For the past two decades, there have been significant efforts to develop methods for object
detection in Remote Sensing (RS) images. In most cases, the datasets for small object …

Water body classification from high-resolution optical remote sensing (RS) images, aiming at
classifying whether each pixel of the image is water or not, has become a hot issue in the …

This article presents a transformer and convolutional neural network (CNN) hybrid deep
neural network for semantic segmentation of very high resolution (VHR) remote sensing …

Multi-scale features fusion plays a critical role in salient object detection. Most of existing
methods have achieved remarkable performance by exploiting various multi-scale features …

Image processing via convolutional neural network (CNN) has been developed rapidly for
remote sensing technology. Moreover, techniques for accurately extracting building …

Deep learning algorithms, especially convolutional neural networks (CNNs), have recently
emerged as a dominant paradigm for high spatial resolution remote sensing (HRS) image …

Building extraction is a challenging research direction in remote sensing image (RSI)
interpretation. Due to the fact that a building has not only its own local structures but also …

Building footprint segmentation from high-resolution remote sensing (RS) images plays a
vital role in urban planning, disaster response, and population density estimation …

Buildings constitute one of the most important landscapes in remote sensing (RS) images
and have been broadly analyzed in a wide range of applications from urban planning to …

Fully automated and volumetric segmentation of critical tumors may play a crucial role in
diagnosis and surgical planning. One of the most challenging tumor segmentation tasks is …