Update README.md

README.md

@@ -236,6 +236,8 @@ or [fastai](https://medium.com/spatial-data-science/deep-learning-for-geospatial
 * [LWN-for-UAVRSI](https://github.com/syliudf/LWN-for-UAVRSI) -> Light-Weight Semantic Segmentation Network for UAV Remote Sensing Images, applied to Vaihingen, UAVid and UDD6 datasets
 * [hypernet](https://github.com/ESA-PhiLab/hypernet) -> library which implements; accurate hyperspectral image (HSI) segmentation and analysis using deep neural networks, optimization of deep neural network architectures for hyperspectral data segmentation, hyperspectral data augmentation, validation of existent and emerging HSI segmentation algorithms, simulation of multispectral data using HSI
 * [ST-UNet](https://github.com/XinnHe/ST-UNet) -> code for 2022 [paper](https://ieeexplore.ieee.org/abstract/document/9686686): Swin Transformer Embedding UNet for Remote Sensing Image Semantic Segmentation
+* [EDFT](https://github.com/h1063135843/EDFT) -> code for 2022 [paper](https://www.mdpi.com/2072-4292/14/5/1294): Efficient Depth Fusion Transformer for Aerial Image Semantic Segmentation
+* [WiCoNet](https://github.com/ggsDing/WiCoNet) -> code for 2022 [paper](https://ieeexplore.ieee.org/document/9759447): Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images
 
 ### Segmentation - Land use & land cover
 * [nga-deep-learning](https://github.com/jordancaraballo/nga-deep-learning) -> performs semantic segmentation on high resultion GeoTIF data using a modified U-Net & Keras, published by NASA researchers
@@ -570,6 +572,7 @@ Several different techniques can be used to count the number of objects in an im
 * [NWD](https://github.com/jwwangchn/NWD) -> code for 2021 [paper](https://arxiv.org/abs/2110.13389): A Normalized Gaussian Wasserstein Distance for Tiny Object Detection. Uses AI-TOD dataset
 * [MSFC-Net](https://github.com/ZhAnGToNG1/MSFC-Net) -> code for 2021 [paper](https://ieeexplore.ieee.org/document/9535169): Multiscale Semantic Fusion-Guided Fractal Convolutional Object Detection Network for Optical Remote Sensing Imagery
 * [LO-Det](https://github.com/Shank2358/LO-Det) -> code for the 2021 [paper](https://ieeexplore.ieee.org/document/9390310): LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images
+* [R2IPoints](https://github.com/shnew/R2IPoints) -> code for 2022 [paper](https://ieeexplore.ieee.org/abstract/document/9770816): R²IPoints: Pursuing Rotation-Insensitive Point Representation for Aerial Object Detection
 
 #### Object counting
 When the object count, but not its shape is required, U-net can be used to treat this as an image-to-image translation problem.
@@ -704,6 +707,7 @@ Detecting the most noticeable or important object in a scene
 * [Ship-Detection-based-on-YOLOv3-and-KV260](https://github.com/xlsjdjdk/Ship-Detection-based-on-YOLOv3-and-KV260) -> entry project of the Xilinx Adaptive Computing Challenge 2021. It uses YOLOv3 for ship target detection in optical remote sensing images, and deploys DPU on the KV260 platform to achieve hardware acceleration
 * [LEVIR-Ship](https://github.com/WindVChen/LEVIR-Ship) -> a dataset for tiny ship detection under medium-resolution remote sensing images
 * [Push-and-Pull-Network](https://github.com/WindVChen/Push-and-Pull-Network) -> code for 2022 paper: Contrastive Learning for Fine-grained Ship Classification in Remote Sensing Images
+* [DRENet](https://github.com/WindVChen/DRENet) -> code for 2022 [paper])(https://ieeexplore.ieee.org/abstract/document/9791363): A Degraded Reconstruction Enhancement-Based Method for Tiny Ship Detection in Remote Sensing Images With a New Large-Scale Dataset
 
 #### Object detection - cars, vehicles & trains
 * [Truck Detection with Sentinel-2 during COVID-19 crisis](https://github.com/hfisser/Truck_Detection_Sentinel2_COVID19) -> moving objects in Sentinel-2 data causes a specific reflectance relationship in the RGB, which looks like a rainbow, and serves as a marker for trucks. Improve accuracy by only analysing roads. Not using object detection but relevant. Also see [S2TD](https://github.com/hfisser/S2TD)
@@ -717,6 +721,7 @@ Detecting the most noticeable or important object in a scene
 * [Rotation-EfficientDet-D0](https://github.com/HsLOL/Rotation-EfficientDet-D0) -> PyTorch implementation of Rotated EfficientDet, applied to a custom rotation vehicle dataset (car counting)
 * [RSVC2021-Dataset](https://github.com/YinongGuo/RSVC2021-Dataset) -> A dataset for Vehicle Counting in Remote Sensing images, created from the DOTA & [ITCVD](https://research.utwente.nl/en/datasets/itcvd-dataset) Datasets
 * [Car Localization and Counting with Overhead Imagery, an Interactive Exploration](https://medium.com/the-downlinq/car-localization-and-counting-with-overhead-imagery-an-interactive-exploration-9d5a029a596b) -> Medium article by Adam Van Etten
+* [Vehicle-Counting-in-Very-Low-Resolution-Aerial-Images](https://github.com/hbsszq/Vehicle-Counting-in-Very-Low-Resolution-Aerial-Images) -> code for 2022 [paper](https://ieeexplore.ieee.org/abstract/document/9775767): Vehicle Counting in Very Low-Resolution Aerial Images via Cross-Resolution Spatial Consistency and Intraresolution Time Continuity
 
 #### Object detection - planes & aircraft
 * [yoltv4](https://github.com/avanetten/yoltv4) includes examples on the [RarePlanes dataset](https://registry.opendata.aws/rareplanes/)
@@ -837,6 +842,7 @@ Generally treated as a semantic segmentation problem or custom features created
 * [HRC_WHU](https://github.com/dr-lizhiwei/HRC_WHU) -> High-Resolution Cloud Detection Dataset comprising 150 RGB images and a resolution varying from 0.5 to 15 m in different global regions
 * [MEcGANs](https://github.com/andrzejmizera/MEcGANs) -> Cloud Removal from Satellite Imagery using Multispectral Edge-filtered Conditional Generative Adversarial Networks
 * [CloudXNet](https://github.com/shyamfec/CloudXNet) -> code for 2020 [paper](https://www.sciencedirect.com/science/article/abs/pii/S2352938520303803): CloudX-net: A robust encoder-decoder architecture for cloud detection from satellite remote sensing images
+* [refined-unet-lite](https://github.com/92xianshen/refined-unet-lite) -> code for 2022 [paper](https://www.sciencedirect.com/science/article/pii/S1877050922005361): Refined UNet Lite: End-to-End Lightweight Network for Edge-precise Cloud Detection
 
 ## Change detection
 Generally speaking, change detection methods are applied to a pair of images to generate a mask of change, e.g. of buildings damaged in a disaster. Note, clouds & shadows change often too..!
@@ -885,7 +891,7 @@ Generally speaking, change detection methods are applied to a pair of images to
 * [DSMSCN](https://github.com/I-Hope-Peace/DSMSCN) -> Tensorflow implementation for Change Detection in Multi-temporal VHR Images Based on Deep Siamese Multi-scale Convolutional Neural Networks
 * [RaVAEn](https://github.com/spaceml-org/RaVAEn) -> a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs) with the specific purpose of on-board deployment.  It flags changed areas to prioritise for downlink, shortening the response time
 * [SemiCD](https://github.com/wgcban/SemiCD) -> Code for [paper](https://arxiv.org/abs/2204.08454): Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images. Achieves the performance of supervised CD even with access to as little as 10% of the annotated training data
-* [FCCDN_pytorch](https://github.com/chenpan0615/FCCDN_pytorch) -> code for paper: FCCDN: Feature Constraint Network for VHR Image Change Detection. Uses the [LEVIR-CD](https://justchenhao.github.io/LEVIR/) building change detection dataset
+* [FCCDN_pytorch](https://github.com/chenpan0615/FCCDN_pytorch) -> code for [paper](https://www.sciencedirect.com/science/article/abs/pii/S0924271622000636): FCCDN: Feature Constraint Network for VHR Image Change Detection. Uses the [LEVIR-CD](https://justchenhao.github.io/LEVIR/) building change detection dataset
 * [INLPG_Python](https://github.com/zcsisiyao/INLPG_Python) -> code for paper: Structure Consistency based Graph for Unsupervised Change Detection with Homogeneous and Heterogeneous Remote Sensing Images
 * [NSPG_Python](https://github.com/zcsisiyao/NSPG_Python) -> code for paper: Nonlocal patch similarity based heterogeneous remote sensing change detection
 * [LGPNet-BCD](https://github.com/TongfeiLiu/LGPNet-BCD) -> code for 2021 paper: Building Change Detection for VHR Remote Sensing Images via Local-Global Pyramid Network and Cross-Task Transfer Learning Strategy
@@ -1074,7 +1080,7 @@ Note that nearly all the MISR publications resulted from the [PROBA-V Super Reso
 * [deepsum](https://github.com/diegovalsesia/deepsum) -> Deep neural network for Super-resolution of Unregistered Multitemporal images (ESA PROBA-V challenge)
 * [3DWDSRNet](https://github.com/frandorr/3DWDSRNet) -> code to reproduce Satellite Image Multi-Frame Super Resolution (MISR) Using 3D Wide-Activation Neural Networks
 * [RAMS](https://github.com/EscVM/RAMS) -> Official TensorFlow code for paper Multi-Image Super Resolution of Remotely Sensed Images Using Residual Attention Deep Neural Networks
-* [TR-MISR](https://github.com/Suanmd/TR-MISR) ->  Transformer-based MISR framework for the the PROBA-V super-resolution challenge
+* [TR-MISR](https://github.com/Suanmd/TR-MISR) ->  Transformer-based MISR framework for the the PROBA-V super-resolution challenge. With [paper](https://ieeexplore.ieee.org/abstract/document/9684717)
 * [HighRes-net](https://github.com/ElementAI/HighRes-net) -> Pytorch implementation of HighRes-net, a neural network for multi-frame super-resolution, trained and tested on the European Space Agency’s Kelvin competition
 * [ProbaVref](https://github.com/centreborelli/ProbaVref) -> Repurposing the Proba-V challenge for reference-aware super resolution
 * [The missing ingredient in deep multi-temporal satellite image super-resolution](https://towardsdatascience.com/the-missing-ingredient-in-deep-multi-temporal-satellite-image-super-resolution-78cac0f063d9) -> Permutation invariance harnesses the power of ensembles in a single model, with repo [piunet](https://github.com/diegovalsesia/piunet)
@@ -1114,6 +1120,8 @@ Image fusion of low res multispectral with high res pan band.
 * [S2S_UCNN](https://github.com/hvn2/S2S_UCNN) -> code for 2021 [paper](https://ieeexplore.ieee.org/document/9464640): Sentinel 2 sharpening using a single unsupervised convolutional neural network with MTF-Based degradation model
 * [SSE-Net](https://github.com/RSMagneto/SSE-Net) -> code for 2022 [paper](https://ieeexplore.ieee.org/abstract/document/9810290): Spatial and Spectral Extraction Network With Adaptive Feature Fusion for Pansharpening
 * [UCGAN](https://github.com/zhysora/UCGAN) -> code for 2022 [paper](https://ieeexplore.ieee.org/abstract/document/9755137): Unsupervised Cycle-consistent Generative Adversarial Networks for Pan-sharpening
+* [GCPNet](https://github.com/Keyu-Yan/GCPNet) -> code for 2022 [paper](https://ieeexplore.ieee.org/abstract/document/9758796): When Pansharpening Meets Graph Convolution Network and Knowledge Distillation
+* [PanFormer](https://github.com/zhysora/PanFormer) -> code for 2022 [paper](https://arxiv.org/abs/2203.02916): PanFormer: a Transformer Based Model for Pan-sharpening
 
 ## Image-to-image translation
 Translate images e.g. from SAR to RGB.
@@ -1488,6 +1496,7 @@ Measure surface contours & locate 3D points in space from 2D images. NeRF stands
 * [Denoising radar satellite images using deep learning in Python](https://medium.com/@petebch/denoising-radar-satellite-images-using-deep-learning-in-python-946daad31022) -> Medium article on [deepdespeckling](https://github.com/hi-paris/deepdespeckling)
 * [random-wetlands](https://github.com/ekcomputer/random-wetlands) -> Random forest classification for wetland vegetation from synthetic aperture radar dataset
 * [AGSDNet](https://github.com/RTSIR/AGSDNet) -> code for 2022 [paper](https://ieeexplore.ieee.org/abstract/document/9755131): AGSDNet: Attention and Gradient-Based SAR Denoising Network
+* [LFG-Net](https://github.com/Evarray/LFG-Net) -> code for 2022 [paper](https://ieeexplore.ieee.org/abstract/document/9815311): LFG-Net: Low-Level Feature Guided Network for Precise Ship Instance Segmentation in SAR Images
 
 ## NVDI - vegetation index
 * Calculated via band math `ndvi = np.true_divide((ir - r), (ir + r))` but challenging due to the size of the imagery
@@ -1904,6 +1913,7 @@ Since there is a whole community around GEE I will not reproduce it here but lis
 * [MtS-WH-Dataset](https://github.com/rulixiang/MtS-WH-Dataset) -> Multi-temporal Scene WuHan (MtS-WH) Dataset
 * [Multi-modality-image-matching](https://github.com/StaRainJ/Multi-modality-image-matching-database-metrics-methods) -> image matching dataset including several remote sensing modalities
 * [Canadian-cropland-dataset](https://github.com/bioinfoUQAM/Canadian-cropland-dataset) -> a novel patch-based dataset compiled using optical satellite images of Canadian agricultural croplands retrieved from Sentinel-2
+* [RID](https://github.com/TUMFTM/RID) -> Roof Information Dataset for CV-Based Photovoltaic Potential Assessment. With [paper](https://www.mdpi.com/2072-4292/14/10/2299)
 
 ## Kaggle
 Kaggle hosts over > 200 satellite image datasets, [search results here](https://www.kaggle.com/search?q=satellite+image+in%3Adatasets).