Deep convolutional neural networks (CNNs) have shown their great success on image
classification. CNNs mainly consist of convolutional and pooling layers, both of which are …

In existing convolutional neural networks (CNNs), both convolution and pooling are locally
performed for image regions separately, no contextual dependencies between different …

As the basic building block of Convolutional Neural Networks (CNNs), the convolutional
layer is designed to extract local patterns and lacks the ability to model global context in its …

While the use of bottom-up local operators in convolutional neural networks (CNNs)
matches well some of the statistics of natural images, it may also prevent such models from …

Remote sensing (RS) scene classification plays an important role in a wide range of RS
applications. Recently, convolutional neural networks (CNNs) have been applied to the field …

Transformer with self-attention has led to the revolutionizing of natural language processing
field, and recently inspires the emergence of Transformer-style architecture design with …

Recently, a large number of channel attention blocks are proposed to boost the
representational power of deep convolutional neural networks (CNNs). These approaches …

Recently, many researchers have been dedicated to using convolutional neural networks
(CNNs) to extract global-context features (GCFs) for remote-sensing scene classification …

Scene classification of remote sensing images has drawn great attention because of its wide
applications. In this paper, with the guidance of the human visual system (HVS), we explore …

With the large amount of high-spatial resolution images now available, scene classification
aimed at obtaining high-level semantic concepts has drawn great attention. The …