remote-sensing-image
There are 6 repositories under remote-sensing-image topic.
- awesome-image-registration
Awesome-Image-Registration-Organization / awesome-image-registration
image registration related books, papers, videos, and toolboxes
WangLibo1995 / GeoSeg
UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery, ISPRS. Also, including other vision transformers and CNNs for satellite, aerial image and UAV image segmentation.
Language:Python 537LiheYoung / SenseEarth2020-ChangeDetection
1st place solution to the Satellite Remote Sensing Image Change Detection Challenge hosted by SenseTime
Language:Python 267AnkurDeria / HSI-Traditional-to-Deep-Models
Pytorch and Keras Implementations of Hyperspectral Image Classification -- Traditional to Deep Models: A Survey for Future Prospects.
Language:Jupyter Notebook 150iOPENCap / awesome-remote-image-captioning
A list of awesome remote sensing image captioning resources
Language:Python 66jaehyunnn / DeepAerialMatching_pytorch
Official Implementation of Deep Aerial Image Matching using PyTorch
Language:Python 66likyoo / BAN
The pytorch implementation for "A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection"
Language:Python 62TachibanaYoshino / Remote-sensing-image-semantic-segmentation-tf2
The remote sensing image semantic segmentation repository based on tf.keras includes backbone networks such as resnet, densenet, mobilenet, and segmentation networks such as deeplabv3+, pspnet, panet, and refinenet.
Language:Python 61- edcstfnLanguage:Python 47
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- Language:Python 37
HsLOL / Rotation-EfficientDet-D0
A PyTorch Implementation Rotation Detector based EfficientDet Detector
Language:C++ 35JohnPPinto / Object_Detection_Satellite_Imagery_Yolov8_DIOR
Building a Yolov8n model from scratch and performing object detection in optical remote sensing images and videos.
Language:Jupyter Notebook 34fan0210 / detection_tools
Some Useful Tools for Object Detection in Remote Sensing Imagery. 一些遥感图像目标检测的实用代码及工具，不断更新中...
Language:Python 31A RetinaNet Pytorch Implementation on remote sensing images and has the similar mAP result with RetinaNet in MMdetection.
Language:Python 29lms-07 / CVSSN
[TCSVT 2022] Official implementation for 'Exploring the Relationship between Center and Neighborhoods: Central Vector Oriented Self-Similarity Network for Hyperspectral Image Classification'
Language:Python 24aj1365 / DeepCNN_Polsar
This code is for the paper "PolSAR Image Classification based on Deep Convolutional Neural Networks and Wavelet Transformation" that is published in the IEEE Geoscience and Remote Sensing Letters journal.
Language:Jupyter Notebook 21[TGRS 2023] Official implementation for 'Adaptive Mask Sampling and Manifold to Euclidean Subspace Learning with Distance Covariance Representation for Hyperspectral Image Classification'
Language:Python 16WeiTan1992 / CoF-MSMG-PCNN
Code for "Remote Sensing Image Fusion via Boundary Measured Dual-Channel PCNN in Multi-Scale Morphological Gradient Domain"
Language:MATLAB 13fmenat / MultiviewCropClassification
Public repository of our IGARSS 2023 submission
Language:Python 12WenliangGuo / STEB-UNet
[Remote Sensing] A Swin Transformer-based Encoding Booster Integrated in U-shaped Network
Language:Python 11kandarpkakkad / GUI-For-Hyperspectral-Image-Preprocessing-Using-Python
The GUI will preprocess the hyperspectral images i.e. removes the bands having negative values. The image format allowed is ".ENVI".
Language:HTML 10phoenixtreesky7 / iders_dehazing
IDeRS: Iterative Dehazing Method for Single Remote Sensing Image
Language:MATLAB 10yuxgis / changeDetection
MAD IRMAD CVA PCA deeplearning
Language:Python 8- Language:MATLAB 7
rishi5kesh / Satellite-Image-Alignment-Differencing-and-Segmentation
Satellite-Image-Alignment-Differencing-and-Segmentation
Language:Python 4FilippoMB / Recognition-of-polar-lows-in-Sentinel-1-SAR-images-with-deep-learning
Code implementation for the paper "Recognition of polar lows in Sentinel-1 SAR images with deep learning"
Language:Jupyter Notebook 1- Language:CSS
