Update README.md

README.md

@@ -466,6 +466,7 @@ Semantic Segmentation
 * [UBC-dataset](https://github.com/AICyberTeam/UBC-dataset) -> a dataset for building detection and classification from very high-resolution satellite imagery with the focus on object-level interpretation of individual buildings
 * [GeoSeg](https://github.com/WangLibo1995/GeoSeg) -> code for 2022 [paper](https://www.sciencedirect.com/science/article/pii/S0924271622001654): UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery
 * [BESNet](https://github.com/FlyC235/BESNet) -> code for 2022 [paper](https://www.mdpi.com/2072-4292/14/7/1638): BES-Net: Boundary Enhancing Semantic Context Network for High-Resolution Image Semantic Segmentation. Applied to Vaihingen and Potsdam datasets
+* [CVNet](https://github.com/xzq-njust/CVNet) -> code for 2022 paper: CVNet: Contour Vibratioin Network for Building Extraction
 
 ### Segmentation - Solar panels
 * [DeepSolar](https://github.com/wangzhecheng/DeepSolar) -> A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States. [Dataset on kaggle](https://www.kaggle.com/tunguz/deep-solar-dataset), actually used a CNN for classification and segmentation is obtained by applying a threshold to the activation map. Original code is tf1 but [tf2/kers](https://github.com/aidan-fitz/deepsolar-v2) and a [pytorch implementation](https://github.com/wangzhecheng/deepsolar_pytorch) are available. Also checkout [Visualizations and in-depth analysis .. of the factors that can explain the adoption of solar energy in ..  Virginia](https://github.com/bessammehenni/DeepSolar_adoption_Virginia) and [DeepSolar tracker: towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping](https://github.com/gabrielkasmi/dsfrance)
@@ -579,6 +580,7 @@ When the object count, but not its shape is required, U-net can be used to treat
 * [Cassava Crop Counting](https://medium.com/@wongsirikuln/cassava-standing-crop-counting-869cca486ce3)
 * [Counting from Sky](https://github.com/gaoguangshuai/Counting-from-Sky-A-Large-scale-Dataset-for-Remote-Sensing-Object-Counting-and-A-Benchmark-Method) -> A Large-scale Dataset for Remote Sensing Object Counting and A Benchmark Method
 * [PSGCNet](https://github.com/gaoguangshuai/PSGCNet) -> code for 2022 [paper](https://arxiv.org/abs/2012.03597): PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote Sensing Images
+* [psgcnet](https://github.com/gaoguangshuai/psgcnet) -> code for 2022 [paper](https://ieeexplore.ieee.org/abstract/document/9720990): PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote-Sensing Images
 
 #### Object detection with rotated bounding boxes
 * OBB: orinted bounding boxes are polygons representing rotated rectangles
@@ -834,6 +836,7 @@ Generally treated as a semantic segmentation problem or custom features created
 * [Cloud-Segmentation-from-Satellite-Imagery](https://github.com/vedantk-b/Cloud-Segmentation-from-Satellite-Imagery) -> applied to Sentinel-2 dataset
 * [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
 
 ## 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..!
@@ -876,7 +879,7 @@ Generally speaking, change detection methods are applied to a pair of images to
 * [ChangeDetectionProject](https://github.com/previtus/ChangeDetectionProject) -> Trying out Active Learning in with deep CNNs for Change detection on remote sensing data
 * [DSFANet](https://github.com/rulixiang/DSFANet) -> Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images
 * [siamese-change-detection](https://github.com/mvkolos/siamese-change-detection) -> Targeted synthesis of multi-temporal remote sensing images for change detection using siamese neural networks
-* [Bi-SRNet](https://github.com/ggsDing/Bi-SRNet) -> Pytorch codes of paper: Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images
+* [Bi-SRNet](https://github.com/ggsDing/Bi-SRNet) -> code for 2022 [paper](https://ieeexplore.ieee.org/abstract/document/9721305): Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images
 * [RaVAEn](https://github.com/spaceml-org/RaVAEn) -> RaVAEn is a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs) with the specific purpose of on-board deployment
 * [SiROC](https://github.com/lukaskondmann/SiROC) -> Implementation of the [paper](https://ieeexplore.ieee.org/document/9627707) Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images. Applied to Sentinel-2 and high-resolution Planetscope imagery on four datasets
 * [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
@@ -1109,6 +1112,8 @@ Image fusion of low res multispectral with high res pan band.
 * [LPPN](https://github.com/ChengJin-git/LPPN) -> code for 2021 [paper](https://www.sciencedirect.com/science/article/abs/pii/S1566253521001809): Laplacian pyramid networks: A new approach for multispectral pansharpening
 * [S2_SSC_CNN](https://github.com/hvn2/S2_SSC_CNN) -> code for 2020 [paper](https://ieeexplore.ieee.org/document/9323614): Zero-shot Sentinel-2 Sharpening Using A Symmetric Skipped Connection Convolutional Neural Network
 * [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
 
 ## Image-to-image translation
 Translate images e.g. from SAR to RGB.