SpectralGPT: Spectral remote sensing foundation model
The foundation model has recently garnered significant attention due to its potential to
revolutionize the field of visual representation learning in a self-supervised manner. While …
revolutionize the field of visual representation learning in a self-supervised manner. While …
Mim-istd: Mamba-in-mamba for efficient infrared small target detection
Recently, infrared small-target detection (ISTD) has made significant progress, thanks to the
development of basic models. Specifically, the models combining CNNs with Transformers …
development of basic models. Specifically, the models combining CNNs with Transformers …
Classification via structure-preserved hypergraph convolution network for hyperspectral image
Graph convolutional network (GCN) as a combination of deep learning (DL) and graph
learning has gained increasing attention in hyperspectral image (HSI) classification …
learning has gained increasing attention in hyperspectral image (HSI) classification …
A comprehensive study on the robustness of deep learning-based image classification and object detection in remote sensing: Surveying and benchmarking
Deep neural networks (DNNs) have found widespread applications in interpreting remote
sensing (RS) imagery. However, it has been demonstrated in previous works that DNNs are …
sensing (RS) imagery. However, it has been demonstrated in previous works that DNNs are …
Semantic-cc: Boosting remote sensing image change captioning via foundational knowledge and semantic guidance
Remote sensing image change captioning (RSICC) aims to articulate the changes in objects
of interest within bitemporal remote sensing images using natural language. Given the …
of interest within bitemporal remote sensing images using natural language. Given the …
CBA: Contextual background attack against optical aerial detection in the physical world
J Lian, X Wang, Y Su, M Ma… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Patch-based physical attacks have increasingly aroused concerns. However, most existing
methods focus on obscuring targets captured on the ground, and some of these methods are …
methods focus on obscuring targets captured on the ground, and some of these methods are …
QKSAN: A quantum kernel self-attention network
The Self-Attention Mechanism (SAM) excels at distilling important information from the
interior of data to improve the computational efficiency of models. Nevertheless, many …
interior of data to improve the computational efficiency of models. Nevertheless, many …
PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images
Segment Anything Model (SAM) is an advanced foundational model for image
segmentation, which is gradually being applied to remote sensing images (RSIs). Due to the …
segmentation, which is gradually being applied to remote sensing images (RSIs). Due to the …
Multi-Frequency Graph Convolutional Network with Cross-Modality Mutual Enhancement for Multisource Remote Sensing Data Classification
JY Yang, HC Li, JH Yang, L Pan, Q Du… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The mining of meaningful features and effective fusion of multisource remote sensing (RS)
data have always been the challenging research problems in the joint classification of …
data have always been the challenging research problems in the joint classification of …
Few-Shot Object Detection in Remote Sensing Images via Label-Consistent Classifier and Gradual Regression
Y Liu, Z Pan, J Yang, B Zhang, G Zhou… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
With the abomination of time-consuming or even impractical large-scale labeling, few-shot
object detection (FSOD) based on natural scenes has attracted extensive attention …
object detection (FSOD) based on natural scenes has attracted extensive attention …