Single Infrared Image-Based Stripe Nonuniformity Correction via a Two-Stage Filtering Method

The presence of stripe nonuniformity severely degrades the image quality and affects the performance in many infrared (IR) sensing applications. Prior works correct the nonuniformity by using similar spatial representations, which inevitably damage some detailed structures of the image. In this paper, we instead take advantage of spectral prior of stripe noise to solve its correction problem in single IR image. We first analyse the significant spectral difference between stripes and image structures and utilize this knowledge to characterize stripe nonuniformity. Then a two-stage filtering strategy is adopted combining spectral and spatial filtering. The proposed method enables stripe nonuniformity to be eliminated from coarse to fine, thus preserving image details well. Extensive experiments on simulated images and raw IR images demonstrate that the proposed method achieves superior correction performance over the recent state-of-the-art methods

Fourier Domain Anomaly Detection and Spectral Fusion for Stripe Noise Removal of TIR Imagery

Stripe noise is a common and unwelcome noise pattern in various thermal infrared (TIR) image data including conventional TIR images and remote sensing TIR spectral images. Most existing stripe noise removal (destriping) methods are often difficult to keep a good and robust efficacy in dealing with the real-life complex noise cases. In this paper, based on the intrinsic spectral properties of TIR images and stripe noise, we propose a novel two-stage transform domain destriping method called Fourier domain anomaly detection and spectral fusion (ADSF). Considering the principal frequencies polluted by stripe noise as outliers in the statistical spectrum of TIR images, our naive idea is first to detect the potential anomalies and then correct them effectively in the Fourier domain to reconstruct a desired destriping result. More specifically, anomaly detection for stripe frequencies is achieved through a regional comparison between the original spectrum and the expected spectrum that statistically follows a generalized Laplacian regression model, and then an anomaly weight map is generated accordingly. In the correction stage, we propose a guidance-image-based spectrum fusion strategy, which integrates the original spectrum and the spectrum of a guidance image via the anomaly weight map. The final reconstruction result not only has no stripe noise but also maintains image structures and details well. Extensive real experiments are performed on conventional TIR images and remote sensing spectral images, respectively. The qualitative and quantitative assessment results demonstrate the superior effectiveness and strong robustness of the proposed method

Measuring Sediment Transport Capacity of Concentrated Flow with Erosion Feeding Method

Sediment transport capacity in rills is an important parameter for erosion modeling on hillslopes. It is difficult to measure, especially at gentle slopes with limited rill length. In this study, a special flume with variable slope gradients in upper and lower sections was implemented to measure the sediment transport capacity. The upper flume section with a higher slope gradient generates faster water flow that could scout more sediment to feed the water flow in the rill. The rest of the flume is set at the designated slopes to measure the transport capacity in different slope and runoff conditions. A series of flume experiments were conducted with silt-loam soil to verify the method. The sediment transport capacity was measured under slope gradients of 5°, 10°, 15°, 20°, and 25° and a flow rate of 2, 4, 8, and 16 L min−1. The measured sediment transport capacity values were compared with reference measurements from other rill erosion experiments with similar materials and setups. At high slope gradients of 15°, 20°, and 25°, the newly suggested method produced almost the same transport capacity values. Under the low slope gradients of 5° and 10°, the maximum sediment concentrations from the 8 m long flume with the uniform gradients in the previous experiments, rill erosion with an 8 m long flume produced were about 36% lower than the values measured with the new method, which is insufficient to make the flow reach sediment transport capacity. The sediment transport capacities at lower slopes measured with the new method followed the same trend as those at higher slopes. The new method can supply enough sediments to ensure the flow approach transport capacity measurement and, therefore, provides a feasible approach for estimating sediment transport capacity for conditions with relatively gentle slopes

Measuring Sediment Transport Capacity of Concentrated Flow with Erosion Feeding Method

Sediment transport capacity in rills is an important parameter for erosion modeling on hillslopes. It is difficult to measure, especially at gentle slopes with limited rill length. In this study, a special flume with variable slope gradients in upper and lower sections was implemented to measure the sediment transport capacity. The upper flume section with a higher slope gradient generates faster water flow that could scout more sediment to feed the water flow in the rill. The rest of the flume is set at the designated slopes to measure the transport capacity in different slope and runoff conditions. A series of flume experiments were conducted with silt-loam soil to verify the method. The sediment transport capacity was measured under slope gradients of 5°, 10°, 15°, 20°, and 25° and a flow rate of 2, 4, 8, and 16 L min−1. The measured sediment transport capacity values were compared with reference measurements from other rill erosion experiments with similar materials and setups. At high slope gradients of 15°, 20°, and 25°, the newly suggested method produced almost the same transport capacity values. Under the low slope gradients of 5° and 10°, the maximum sediment concentrations from the 8 m long flume with the uniform gradients in the previous experiments, rill erosion with an 8 m long flume produced were about 36% lower than the values measured with the new method, which is insufficient to make the flow reach sediment transport capacity. The sediment transport capacities at lower slopes measured with the new method followed the same trend as those at higher slopes. The new method can supply enough sediments to ensure the flow approach transport capacity measurement and, therefore, provides a feasible approach for estimating sediment transport capacity for conditions with relatively gentle slopes

An Underwater Source Localization Method Using Bearing Measurements

Angle-of-arrival (AOA) measurements are often used in underwater acoustical localization. Different from the traditional AOA model based on azimuth and elevation measurements, the AOA model studied in this paper uses bearing measurements. It is also often used in the Ultra-Short Baseline system (USBL). However, traditional acoustical localization needs additional range information. If the range information is unavailable, the closed-form solution is difficult to obtain only with bearing measurements. Thus, a localization closed-form solution using only bearing measurements is explored in this article. A pseudo-linear measurement model between the source position and the bearing measurements is derived, and considering the nonlinear relationship of the parameters, a weighted least-squares optimization equation based on multiple constraints is established. Different from the traditional two-step least-squares method, the semidefinite programming (SDP) method is designed to obtain the initial solution, and then a bias compensation method is proposed to further minimize localization errors based on the SDP result. Numerical simulations show that the performance of the proposed method can achieve Cramer–Rao lower bound (CRLB) accuracy. The field test also proves that the proposed method can locate the source position without range measurements and obtain the highest positioning accuracy

A Robust INS/USBL/DVL Integrated Navigation Algorithm Using Graph Optimization

The Autonomous Underwater Vehicle (AUV) is usually equipped with multiple sensors, such as an inertial navigation system (INS), ultra-short baseline system (USBL), and Doppler velocity log (DVL), to achieve autonomous navigation. Multi-source information fusion is the key to realizing high-precision underwater navigation and positioning. To solve the problem, a fusion scheme based on factor graph optimization (FGO) is proposed. Due to multiple iterations and joint optimization of historical data, FGO could usually show a better performance than the traditional Kalman filter. In addition, considering that USBL and DVL are usually heavily influenced by the environment, outliers are often present. A robust integrated navigation algorithm based on a maximum correntropy criterion and FGO scheme is proposed. The proposed algorithm solves the problem of multi-sensor fusion and non-Gaussian noise. Numerical simulations and field tests demonstrate that the proposed FGO scheme shows a better performance and robustness than the traditional Kalman filter. Compared with the traditional Kalman filtering, the positioning accuracy is improved by 5.3%, 9.1%, and 5.1% in the east, north, and height directions. It can realize a more accurate navigation and positioning of underwater multi-sensors

Temporal-Spatial Nonlinear Filtering for Infrared Focal Plane Array Stripe Nonuniformity Correction

In this work, we introduce a temporal-spatial approach for infrared focal plane array (IRFPA) stripe nonuniformity correction in infrared images that generates visually appealing results. We posit that the nonuniformity appears as a striped structure in the spatial domain and that the pixel values change slowly in the temporal domain. Based on this, we formulate our correction method in two steps. In the first step, weighted guided image filtering with our adaptive weight is utilized to predict the stripe nonuniformity using a single frame. In the second step, the temporal profile of each pixel can be formed using a few frames of successive nonuniformity images. Further, we present a temporal nonlinear diffusion equation to remove scene residuals from the temporal profile of nonuniformity images in order to estimate a more accurate value of the stripe nonuniformity. The results of extensive experiments demonstrate that the proposed nonuniformity correction algorithm substantially outperforms many state-of-the-art approaches, including both traditional and deep convolution-neural-network-based methods, on four popular infrared videos. In addition, the proposed method only requires a fraction (less than ten) of the video frames

Effects of rye grass coverage on soil loss from loess slopes

Vegetative coverage is commonly used to reduce urban slope soil erosion. Laboratory experimental study on soil erosion under grass covered slopes is conventionally time and space consuming. In this study, a new method is suggested to study the influences of vegetation coverage on soil erosion from a sloped loess surface under three slope gradients of 5°, 15°, and 25°; four rye grass coverages of 0%, 25%, 50%, and 75%; and three rainfall intensities of 60, 90, and 120 mm/h with a silt-loamy loess soil. Rye grasses were planted in the field with the studied soil before being transplanted into a laboratory flume. Grass was allowed to resume growth for a period before the rain simulation experiment. Results showed that the grass cover reduced soil erosion by 63.90% to 92.75% and sediment transport rate by 80.59% to 96.17% under different slope gradients and rainfall intensities. The sediment concentration/sediment transport rate from bare slope was significantly higher than from a grass-covered slope. The sediment concentration/transport rate from grass-covered slopes decreased linearly with grass coverage and increased with rainfall intensity. The sediment concentration/transport rate from the bare slope increased as a power function of slope and reached the maximum value at the gradient of about 25°, whereas that from grass-covered slope increased linearly and at much lower levels. The results of this study can be used to estimate the effect of vegetation on soil erosion from loess slopes