Recent technological advancements in satellite imagery have increased the production of remotely sensed images. Therefore, developing efficient methods for annotating these images has gained popularity. Most of the current state-of-the-art methods are based on supervised machine learning techniques. We propose a method called semisupervised multi-label annotizer (SS-MLA) that adapts vector-quantized temporal associative memory to annotate remotely sensed images. One of the advantages of SS-MLA over the supervised methods is that it extracts features not only from the given sample but also from similar samples that are previously seen without using an explicit attention mechanism. Thus SS-MLA enhances the learning efficiency of the training process. We conduct extensive performance comparisons with five different methods in the literature over four datasets. The comparison results indicate the success of the proposed method over the existing ones: SS-MLA generates the best results in 7 out of 11 comparisons.

中文翻译：

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SS-MLA：一种用于遥感图像多标签标注的半监督方法

卫星图像的最新技术进步增加了遥感图像的制作。因此，开发用于注释这些图像的有效方法已经很受欢迎。大多数当前最先进的方法都基于监督机器学习技术。我们提出了一种称为半监督多标签标注器 (SS-MLA) 的方法，该方法采用矢量量化的时间关联记忆来标注遥感图像。SS-MLA 相对于监督方法的优势之一是它不仅从给定的样本中提取特征，而且还从以前在不使用显式注意机制的情况下看到的类似样本中提取特征。因此 SS-MLA 提高了训练过程的学习效率。我们使用文献中的五种不同方法对四个数据集进行了广泛的性能比较。比较结果表明所提出的方法优于现有方法：SS-MLA 在 11 次比较中的 7 次中产生了最好的结果。