In response to the growing importance of geospatial data, its analysis including semantic
segmentation becomes an increasingly popular task in computer vision today. Convolutional …

We present an end-to-end trainable deep convolutional neural network (DCNN) for semantic
segmentation with built-in awareness of semantically meaningful boundaries. Semantic …

Semantic segmentation based on deep learning has achieved impressive results in recent
years, but these results are supported by a large amount of labeled data, which requires …

Because of its wide potential applications, remote sensing (RS) image semantic
segmentation has attracted increasing research interest in recent years. Until now, deep …

In recent years, with the development of deep learning, semantic segmentation for remote
sensing images has gradually become a hot issue in computer vision. However …

Semantic segmentation of remote sensing imagery (RSI) is critical in many domains due to
the diverse landscapes and different sizes of geo-objects that RSI contains, making …

The applied research in remote sensing images has been pushed by convolutional neural
network (CNN). Because of the fixed size of the perceptual field, CNN is unable to model …

Scene understanding is an important task in information extraction from high-resolution
aerial images, an operation which is often involved in remote sensing applications …

The trade-off between feature representation power and spatial localization accuracy is
crucial for the dense classification/semantic segmentation of remote sensing images (RSIs) …

Foremost deep neural network models trained in natural scenes cannot transfer and apply to
remote sensing image semantic segmentation well. Studies have shown that fine-tuning …