Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

Semantic segmentation of remote sensing imagery has been employed in many
applications and is a key research topic for decades. With the success of deep learning …

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

Sustainable agricultural development is a significant solution with fast population
development through the use of information and communication (ICT) in precision …

Deep learning has largely reshaped remote sensing (RS) research for aerial image
understanding and made a great success. Nevertheless, most of the existing deep models …

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 understanding of high resolution aerial images is of great importance for the task of
automated monitoring in various remote sensing applications. Due to the large within-class …

Lately, with deep learning outpacing the other machine learning techniques in classifying
images, we have witnessed a growing interest of the remote sensing community in …

Accurate and precise brain tumor MR images classification plays important role in clinical
diagnosis and decision making for patient treatment. The key challenge in MR images …

This study aims to develop a deep learning based classification framework for remotely
sensed time series. The experiment was carried out in Yolo County, California, which has a …