Sensing Libraries

12 Components & Libraries

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Sensing Libraries

Python module for hyperspectral image processing

Python scripts for the textbook "Image Analysis, Classification and Change Detection in Remote Sensing, Fourth Revised Edition"

Source files for the Docker image mort/crc4docker Python scripts for the textbook "Image Analysis, Classification and Change Detection in Remote Sensing, Fourth Revised Edition" On Ubuntu, for exampl…

Deep learning with satellite & aerial imagery

Extracting roads is challenging due to the occlusions caused by other objects and the complex traffic environment In instance segmentation, each individual 'instance' of a segmented area is given a u…

Algorithms for computing global land surface temperature and emissivity from NASA's Landsat satellite images with Python.

Additionally, it also provides multiple methods for computing land surface emissivity. It is targeted towards supporting research and science workflows in many fields including climate science, earth…

A Google Earth Engine based algorithm that extracts river centerlines and widths from satellite images

The core algorithms responsible for calculating river centerlines and widths are identical in the JavaScript and the Python version. However, there is minor differences in how users might call these …

CARS is a dedicated and open source 3D tool to produce Digital Surface Models from satellite imaging by photogrammetry.

Be aware that the project is new and is evolving to maturity with CNES usage roadmaps and projects such as: CARS is available on Docker Hub and can be downloaded by: You only need to launch one comma…

A review of change detection methods, including codes and open data sets for deep learning. From paper: change detection based on artificial intelligence: state-of-the-art and challenges.

Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring,…

Modular and scalable computational imaging in Python with GPU/out-of-core computing.

In the realm of computer vision 📷, digital image restoration and enhancement techniques have established themselves as indispensable pillars. These techniques, aiming to restore and elevate the qual…

[TGRS 2020] Official Tensorflow implementation for Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network

With the rapid development of Earth observation technology, very-high-resolution (VHR) images from various satellite sensors are more available, which greatly enrich the data source of change detecti…

[IEEE TCYB 2021] Official Python implementation for Unsupervised Change Detection in Multitemporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network

With the development of Earth observation technology, very-high-resolution (VHR) image has become an important data source of change detection. Nowadays, deep learning methods have achieved conspicuo…

An introduction to script-based satellite image processing using Python

Satellite imagery has become a primary data source in the natural sciences, economics, archaeology, sustainability, national security & defence, and many other domains which utilize geospatial in…

A geospatial raster processing library for machine learning

When geospatial raster data is concerned in a machine learning pipeline, it is often required to extract meaningful features, such as vegetation indices (e.g., NDVI, EVI, NDRE, etc.) or textures. Thi…

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