Welcome to FanInSAR’s documentation!

Introduction

FanInSAR is a fancy InSAR post-processing library written in Python. It is specifically designed to assist in the efficient processing of InSAR data, offering a Pythonic, fast, and flexible approach. If you are looking to implement your own InSAR algorithm, FanInSAR is highly recommended.

Highlight Features

• Fast: The core computation in FanInSAR is implemented using PyTorch, a high-performance deep learning library. This allows for efficient processing on both CPU and GPU, enabling faster execution.

• Pythonic: FanInSAR is written in Python and provides a user-friendly API. The API is designed to be simple and intuitive, making it easy for InSAR users to work with. For example, loading data from HyP3 or LiCSAR products is as simple as providing the corresponding home directory. Filtering interferometric pairs can be performed by a time slice, similar to the pandas package. The complex processing pipeline is abstracted away, allowing users to focus on algorithm development.

• Flexible: FanInSAR is designed to be flexible, allowing for customization and extension. Users can easily inherit classes or customize the processing pipeline to suit their specific needs.

Contents:

• Installation
• User Guide
  • Quick Start
    • Load InSAR data
  • Colormap reference
    • Usage
    • Colormap list
  • Data Structures
  • Indexing and selecting data
• API Reference
  • geospatial Datasets
    • GeoDataset
    • RasterDataset
    • PairDataset
    • InterferogramDataset
    • HyP3S1
    • HyP3S1Burst
    • LiCSAR
    • ApsDataset
    • GACOS
    • GACOSPairs
  • geospatial Queries
    • Points query
    • Bounding Box query
    • Polygons query
    • GeoQuery
  • Dataset Samplers
    • RowSampler
  • faninsar.NSBAS
    • Inversion
    • Time-Series Models
  • Pairs and Loops
    • Pair
    • Pairs
    • Loop
    • Loops
    • TripletLoops
    • PairsFactory
• Terminology