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Polarimetric imaging technology plays a crucial role in de-scattering, particularly in the field of underwater image restoration. Circularly polarized light (or the underlying circular polarization memory effect) has been proven to better preserve the polarization characteristics of detected light. Utilizing circularly polarized light as illumination can further enhance the effectiveness of polarization de-scattering techniques. After rethinking the advantages of circularly polarized light, this paper proposes a new method for underwater polarimetric imaging restoration that leverages the pre-processing of polarized sub-images and the correlation of polarization characteristics (i.e., the angle of polarization and degree of polarization). Additionally, to address the challenge of selecting scattering light parameters due to uneven light fields in target scenes, an intensity adjustment factor search algorithm is designed. This algorithm eliminates the need for the manual selection of scattering light parameters, effectively solving the problem of uneven illumination in restoration results. A series of experiments demonstrate that, compared to traditional algorithms, the proposed method offers superior detail restoration and higher robustness. [ABSTRACT FROM AUTHOR]

The diagnostic horizon in a soil is reflective of the environment in which it developed and the inherent characteristics of the material, therefore quantitative approaches to horizon delineation should focus on the diagnostic horizon. Moreover, it can enable the exchange and transfer of soil information between different taxonomic systems. This study aims to explore the use of deep learning and RGB images to train a soil diagnostic horizon identification model that can help field workers determine soil horizon information quickly, efficiently, easily, and cost-effectively. A total of 331 soil profile images of the main soil categories (five soil orders, including Primosols, Ferrosols, Argosols, Anthrosols, and Cambosols) from Hubei and Jiangxi Provinces were used. Each soil profile image was preprocessed and augmented to 10 images and then inputted into the UNet++ architecture. The mean intersection over union and pixel accuracy of the model were 71.24% and 82.66%, respectively. Results show that the model could accurately identify and delineate the soil diagnostic horizons. Moreover, the model performance varied considerably due to the definition of the horizon and whether the diagnostic conditions applied to a wide range of visual features on RGB images, the number of samples, and the soil characteristics of the study area. [ABSTRACT FROM AUTHOR]

In the subtropical hilly areas of China, a collapsing gully, a particular type of permanent gully, poses a great threat to the productivity and sustainability of the local ecological and agricultural systems. However, few studies have been performed regarding the effects of collapsing gully erosion on soil degradation. The aim of this study was to evaluate the effects of collapsing gully erosion on soil-quality-related physical and chemical properties. The collapsing gullies that were severely affected by erosion processes were considered at three stages (initial, active and stable stages) and corresponding soil samples were collected to analyze the spatial variation of the soil physical and chemical quality at each stage. The changes in the properties were assumed to be considerable in the regions affected by the erosion process compared with those unaffected by this process. Soil physical properties were more susceptible than soil nutrients to collapsing gully erosion in different spatial locations. The soil quality index (SQI) system consists of total nitrogen (TN), total phosphorus (TP), pH, capillary porosity (CP), sand content (SA), soil cohesion (SC) and root density (RD). Collapsing gully erosion was found to affect the soil physical and chemical properties by progressively reducing the SQI. The mean SQI value was the lowest in the active stage of the collapsing gully, with a higher soil degradation. For the different spatial positions in the collapsing gullies, the scour channel showed the lowest SQI value. The limiting indicators varied in the different stages or spatial sites in the collapsing gullies. [ABSTRACT FROM AUTHOR]