Image semantic segmentation is one of the most important tasks in the field of computer
vision, and it has made great progress in many applications. Many fully supervised deep …

Scene classification is a highly useful task in Remote Sensing (RS) applications. Many
efforts have been made to improve the accuracy of RS scene classification. Scene …

The dilated convolution algorithm, which is widely used for image segmentation, is applied
in the image classification field in this paper. In many traditional image classification …

Convolutional neural networks (CNNs) have outstanding advantages in the classification of
remote sensing scenes. Deep CNN models with better classification performance typically …

Remote sensing scene categorization (RSSC) is a long-standing, vital, and complex issue in
computer vision. It seeks to classify a scene into one of the predetermined scene groups by …

The current literature of remote sensing (RS) scene classification shows that state-of-the-art
results are achieved using feature extraction methods, where convolutional neural networks …

In recent years, with the rapid development of computer vision, increasing attention has
been paid to remote sensing image scene classification. To improve the classification …

In recent years, convolution neural networks (CNNs) have been widely used in the field of
remote sensing scene image classification. However, CNN models with good classification …

The efficient classification of remote sensing images (RSIs) has become the key of remote
sensing application. To tackle the high computational cost in the traditional classification …

Convolutional neural networks (CNNs) have been widely used in remote sensing scene
classification due to their excellent performance in natural image classification. However, the …