Classification of Land Optical Remote Sensing Images UsingWeakly Supervised Learning Algorithm
Due to the complexity and diversity of land details,as well as the difficulty of image acquisition,land features can also change at different times and regions,making the land classification process more complex.To solve this problem,this article presented a method of classifying optical remote sensing land images based on weakly-super-vised learning.First,pseudo-median filtering method was adopted to remove the noise from optical remote sensing im-ages.And then,fuzzy contrast method was utilized to enhance the image contrast.On this basis,a weakly-supervised location network was used to obtain the interest examples.Moreover,the sub-concept layer was introduced into the multi-instance aggregation network to calculate the matching scores between the interest examples and labels.Finally,the classification of land images was completed.The experimental results show that the proposed method is accurate in land classification,and the Kappa coefficient is closer to 1.Therefore,the method has good application performance.
Weakly supervised learningRemote sensing image classificationPseudo median filteringFuzzy con-trastSub-concept learning
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