Abstract Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images, time-series, natural …

Remote sensing image scene classification, which aims at labeling remote sensing images
with a set of semantic categories based on their contents, has broad applications in a range …

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 …

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

Remote sensing is mainly used to investigate sites of dams, bridges, and pipelines to locate
construction materials and provide detailed geographic information. In remote sensing …

As a fundamental but challenging task in the interpretation of remote sensing images, scene
classification plays an important role in various applications and has become an active …

Deep learning has achieved excellent performance in remote-sensing image scene
classification, since a large number of datasets with annotations can be applied for training …

Remote sensing scene classification has gained increasing interest in remote sensing
image understanding and feature representation is the crucial factor for classification …

Many remote sensing scene classification algorithms improve their classification accuracy
by additional modules, which increases the parameters and computing overhead of the …

Recognizing smoke in satellite imagery is a critical approach in an Internet of Things (IoT)
system for monitoring forest fires. However, the task remains challenging due to false alarms …