Introduction

A PYthon library for basic and eXtended COntinuous Change Detection algorithms

The Continuous Change Detection and Claasification (CCDC) algorithm has been popular for processing satellite-based time series datasets, particularly for Landsat-based datasets. As a CCDC user, you may already be familiar with the existing CCDC tools such as pyccd and gee ccdc.

Wait.. so why does the pyxccd package still exist?

We developed pyxccd mainly for the below purposes:

1. Near real-time monitoring: Implements the unique S-CCD algorithm, which recursively updates model coefficients and enables timely change detection.

2. Latest CCDC (COLD): Integrates the advanced COLD algorithm, offering the highest retrospective breakpoint detection accuracy to date, validated against Zhe’s MATLAB version.

3. Efficient Large-scale time-series processing: The core of pyxccd is written in C language, ensuring high computational efficiency and low memory usage in the desktop as well as HPC environments.

4. Flexible multi-sensor support: Supports arbitrary band combinations from diverse sensors (e.g., Sentinel-2, MODIS, GOSIF, and SMAP) in addition to Landsat.

5. State-space model incoporation: S-CCD allows modeling trend and seasonal signals as time-varying variables (namely “states”) guided by break detection, enabling (a) characterization of subtle inter-segment variations (e.g., phenological shifts) and (b) gap filling that accounts for land cover conversions (temporal breaks).