Update README.md

README.md

@@ -235,6 +235,7 @@ or [fastai](https://medium.com/spatial-data-science/deep-learning-for-geospatial
 * [wheelRuts_semanticSegmentation](https://github.com/SmartForest-no/wheelRuts_semanticSegmentation) -> code for 2022 [paper](https://academic.oup.com/forestry/advance-article/doi/10.1093/forestry/cpac023/6627280): Mapping wheel-ruts from timber harvesting operations using deep learning techniques in drone imagery
 * [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
 
 ### 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
@@ -596,7 +597,7 @@ When the object count, but not its shape is required, U-net can be used to treat
 * [R2CNN_Faster-RCNN_Tensorflow](https://github.com/DetectionTeamUCAS/R2CNN_Faster-RCNN_Tensorflow) -> Rotational region detection based on Faster-RCNN
 * [Rotated-RetinaNet](https://github.com/ming71/Rotated-RetinaNet) -> implemented in pytorch, it supports the following datasets: DOTA, HRSC2016, ICDAR2013, ICDAR2015, UCAS-AOD, NWPU VHR-10, VOC2007
 * [OBBDet_Swin](https://github.com/ming71/OBBDet_Swin) -> The sixth place winning solution in 2021 Gaofen Challenge
-* [CG-Net](https://github.com/WeiZongqi/CG-Net) -> Learning Calibrated-Guidance for Object Detection in Aerial Images
+* [CG-Net](https://github.com/WeiZongqi/CG-Net) -> Learning Calibrated-Guidance for Object Detection in Aerial Images. With [paper](https://ieeexplore.ieee.org/abstract/document/9735375)
 * [OrientedRepPoints_DOTA](https://github.com/hukaixuan19970627/OrientedRepPoints_DOTA) -> Oriented RepPoints + Swin Transformer/ReResNet
 * [yolov5_obb](https://github.com/hukaixuan19970627/yolov5_obb) -> yolov5 + Oriented Object Detection
 * [How to Train YOLOv5 OBB](https://blog.roboflow.com/yolov5-for-oriented-object-detection/) -> YOLOv5 OBB tutorial and [YOLOv5 OBB noteboook](https://colab.research.google.com/drive/16nRwsioEYqWFLBF5VpT_NvELeOeupURM#scrollTo=1NZxhXTMWvek)
@@ -611,7 +612,7 @@ When the object count, but not its shape is required, U-net can be used to treat
 * [Detection_and_Recognition_in_Remote_Sensing_Image](https://github.com/whywhs/Detection_and_Recognition_in_Remote_Sensing_Image) -> This work uses PaNet to realize Detection and Recognition in Remote Sensing Image by MXNet
 * [DrBox-v2-tensorflow](https://github.com/ZongxuPan/DrBox-v2-tensorflow) -> tensorflow implementation of DrBox-v2 which is an improved detector with rotatable boxes for target detection in remote sensing images
 * [Rotation-EfficientDet-D0](https://github.com/HsLOL/Rotation-EfficientDet-D0) -> A PyTorch Implementation Rotation Detector based EfficientDet Detector, applied to custom rotation vehicle datasets
-* [DODet](https://github.com/yanqingyao1994/DODet) -> Dual alignment for oriented object detection, uses DOTA dataset
+* [DODet](https://github.com/yanqingyao1994/DODet) -> Dual alignment for oriented object detection, uses DOTA dataset. With [paper](https://ieeexplore.ieee.org/abstract/document/9706434)
 * [GF-CSL](https://github.com/WangJian981002/GF-CSL) -> code for 2022 [paper](https://ieeexplore.ieee.org/document/9776580): Gaussian Focal Loss: Learning Distribution Polarized Angle Prediction for Rotated Object Detection in Aerial Images
 * [simplified_rbox_cnn](https://github.com/SIAnalytics/simplified_rbox_cnn) -> code for 2018 [paper](https://dl.acm.org/doi/10.1145/3274895.3274915): RBox-CNN: rotated bounding box based CNN for ship detection in remote sensing image. Uses Tensorflow object detection API
 * [Polar-Encodings](https://github.com/flyingshan/Learning-Polar-Encodings-For-Arbitrary-Oriented-Ship-Detection-In-SAR-Images) -> code for 2021 [paper](Learning Polar Encodings for Arbitrary-Oriented Ship Detection in SAR Images)
@@ -634,7 +635,7 @@ When the object count, but not its shape is required, U-net can be used to treat
 
 #### Salient object detection
 Detecting the most noticeable or important object in a scene
-* [ACCoNet](https://github.com/MathLee/ACCoNet) -> Adjacent Context Coordination Network for Salient Object Detection in Optical Remote Sensing Images
+* [ACCoNet](https://github.com/MathLee/ACCoNet) -> code for 2022 [paper](https://ieeexplore.ieee.org/abstract/document/9756652): Adjacent Context Coordination Network for Salient Object Detection in Optical Remote Sensing Images
 * [MCCNet](https://github.com/MathLee/MCCNet) -> Multi-Content Complementation Network for Salient Object Detection in Optical Remote Sensing Images
 * [CorrNet](https://github.com/MathLee/CorrNet) -> Lightweight Salient Object Detection in Optical Remote Sensing Images via Feature Correlation
 * [Reading list for deep learning based Salient Object Detection in Optical Remote Sensing Images](https://github.com/MathLee/ORSI-SOD_Summary)
@@ -646,6 +647,7 @@ Detecting the most noticeable or important object in a scene
 * [FSMINet](https://github.com/zxforchid/FSMINet) -> code for 2022 paper: Fully Squeezed Multi-Scale Inference Network for Fast and Accurate Saliency Detection in Optical Remote Sensing Images
 * [AGNet](https://github.com/NuaaYH/AGNet) -> code for 2022 paper: AGNet: Attention Guided Network for Salient Object Detection in Optical Remote Sensing Images
 * [MSCNet](https://github.com/NuaaYH/MSCNet) -> code for 2022 [paper](https://arxiv.org/abs/2205.08959): A lightweight multi-scale context network for salient object detection in optical remote sensing images
+* [GPnet](https://github.com/liuyu1002/GPnet) -> code for 2022 [paper](https://ieeexplore.ieee.org/abstract/document/9687549): Global Perception Network for Salient Object Detection in Remote Sensing Images
 
 #### Object detection - buildings, rooftops & solar panels
 * [Machine Learning For Rooftop Detection and Solar Panel Installment](https://omdena.com/blog/machine-learning-rooftops/) discusses tiling large images and generating annotations from OSM data. Features of the roofs were calculated using a combination of contour detection and classification. [Follow up article using semantic segmentation](https://omdena.com/blog/rooftops-classification/)
@@ -916,6 +918,8 @@ Generally speaking, change detection methods are applied to a pair of images to
 * [dfc2021-msd-baseline](https://github.com/calebrob6/dfc2021-msd-baseline) -> A baseline for the "Multitemporal Semantic Change Detection" track of the 2021 IEEE GRSS Data Fusion Competition
 * [CorrFusionNet](https://github.com/rulixiang/CorrFusionNet) -> code for 2020 [paper](https://arxiv.org/abs/2006.02176): Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion
 * [ChangeDetectionPCAKmeans](https://github.com/rulixiang/ChangeDetectionPCAKmeans) -> MATLAB implementation for Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and k-Means Clustering.
+* [IRCNN](https://github.com/thebinyang/IRCNN) -> code for 2022 [paper](https://ieeexplore.ieee.org/abstract/document/9721897): IRCNN: An Irregular-Time-Distanced Recurrent Convolutional Neural Network for Change Detection in Satellite Time Series
+* [UTRNet](https://github.com/thebinyang/UTRNet) -> An Unsupervised Time-Distance-Guided Convolutional Recurrent Network for Change Detection in Irregularly Collected Images
 
 ## Time series
 More general than change detection, time series observations can be used for applications including improving the accuracy of crop classification, or predicting future patterns & events. Crop yield is very typically application and has its own section below
@@ -1211,6 +1215,7 @@ Efforts to detect falsified images & deepfakes. Also checkout [Synthetic data](h
 * [MLAT](https://github.com/Chen-Yang-Liu/MLAT) -> code for 2022 [paper](https://ieeexplore.ieee.org/document/9709791): Remote-Sensing Image Captioning Based on Multilayer Aggregated Transformer
 * [WordSent](https://github.com/hw2hwei/WordSent) -> code for 2020 [paper](https://ieeexplore.ieee.org/document/9308980): Word–Sentence Framework for Remote Sensing Image Captioning
 * [a-mask-guided-transformer-with-topic-token](https://github.com/Meditation0119/a-mask-guided-transformer-with-topic-token-for-remote-sensing-image-captioning) -> code for 2022 [paper](https://www.mdpi.com/2072-4292/14/12/2939): A Mask-Guided Transformer Network with Topic Token for Remote Sensing Image Captioning
+* [MetaCaptioning](https://github.com/QiaoqiaoYang/MetaCaptioning) -> code for 2022 [paper](https://www.sciencedirect.com/science/article/abs/pii/S0924271622000351): Meta captioning: A meta learning based remote sensing image captioning framework
 
 ## Mixed data learning
 These techniques combine multiple data types, e.g. imagery and text data.
@@ -1476,6 +1481,7 @@ Measure surface contours & locate 3D points in space from 2D images. NeRF stands
 * [sar_snow_melt_timing](https://github.com/egagli/sar_snow_melt_timing) -> notebooks and tools to identify snowmelt timing using timeseries analysis of backscatter of Sentinel-1 C-band SAR
 * [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
 
 ## NVDI - vegetation index
 * Calculated via band math `ndvi = np.true_divide((ir - r), (ir + r))` but challenging due to the size of the imagery
@@ -1602,6 +1608,7 @@ This section contains a short list of datasets relevant to deep learning, partic
 * [earthspy](https://github.com/AdrienWehrle/earthspy) -> Monitor and study any place on Earth and in Near Real-Time (NRT) using the Sentinel Hub services developed by the EO research team at Sinergise
 * [Space2Ground](https://github.com/Agri-Hub/Space2Ground) -> dataset with Space (Sentinel-1/2) and Ground (street-level images) components, annotated with crop-type labels for agriculture monitoring.
 * [sentinel2tools](https://github.com/QuantuMobileSoftware/sentinel2tools) -> downloading & basic processing of Sentinel 2 imagesry. Read [Sentinel2tools: simple lib for downloading Sentinel-2 satellite images](https://medium.com/geekculture/sentinel2tools-simple-lib-for-downloading-sentinel-2-satellite-images-f8a6be3ee894)
+* [open-sentinel-map](https://github.com/VisionSystemsInc/open-sentinel-map) -> The OpenSentinelMap dataset contains Sentinel-2 imagery and per-pixel semantic label masks derived from OpenStreetMap
 
 ## Landsat
 * Long running US program -> see [Wikipedia](https://en.wikipedia.org/wiki/Landsat_program)