic spatial data sets to provide geographic coordinates and positing for regions of interest. This paper proposes an on-board georeferencing method for remotely sensed imagery, which contains five modules: input data, coordinate transformation, bilinear interpolation, and output data. The experimental results demonstrate multiple benefits of the proposed method: (1) the computation speed using the proposed algorithm is 8 times faster than that using PC computer; (2) the resources of the field programmable gate array (FPGA) can meet the requirements of design. In the coordinate transformation scheme, 250,656 LUTs, 499,268 registers, and 388 DSP48s are used. Furthermore, 27,218 LUTs, 45,823 registers, 456 RAM/FIFO, and 267 DSP48s are used in the bilinear interpolation module; (3) the values of root mean square errors (RMSEs) are less than one pixel, and the other statistics, such as maximum error, minimum error, and mean error are less than one pixel; (4) the gray values of the georeferenced image when implemented using FPGA have the same accuracy as those implemented using MATLAB and Visual studio (C++), and have a very close accuracy implemented using ENVI software; and (5) the on-chip power consumption is 0.659 W. Therefore, it can be concluded that the proposed georeferencing method implemented using FPGA with second-order polynomial model and bilinear interpolation algorithm can achieve real-time geographic referencing for remotely sensed imagery.

With increasing demands in real-time or near real-time remotely sensed imagery applications in such as military deployments, quick response to terrorist attacks and disaster rescue, the on-board geometric calibration problem has attracted the attention of many scientists in recent years. This paper presents an on-board geometric calibration method for linear CCD sensor arrays using FPGA chips. The proposed method mainly consists of four modules—Input Data, Coefficient Calculation, Adjustment Computation and Comparison—in which the parallel computations for building the observation equations and least squares adjustment, are implemented using FPGA chips, for which a decomposed matrix inversion method is presented. A Xilinx Virtex-7 FPGA VC707 chip is selected and the MOMS-2P data used for inflight geometric calibration from DLR (Köln, Germany), are employed for validation and analysis. The experimental results demonstrated that: (1) When the widths of floating-point data from 44-bit to 64-bit are adopted, the FPGA resources, including the utilizations of FF, LUT, memory LUT, I/O and DSP48, are consumed at a fast increasing rate; thus, a 50-bit data width is recommended for FPGA-based geometric calibration. (2) Increasing number of ground control points (GCPs) does not significantly consume the FPGA resources, six GCPs is therefore recommended for geometric calibration. (3) The FPGA-based geometric calibration can reach approximately 24 times faster speed than the PC-based one does. (4) The accuracy from the proposed FPGA-based method is almost similar to the one from the inflight calibration if the calibration model and GCPs number are the same.

Feature points that are obtained from the combined speeded-up robust feature (SURF) detector and binary robust independent elementary features (BRIEF) descriptor have a highly robust performance. These points are previously considered the ground control points (GCPs) for building a connection between the image coordinates and the corresponding geodetic coordinates. This paper proposes a novel architecture to automatically and intelligently extract GCPs based on field programmable gate arrays (FPGAs). The parallelization SURF detector, BRIEF descriptor and BRIEF matching are implemented in a single Xilinx XC7VX980T FPGA system. Word length reduction (WLR), memory-efficient parallel architecture (MEPA), shift and subtraction strategies (SAS), a sliding window for separable convolution, and an optimized multispacer-scale are used to optimize the SURF detector. Improved parallel adder trees are used to accelerate the BRIEF matching. The proposed system achieves 380 frame per second (fps) with a 100 MHz clock frequency, which satisfies the real-time and low-power requirements of embedded devices. The results of the experiment demonstrate that the proposed architecture, when mapped onto a Xilinx Virtex-7 XC7VX980T FPGA device, can select the robust feature points.

This paper proposes a novel low-complexity implementation with one parameter two dimensions cubic convolution interpolation (2D-CCI) of image interpolation for spaceborne georeferencing. The experiment results demonstrate that (1) the proposed scheme can achieve better performance without increasing the computational complexity compared to the traditional CCI; (2) the number of multiplication and addition is reduced to 33.33% and 40% respectively compared with original CCI; (3) the occupied resources of LUTs, register, DSP and memory are reduced to 23.7%, 21.9%, 18.5% and 25%, respectively; (4) the speedup time of the proposed Implementation is about 25 times higher than that when using the PC-based implementation.