Update README.md

Author: robmarkcole

README.md

@@ -162,6 +162,7 @@ or [fastai](https://medium.com/spatial-data-science/deep-learning-for-geospatial
 * [PBDL](https://github.com/Usman1021/PBDL) -> code for 2022 [paper](https://www.mdpi.com/2072-4292/14/23/5913): Patch-Based Discriminative Learning for Remote Sensing Scene Classification
 * [EmergencyNet](https://github.com/ckyrkou/EmergencyNet) -> identify fire and other emergencies from a drone
 * [satellite-deforestation](https://github.com/drewhibbard/satellite-deforestation) -> Using Satellite Imagery to Identify the Leading Indicators of Deforestation, applied to the Kaggle Challenge Understanding the Amazon from Space
+* [RSMLC](https://github.com/marjanstoimchev/RSMLC) -> code for 2023 [paper](https://www.mdpi.com/2072-4292/15/2/538): Deep Network Architectures as Feature Extractors for Multi-Label Classification of Remote Sensing Images
 
 ## Segmentation
  Segmentation will assign a class label to each **pixel** in an image. Single class models are often trained for road or building segmentation, with multi class for land use/crop type classification. **Note** that many articles which refer to 'hyperspectral land classification' are actually describing semantic segmentation. Note that cloud detection can be addressed with semantic segmentation and has its own section [Cloud detection & removal](https://github.com/robmarkcole/satellite-image-deep-learning#cloud-detection--removal). Read more about segmentation in my post [A brief introduction to satellite image segmentation with neural networks](https://www.satellite-image-deep-learning.com/p/a-brief-introduction-to-satellite-365)
@@ -1424,6 +1425,7 @@ Federated learning is a process for training models in a distributed fashion wit
 Image registration is the process of registering one or more images onto another (typically well georeferenced) image. Traditionally this is performed manually by identifying control points (tie-points) in the images, for example using QGIS. This section lists approaches which mostly aim to automate this manual process. There is some overlap with the data fusion section but the distinction I make is that image registration is performed as a prerequisite to downstream processes which will use the registered data as an input.
 * [Wikipedia article on registration](https://en.wikipedia.org/wiki/Image_registration) -> register for change detection or [image stitching](https://mono.software/2018/03/14/Image-stitching/)
 * [Phase correlation](https://en.wikipedia.org/wiki/Phase_correlation) is used to estimate the XY translation between two images with sub-pixel accuracy. Can be used for accurate registration of low resolution imagery onto high resolution imagery, or to register a [sub-image on a full image](https://www.mathworks.com/help/images/registering-an-image-using-normalized-cross-correlation.html) -> Unlike many spatial-domain algorithms, the phase correlation method is resilient to noise, occlusions, and other defects. With [additional pre-processing](https://scikit-image.org/docs/dev/auto_examples/registration/plot_register_rotation.html) image rotation and scale changes can also be calculated.
+  * [How to Co-Register Temporal Stacks of Satellite Images](https://medium.com/sentinel-hub/how-to-co-register-temporal-stacks-of-satellite-images-5167713b3e0b)
 * [ImageRegistration](https://github.com/jandremarais/ImageRegistration) -> Interview assignment for multimodal image registration using SIFT
 * [imreg_dft](https://github.com/matejak/imreg_dft) -> Image registration using discrete Fourier transform. Given two images it can calculate the difference between scale, rotation and position of imaged features. Used by the [up42 co-registration service](https://up42.com/marketplace/blocks/processing/up42-coregistration)
 * [arosics](https://danschef.git-pages.gfz-potsdam.de/arosics/doc/about.html) -> Perform automatic subpixel co-registration of two satellite image datasets using phase-correlation, XY translations only.
@@ -1648,7 +1650,7 @@ Training data can be hard to acquire, particularly for rare events such as chang
 
 # Deep learning projects & frameworks
 * [TorchGeo](https://github.com/microsoft/torchgeo) -> a PyTorch domain library providing datasets, samplers, transforms, and pre-trained models specific to geospatial data, supported by Microsoft. Read [Geospatial deep learning with TorchGeo](https://pytorch.org/blog/geospatial-deep-learning-with-torchgeo/)
-* [rastervision](https://docs.rastervision.io/) -> An open source Python framework for building computer vision models on aerial, satellite, and other large imagery sets
+* [rastervision](https://rastervision.io/) -> An open source Python framework for building computer vision models on aerial, satellite, and other large imagery sets
 * [GeoTorchAI](https://github.com/DataSystemsLab/GeoTorchAI) -> A Deep Learning and Scalable Data Processing Framework for Raster and Spatio-Temporal Datasets
 * [torchrs](https://github.com/isaaccorley/torchrs) -> PyTorch implementation of popular datasets and models in remote sensing tasksenhance) -> Enhance PyTorch vision for semantic segmentation, multi-channel images and TIF file
 * [DeepHyperX](https://github.com/eecn/Hyperspectral-Classification) -> A Python/pytorch tool to perform deep learning experiments on various hyperspectral datasets