Impact of hematite on dust absorption at wavelengths ranging from 0.2 to 1.0 µm: an evaluation of literature data using the T-matrix method.

Hematite is the absorbing mineral component of dust aerosols in the shortwave spectral region. However, dust shortwave absorption related to hematite suffers from significant uncertainties. In this study, we evaluated available hematite complex refractive index data in the literature on determining the dust effective refractive index at wavelengths ranging from 0.2 to 1.0 µm using rigorous T-matrix methods. Both spherical and super-spheroidal dust with hematite inclusions were examined to compute the dust optical properties and associated effective refractive indices. We found that the imaginary part of the effective refractive index retrieved from all available hematite complex refractive index data is larger than the measured effective values from Di Biagio et al. [Atmos. Chem. Phys.19, 15503, (2019)10.5194/acp-19-15503-2019]. The result obtained using the hematite refractive index from Hsu and Matijevic [Appl. Opt.241623 (1985)10.1364/AO.24.001623] is closest to but approximately two times larger than Di Biagio et al. [Atmos. Chem. Phys.19, 15503, (2019)10.5194/acp-19-15503-2019]. Our results emphasize the importance of accurate measurements of mineral refractive indices to clarify the dust absorption enigma.

Recognition and Classification of Ship Images Based on SMS-PCNN Model.

In the field of ship image recognition and classification, traditional algorithms lack attention to the differences between the grain of ship images. The differences in the hull structure of different categories of ships are reflected in the coarse-grain, whereas the differences in the ship equipment and superstructures of different ships of the same category are reflected in the fine-grain. To extract the ship features of different scales, the multi-scale paralleling CNN oriented on ships images (SMS-PCNN) model is proposed in this paper. This model has three characteristics. (1) Extracting image features of different sizes by parallelizing convolutional branches with different receptive fields. (2) The number of channels of the model is adjusted two times to extract features and eliminate redundant information. (3) The residual connection network is used to extend the network depth and mitigate the gradient disappearance. In this paper, we collected open-source images on the Internet to form an experimental dataset and conduct performance tests. The results show that the SMS-PCNN model proposed in this paper achieves 84.79% accuracy on the dataset, which is better than the existing four state-of-the-art approaches. By the ablation experiments, the effectiveness of the optimization tricks used in the model is verified.