Accurate semantic segmentation of remote sensing data plays a crucial role in the success
of geoscience research and applications. Recently, multimodal fusion-based segmentation …

Real-time semantic segmentation of remote sensing imagery is a challenging task that
requires a tradeoff between effectiveness and efficiency. It has many applications, including …

Accurate and robust headland boundary detection in the field is crucial for formulating
turning strategies for agro-machinery. The headland area is challenging to be detected …

Urban road extraction is important for the applications of urban planning and transportation.
High-resolution image (HRI) has been one of the most popular data sources for extracting …

Few-shot aerial image semantic segmentation is a challenging task that requires precisely
parsing unseen-category objects in query aerial images with limited annotated support …

When conducting land cover classification, it is inevitable to encounter foggy conditions,
which degrades the performance by a large margin. Robustness may be reduced by a …

In this paper, we focus on the problem of contextual aggregation in the semantic
segmentation of aerial images. Current contextual aggregation methods only aggregate …

Remote sensing image registration can benefit from a machine learning method based on
the likelihood of predicting semantic spatial position distributions. Semantic segmentation of …

With the continuous development and popularization of remote sensing technology, remote
sensing images have been widely used in the field of land cover classification. Since remote …

The fusion of optical and synthetic aperture radar (SAR) images is not only a crucial method
for enhancing land use classification tasks but also forms a fundamental basis for the …