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 …

Ecological resources are an important material foundation for the survival, development, and
self-realization of human beings. In-depth and comprehensive research and understanding …

In the convolutional neural network, the precise segmentation of small-scale objects and
object boundaries in remote sensing images is a great challenge. As the model gets deeper …

The progress of obstacle detection via semantic segmentation on unmanned surface
vehicles (USVs) has been significantly lagging behind the developments in the related field …

There are still various challenges in remote sensing semantic segmentation due to objects
diversity and complexity. Transformer-based models have significant advantages in …

In this study, the utilization of drone images and deep learning to monitor the seagrass
habitat, which is important in the marine ecosystem, is evaluated. Two experiments were …

Remote sensing (RS) instance segmentation is an important but challenging task due to
multi-oriented, densely arranged objects and lack of mask annotation. Compared with …

The timely and accurate acquisition of greenhouse information is crucial for strategically
planning modern agriculture. However, existing methods are affected by the close spacing …

Two-dimensional position information of input tokens is essential for transformer-based
semantic segmentation models, especially on high-resolution aerial images. However …

As a method that can incorporate unlabeled data into model training, semi-supervised
semantic segmentation (SSS) can mitigate the burden of manual annotation in geographic …