Retinal vessel segmentation has important application value in clinical diagnosis. If experts manually segment the retinal vessels, the workload is heavy, and the result is strong subjectively. However, some existing automatic segmentation methods have the problems of incomplete vessel segmentation and low‐segmentation accuracy. In order to solve the above problems, this study proposes a retinal vessel segmentation method based on task‐driven generative adversarial network (GAN). In the generative model, a U‐Net network is used to segment the retinal vessels. In the discriminative model, multi‐scale discriminators with different receptive fields are used to guide the generative model to generate more details. On the other hand, in view of the uncontrollable characteristics of the data generated by the traditional GAN, a task‐driven model based on perceptual loss is added to traditional GAN for feature matching, which makes the generated image more task‐specific. Experimental results show that the accuracy, sensitivity, specificity and area under the receiver operating characteristic curve of the proposed method on data set digital retinal images for vessel extraction are 96.83, 80.66, 98.97 and 0.9830%, respectively.
Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency.
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