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 …

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 …

From the autonomous car driving to medical diagnosis, the requirement of the task of image
segmentation is everywhere. Segmentation of an image is one of the indispensable tasks in …

Availability of very high-resolution remote sensing images and advancement of deep
learning methods have shifted the paradigm of image classification from pixel-based and …

Deep learning (DL)-based semantic segmentation methods have been providing state-of-
the-art performance in the past few years. More specifically, these techniques have been …

Reliable detection of clouds is a critical pre-processing step in optical satellite based remote
sensing. Currently, most methods are based on classifying invidual pixels from their spectral …

Abstract Analysis of shoreline change is fundamental to a broad range of investigations
undertaken by coastal scientists, coastal engineers, and coastal managers. Multispectral …

Coastlines are important basic geographic elements and map** their spatial and attribute
changes can help monitor, model and manage coastal zones. Traditional studies focused on …

Automatic facial expression recognition (FER) has gained much attention due to its
applications in human-computer interaction. Among the approaches to improve FER tasks …

Deep learning-based coastline detection algorithms have begun to outshine traditional
statistical methods in recent years. However, they are usually trained only as single-purpose …