Localization is all you evaluate: Data leakage in online map** datasets and how to fix it
The task of online map** is to predict a local map using current sensor observations eg
from lidar and camera without relying on a pre-built map. State-of-the-art methods are based …
from lidar and camera without relying on a pre-built map. State-of-the-art methods are based …
Hyperspectral image classification using an encoder-decoder model with depthwise separable convolution, squeeze and excitation blocks
XT Nguyen, GS Tran - Earth Science Informatics, 2024 - Springer
Remote sensing is one of the major domains witnessing the increasingly significant interest
in Hyperspectral image (HSI) classification. One recent approach achieving great success in …
in Hyperspectral image (HSI) classification. One recent approach achieving great success in …
Hyperspectral Image Classification Framework Based on Multichannel Graph Convolutional Networks and Class-Guided Attention Mechanism
H Feng, Y Wang, C Chen, D Xu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Graph convolutional networks (GCNs) can extract features of samples in non-Euclidean
space, which can be used for hyperspectral image (HSI) classification in collaboration with …
space, which can be used for hyperspectral image (HSI) classification in collaboration with …
Hyperspectral Image Classification Based on Double-Branch Multi-Scale Dual-Attention Network
H Zhang, H Liu, R Yang, W Wang, Q Luo, C Tu - Remote Sensing, 2024 - mdpi.com
Although extensive research shows that CNNs achieve good classification results in HSI
classification, they still struggle to effectively extract spectral sequence information from …
classification, they still struggle to effectively extract spectral sequence information from …
Constrained Spectral–Spatial Attention Residual Network and New Cross-Scene Dataset for Hyperspectral Classification
S Li, B Chen, N Wang, Y Shi, G Zhang, J Liu - Electronics, 2024 - mdpi.com
Hyperspectral image classification is widely applied in several fields. Since existing datasets
focus on a single scene, current deep learning-based methods typically divide patches …
focus on a single scene, current deep learning-based methods typically divide patches …
AMBER--Advanced SegFormer for Multi-Band Image Segmentation: an application to Hyperspectral Imaging
Deep learning has revolutionized the field of hyperspectral image (HSI) analysis, enabling
the extraction of complex and hierarchical features. While convolutional neural networks …
the extraction of complex and hierarchical features. While convolutional neural networks …
Enhancing Hyper-Spectral Image Classification with Reinforcement Learning and Advanced Multi-Objective Binary Grey Wolf Optimization.
Hyperspectral (HS) image classification plays a crucial role in numerous areas including
remote sensing (RS), agriculture, and the monitoring of the environment. Optimal band …
remote sensing (RS), agriculture, and the monitoring of the environment. Optimal band …
Analyzing Information Leakage on Video Object Detection Datasets by Splitting Images Into Clusters With High Spatiotemporal Correlation
RBD Figueiredo, HA Mendes - IEEE Access, 2024 - ieeexplore.ieee.org
Random splitting strategy is a common approach for training, testing, and validating object
detection algorithms based on deep learning. Is common for datasets to have images …
detection algorithms based on deep learning. Is common for datasets to have images …