Rethinking remote sensing pretrained model: Instance-aware visual prompting for remote sensing scene classification
Large-scale pretrained models, such as vision transformers (ViTs), have made significant
progress in remote sensing (RS) scene classification tasks. For a new scene classification …
progress in remote sensing (RS) scene classification tasks. For a new scene classification …
SOCNet: A lightweight and fine-grained object recognition network for satellite on-orbit computing
High-quality remote sensing images (RSIs) not only provide opportunities for deep-learning-
based image interpretation but also challenge satellite edge devices for storage, processing …
based image interpretation but also challenge satellite edge devices for storage, processing …
Tpenas: A two-phase evolutionary neural architecture search for remote sensing image classification
The application of deep learning in remote sensing image classification has been paid more
and more attention by industry and academia. However, manually designed remote sensing …
and more attention by industry and academia. However, manually designed remote sensing …
Improvement of the model of object recognition in aero photographs using deep convolutional neural networks
Detection and recognition of objects in images is the main problem to be solved by computer
vision systems. As part of solving this problem, the model of object recognition in aerial …
vision systems. As part of solving this problem, the model of object recognition in aerial …
MSE-Net: A novel master–slave encoding network for remote sensing scene classification
Remote sensing scene (RSS) image classification plays a vital role in various fields such as
urban planning and environmental protection. However, due to higher inter-class similarity …
urban planning and environmental protection. However, due to higher inter-class similarity …
TAKD: Target-Aware Knowledge Distillation for Remote Sensing Scene Classification
Remote sensing (RS) scene classification based on deep neural networks (DNNs) has
recently drawn remarkable attention. However, the DNNs contain a great number of …
recently drawn remarkable attention. However, the DNNs contain a great number of …
DCNNet: a distributed convolutional neural network for remote sensing image classification
With the development of information technology, multiplatform collaborative collection and
processing of remote sensing (RS) images has become a significant trend. However, the …
processing of remote sensing (RS) images has become a significant trend. However, the …
Remote sensing scene image classification based on dense fusion of multi-level features
C Shi, X Zhang, J Sun, L Wang - Remote Sensing, 2021 - mdpi.com
For remote sensing scene image classification, many convolution neural networks improve
the classification accuracy at the cost of the time and space complexity of the models. This …
the classification accuracy at the cost of the time and space complexity of the models. This …
RepSViT: An Efficient Vision Transformer Based on Spiking Neural Networks for Object Recognition in Satellite On-Orbit Remote Sensing Images
The role of on-orbit computing for satellites is transitioning from being a backup measure to
becoming a primary key function. However, the limited computing resources available on …
becoming a primary key function. However, the limited computing resources available on …
EAS-CNN: automatic design of convolutional neural network for remote sensing images semantic segmentation
Accurate and effective semantic segmentation methods for remote sensing are important for
applications such as precision agriculture, urban planning, and disaster monitoring …
applications such as precision agriculture, urban planning, and disaster monitoring …