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.
1st place solution to the Satellite Remote Sensing Image Change Detection Challenge hosted by SenseTime
A list of awesome remote sensing image captioning resources
The pytorch implementation for "A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection"
Official Implementation of Deep Aerial Image Matching using PyTorch
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.
An Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion
[TCSVT 2022] Official implementation for 'Exploring the Relationship between Center and Neighborhoods: Central Vector Oriented Self-Similarity Network for Hyperspectral Image Classification'
[CVPR'23] Probability-based Global Cross-modal Upsampling for Pansharpening
Deep Convolutional SpatioTemporal Fusion Network (DCSTFN)
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...
A RetinaNet Pytorch Implementation on remote sensing images and has the similar mAP result with RetinaNet in MMdetection.
[TGRS 2023] Official implementation for 'Adaptive Mask Sampling and Manifold to Euclidean Subspace Learning with Distance Covariance Representation for Hyperspectral Image Classification'
Public repository of our IGARSS 2023 submission
[Remote Sensing] A Swin Transformer-based Encoding Booster Integrated in U-shaped Network
MAD IRMAD CVA PCA deeplearning
This is the pytorch implement of the paper "GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution"
This is the pytorch implement of the paper "ConvMambaSR: Leveraging State-Space Models and CNNs in a Dual-Branch Architecture for Remote Sensing Imagery Super-Resolution"
Computer vision and satellite imagery analysis for precision agriculture. Detects crop diseases, monitors soil health, predicts yields, and optimizes irrigation using drone imagery and multispectral data.
Satellite-Image-Alignment-Differencing-and-Segmentation
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