Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

JE Ball, DT Anderson, CS Chan - Journal of applied remote …, 2017 - spiedigitallibrary.org
In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the
top in numerous areas, namely computer vision (CV), speech recognition, and natural …

Deep learning for remote sensing data: A technical tutorial on the state of the art

L Zhang, L Zhang, B Du - IEEE Geoscience and remote …, 2016 - ieeexplore.ieee.org
Deep-learning (DL) algorithms, which learn the representative and discriminative features in
a hierarchical manner from the data, have recently become a hotspot in the machine …

Wide-area land cover mapping with Sentinel-1 imagery using deep learning semantic segmentation models

S Šćepanović, O Antropov, P Laurila… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
Land cover (LC) mapping is essential for monitoring the environment and understanding the
effects of human activities on it. Recent studies demonstrated successful applications of …

Transferring pre-trained deep CNNs for remote scene classification with general features learned from linear PCA network

J Wang, C Luo, H Huang, H Zhao, S Wang - Remote Sensing, 2017 - mdpi.com
Deep convolutional neural networks (CNNs) have been widely used to obtain high-level
representation in various computer vision tasks. However, in the field of remote sensing …

A multiscale fuzzy dual-domain attention network for urban remote sensing image segmentation

Q Chong, J Xu, F Jia, Z Liu, W Yan… - International Journal of …, 2022 - Taylor & Francis
Semantic segmentation of high-resolution remote sensing images plays an important role in
the remote sensing community. However, many indistinguishable objects are prevalent …

Hashing for localization (HfL): A baseline for fast localizing objects in a large-scale scene

L Han, P Li, A Plaza, P Ren - IEEE Transactions on Geoscience …, 2021 - ieeexplore.ieee.org
Advanced remote-sensing instruments produce massively large scenes from the surface of
the earth, with very high spatial resolution and dimensionality. Developing methods for …

RiSSNet: Contrastive Learning Network with a Relaxed Identity Sampling Strategy for Remote Sensing Image Semantic Segmentation

H Li, W Jing, G Wei, K Wu, M Su, L Liu, H Wu, P Li, J Qi - Remote Sensing, 2023 - mdpi.com
Contrastive learning techniques make it possible to pretrain a general model in a self-
supervised paradigm using a large number of unlabeled remote sensing images. The core …

Predicting poverty level from satellite imagery using deep neural networks

V Chitturi, Z Nabulsi - arXiv preprint arXiv:2112.00011, 2021 - arxiv.org
Determining the poverty levels of various regions throughout the world is crucial in
identifying interventions for poverty reduction initiatives and directing resources fairly …

MGFN: A multi-granularity fusion convolutional neural network for remote sensing scene classification

Z Zeng, X Chen, Z Song - IEEE Access, 2021 - ieeexplore.ieee.org
Convolutional neural networks (CNNs) have been successfully used in remote sensing
scene classification and identification due to their ability to capture deep spatial feature …

Using the improved mask R-CNN and softer-NMS for target segmentation of remote sensing image

Y Wang, Y Rao, C Huang, Y Yang… - 2021 4th International …, 2021 - ieeexplore.ieee.org
Recently, the combination of remote sensing image processing and deep learning methods
is an increasingly popular trend. In this paper, we combine the existing instance …