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 …

Land-use map** (LUM) using high-spatial resolution remote sensing images (HSR-RSIs)
is a challenging and crucial technology. However, due to the characteristics of HSR-RSIs …

Semantic labeling for very high resolution (VHR) images in urban areas, is of significant
importance in a wide range of remote sensing applications. However, many confusing …

Semantic segmentation requires methods capable of learning high-level features while
dealing with large volume of data. Toward such goal, convolutional networks can learn …

As the acquisition of very high resolution (VHR) satellite images becomes easier owing to
technological advancements, ever more stringent requirements are being imposed on …

We investigate the task of unsupervised domain adaptation in aerial semantic segmentation
observing that there are some shortcomings in the class mixing strategies used by the recent …

In this paper, we investigate the problem of Semantic Segmentation for agricultural aerial
imagery. We observe that the existing methods used for this task are designed without …

Semantic labeling for high resolution aerial images is a fundamental and necessary task in
remote sensing image analysis. It is widely used in land-use surveys, change detection, and …

In traditional semantic segmentation, knowing about all existing classes is essential to yield
effective results with the majority of existing approaches. However, these methods trained in …

Over the past decade, Convolutional Networks (ConvNets) have renewed the perspectives
of the research and industrial communities. Although this deep learning technique may be …