Recently, deep learning approaches, especially convolutional neural networks (CNNs), have attracted extensive attention in iris recognition. Though CNN-based approaches realize automatic feature extraction and achieve outstanding performance, they usually require more training samples and higher computational complexity than the classic methods. This work focuses on training a novel condensed 2-channel (2-ch) CNN with few training samples for efficient and accurate iris identification and verification. A multi-branch CNN with three well-designed online augmentation schemes and radial attention layers is first proposed as a high-performance basic iris classifier. Then, both branch pruning and channel pruning are achieved by analyzing the weight distribution of the model. Finally, fast finetuning is optionally applied, which can significantly improve the performance of the pruned CNN while alleviating the computational burden. In addition, we further investigate the encoding ability of 2-ch CNN and propose an efficient iris recognition scheme suitable for large database application scenarios. Moreover, the gradient-based analysis results indicate that the proposed algorithm is robust to various image contaminations. We comprehensively evaluated our algorithm on three publicly available iris databases for which the results proved satisfactory for real-time iris recognition.
GF-6 is the first optical remote sensing satellite for precision agriculture observations in China. Accurate identification of the cloud in GF-6 helps improve data availability. However, due to the narrow band range contained in GF-6, Fmask version 3.2 for Landsat is not suitable for GF-6. Hence, this paper proposes an improved Fmask based on the spectral-contextual information to solve the inapplicability of Fmask version 3.2 in GF-6. The improvements are divided into the following six aspects. The shortwave infrared (SWIR) in the “Basic Test” is replaced by blue band. The threshold in the original “HOT Test” is modified based on the comprehensive consideration of fog and thin clouds. The bare soil and rock are detected by the relationship between green and near infrared (NIR) bands. The bright buildings are detected by the relationship between the upper and lower quartiles of blue and red bands. The stratus with high humidity and fog_W (fog over water) are distinguished by the ratio of blue and red edge position 1 bands. Temperature probability for land is replaced by the HOT-based cloud probability (LHOT), and SWIR in brightness probability is replaced by NIR. The average cloud pixels accuracy (TPR) of the improved Fmask is 95.51%.
With the development of deep learning in satellite remote sensing image segmentation, convolutional neural networks have achieved better results than traditional methods. In some full convolutional networks, the number of network layers usually increases to obtain deep features, but the gradient disappearance problem occurs when the number of network layers deepens. Many scholars have obtained multiscale features by using different convolutional calculations. We want to obtain multiscale features in the network structure while obtaining contextual information by other means. This article employs the self-attention mechanism and auxiliary loss network (SAMALNet) structure to solve the above problems. We adopt the self-attention strategy in the atrous spatial pyramid pooling module to extract multiscale features while considering the contextual information. We add auxiliary loss to overcome the gradient disappearance problem. The experimental results of extracting aquaculture areas in the Jiaozhou Bay area of Qingdao from high-resolution GF-2 satellite images show that, in general, SAMALNet achieves better experimental results compared with UPS-Net, SegNet, DeepLabv3, UNet, DeepLabv3+, and PSPNet network structures, including recall 96.34%, precision 95.91%, F1 score 96.12%, and MIoU 92.60%. SAMALNet achieved better results extracting aquaculture area boundaries than the other network structures listed above. The high accuracy of the aquaculture area can provide data support for the rational planning and environmental protection of the coastal aquaculture area and promote more rational usage of the coastal aquaculture area.
With the continuous development of image technology, the quality of image processing has also been greatly improved. But its evaluation index is limited to objective evaluation, which results in high image quality but poor visual quality. However, subjective evaluation does not have specific indicators, it cannot be quantitatively detected, and it is poorly informatized and highly dependent on the observer’s knowledge background and emotions. Therefore, this study has conducted research on subjective evaluation, and proposed to use three-dimensional volume rendering and reconstruction as the auxiliary index of subjective evaluation to achieve the effect of stereoscopic image and reduce the degree of subjective dependence on the observer. The experimental analysis shows that the method has high practical application value and obvious effect, and can be used as an auxiliary index for subjective evaluation.
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