Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …

Over recent years, there has been a rapid development of deep learning (DL) in both
industry and academia fields. However, finding the optimal hyperparameters of a DL model …

Convolutional neural network (CNN)-based methods are widely used in remote sensing
image scene classification and can obtain excellent performances. However, the stacked …

Few-shot image classification has attracted extensive attention, which aims to recognize
unseen classes given only a few labeled samples. Due to the large intraclass variances and …

Building footprint is a primary dataset of an urban geographic information system (GIS)
database. Therefore, it is essential to establish a robust and automated framework for large …

Deep convolutional neural networks have become an indispensable method in remote
sensing image scene classification because of their powerful feature extraction capabilities …

Deep neural network has been successfully applied to remote sensing image scene
classification, which requires a large amount of annotated data for training. However, it is …

Owing to the powerful data representation ability of deep learning (DL) techniques,
tremendous progress has been recently made in hyperspectral image (HSI) classification …

Remote sensing (RS) image scene classification has obtained increasing attention for its
broad application prospects. Conventional fully-supervised approaches usually require a …

The methods for remote sensing image (RSI) scene classification based on deep
convolutional neural networks (DCNNs) have achieved prominent success. However …