Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective
Image segmentation is a critical and important step in (GEographic) Object-Based Image
Analysis (GEOBIA or OBIA). The final feature extraction and classification in OBIA is highly …
Analysis (GEOBIA or OBIA). The final feature extraction and classification in OBIA is highly …
Remote sensing image segmentation advances: A meta-analysis
The advances in remote sensing sensors during the last two decades have led to the
production of very high spatial resolution multispectral images. In order to adapt to this rapid …
production of very high spatial resolution multispectral images. In order to adapt to this rapid …
Extended vision transformer (ExViT) for land use and land cover classification: A multimodal deep learning framework
The recent success of attention mechanism-driven deep models, like vision transformer (ViT)
as one of the most representatives, has intrigued a wave of advanced research to explore …
as one of the most representatives, has intrigued a wave of advanced research to explore …
Landslide detection using deep learning and object-based image analysis
Recent landslide detection studies have focused on pixel-based deep learning (DL)
approaches. In contrast, intuitive annotation of landslides from satellite imagery is based on …
approaches. In contrast, intuitive annotation of landslides from satellite imagery is based on …
Methods and datasets on semantic segmentation for Unmanned Aerial Vehicle remote sensing images: A review
Abstract Unmanned Aerial Vehicle (UAV) has seen a dramatic rise in popularity for remote-
sensing image acquisition and analysis in recent years. It has brought promising results in …
sensing image acquisition and analysis in recent years. It has brought promising results in …
[HTML][HTML] Monitoring and map** vegetation cover changes in arid and semi-arid areas using remote sensing technology: A review
Vegetation cover change is one of the key indicators used for monitoring environmental
quality. It can accurately reflect changes in hydrology, climate, and human activities …
quality. It can accurately reflect changes in hydrology, climate, and human activities …
Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning
Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art
performance on many computer vision tasks (eg, object recognition, object detection …
performance on many computer vision tasks (eg, object recognition, object detection …
[HTML][HTML] UAVs for vegetation monitoring: Overview and recent scientific contributions
This paper reviewed a set of twenty-one original and innovative papers included in a special
issue on UAVs for vegetation monitoring, which proposed new methods and techniques …
issue on UAVs for vegetation monitoring, which proposed new methods and techniques …
Land use and land cover map** using deep learning based segmentation approaches and vhr worldview-3 images
Deep learning-based segmentation of very high-resolution (VHR) satellite images is a
significant task providing valuable information for various geospatial applications …
significant task providing valuable information for various geospatial applications …
[HTML][HTML] A survey of mobile laser scanning applications and key techniques over urban areas
Urban planning and management need accurate three-dimensional (3D) data such as light
detection and ranging (LiDAR) point clouds. The mobile laser scanning (MLS) data, with up …
detection and ranging (LiDAR) point clouds. The mobile laser scanning (MLS) data, with up …