Convolutional neural networks (CNNs) have made significant contributions to natural and
remote sensing imaging since the development of deep learning. Scene-level image …

It is hard to use a single label to describe an image for the complexity of remote sensing
scenes. Thus, it is a more general and practical choice to use multilabel image classification …

The recent widespread usage of image resources has led to the availability of a vast amount
of data in for of images. Classifying or labelling images in an automated way is an ongoing …

Neighborhood rough set (NRS) is considered as an effective tool for feature selection and
has been widely used in processing high‐dimensional data. However, most of the existing …

The research focus in remote sensing scene image classification has been recently shifting
towards deep learning (DL) techniques. However, even the state-of-the-art deep-learning …

Multilabel classification in remote sensing is very significant and plays an important role in
extracting valuable information from satellite imagery. Ignoring the distinct information …

Multilabel remote sensing image classification (MLRSIC) has received increasing research
interest. Taking the co-occurrence relationship of multiple labels as additional information …

Label missing and class imbalance problems are two hot research topics in machine
learning, and they have been impeding the improvement of model performance, especially …

Multilabel remote sensing image classification can provide comprehensive object-level
semantic descriptions of remote sensing images. However, most existing methods cannot …

The increasing number and complex content of aerial images have made some recent
methods based on deep learning not fit well with different aerial image processing tasks …