Graph convolutional network (GCN) has attracted much attention in the field of hyperspectral
image classification for its excellent feature representation and convolution on arbitrarily …

The intelligent segmentation of high-resolution remote sensing (HRS) image, also called as
dense prediction task for HRS image, has been and will continue to be important research in …

Semantic segmentation is crucial for a wide range of downstream applications in remote
sensing, aiming to classify pixels in remote sensing images (RSIs) at the semantic level. The …

Accurate detection of early diseased plants is of great significance for high quality and high
yield of crops, as well as cultivation management. Aiming at the low accuracy of the …

ABSTRACT Sea-Land Segmentation (SLS) of remote sensing images is a meaningful task
in the remote sensing and computer vision community. Some tricky situations, such as …

The detection of black and odorous water using remote sensing technology has become an
effective method. The high-resolution remote sensing images can extract target features …

Due to being affected by factors such as imaging distance, lighting, ground features, and
environment, objects in the same class may have certain differences, and different classes of …

Deep learning and fuzzy models provide powerful and practical techniques for solving large-
scale deep-learning tasks. The fusion technique on deep learning and fuzzy system are …

Picture fuzzy C-means clustering is a novel computational intelligence method that has
some advantages over fuzzy clustering in pattern analysis and machine intelligence …

To address issues in bull face detection, such as unsatisfactory inspection results and fragile
inspection devices, we conducted a comparative study on existing representative deep …