Add Anomaly detection

README.md

@@ -60,6 +60,7 @@ Orbuculum's potential extends far beyond the reinvention of the GIS/EO research
 - Data fusion
 - Generative networks
 - Autoencoders, dimensionality reduction, image embeddings & similarity search
+- Anomaly detection
 - Image retrieval
 - Image Captioning
 - Visual Question Answering
@@ -981,8 +982,6 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 
 - [clusternet_segmentation](https://github.com/zhygallo/clusternet_segmentation) -> Unsupervised Segmentation by applying K-Means clustering to the features generated by Neural Network
 
-- [TDD](https://github.com/Jingtao-Li-CVer/TDD) -> One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning
-
 - [Efficient-Transformer](https://github.com/zyxu1996/Efficient-Transformer) -> Efficient Transformer for Remote Sensing Image Segmentation
 
 - [weakly_supervised](https://github.com/LobellLab/weakly_supervised) -> Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery
@@ -1425,8 +1424,6 @@ Detecting the most noticeable or important object in a scene
 
 - [vessel-detection-viirs](https://github.com/allenai/vessel-detection-viirs) -> Model and service code for streaming vessel detections from VIIRS satellite imagery
 
-- [anomaly-detection-in-SAR-imagery](https://github.com/iamyadavabhishek/anomaly-detection-in-SAR-imagery) -> identify an unknown ship in docks using keras & retinanet
-
 ### Object detection - Cars, vehicles & trains
 
 - [Detection of parkinglots and driveways with retinanet](https://github.com/spiyer99/retinanet)
@@ -2901,6 +2898,16 @@ Autoencoders are a type of neural network that aim to simplify the representatio
 
 - [satclip](https://github.com/microsoft/satclip) -> A Global, General-Purpose Geographic Location Encoder from Microsoft
 
+#
+## Anomaly detection
+Anomaly detection refers to the process of identifying unusual patterns or outliers in satellite or aerial images that do not conform to expected norms. This is crucial in applications such as environmental monitoring, defense surveillance, and urban planning. Machine learning algorithms, particularly unsupervised learning methods, are used to analyze vast amounts of remote sensing data efficiently. These algorithms learn the typical patterns and variations in the data, allowing them to flag anomalies such as unexpected land cover changes, illegal deforestation, or unusual maritime activities. The detection of these anomalies can provide valuable insights for timely decision-making and intervention in various fields.
+
+- [marine-anomaly-detection](https://github.com/lucamarini22/marine-anomaly-detection) -> Semantic segmentation of marine anomalies using semi-supervised learning (FixMatch for semantic segmentation) on Sentinel-2 multispectral images
+
+- [TDD](https://github.com/Jingtao-Li-CVer/TDD) -> One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning
+
+- [anomaly-detection-in-SAR-imagery](https://github.com/iamyadavabhishek/anomaly-detection-in-SAR-imagery) -> identify an unknown ship in docks using keras & retinanet
+
 #
 ## Image retrieval