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MATLAB Toolbox for Remote Sensing Change Detection

This repository includes a MATLAB toolbox for remote sensing change detection (RSCD) wherein several classical methods are implemented. I'm working on building an open-source, end-to-end, and extensible benchmark framework for the convenience of the RSCD research.

Overview

Generally, a typical RSCD process can be divided into four steps, namely image pre-processing, change detection, binarization (thresholding) and accuracy evaluation. Currently, the toolbox is mainly based on the final three steps. For ease of usage and maintenance, the toolbox adopted modular design, which was realized by packages and classes in MATLAB semantics.

• The Datasets package holds several classes of change detection datasets.
• Algorithms collects the change detection methods to yield a difference image.
• ThreAlgs contains 3 common methods for binary segmentation.
• Metrics groups 6 metrics that are frequently used in researches.

Each of these packages contains replaceable modules and the modules from different packages can be freely combined. It is worth mentioning that the modules for pre-processing and post-processing are still under development.

Dependencies and Environment

The toolbox is developed and tested in MATLAB R2017b.

Getting Started

For a quick start, use detectChange.m. Here are some examples.

[CM, DI] = detectChange('CVA', {}, 'KMeans', {}, 'data/2000TM', 'data/2003TM')

[CM, DI, results] = detectChange('MAD', {}, 'OTSU', {}, '2000TM.tif', '2003TM.tif', 'gt.png', {'AUC', 'UA'}, {{}, {}})

See the usage of detectChange.m by

help detectChange

Alternatively, try

cd UI
run guiCD.m

to enable the visual interface.

To try full functionality, find the main script main.m in the root directory of this repo, and follow the three steps below:

Step 1

First, check and set the global configurations. They are in the beginning of the script and most of them are about logging and visualization settings. So feel free to fiddle. See the comment to understand what an option will do.

Step 2

Second, select the modules you would like to use by specifying their names in quotes. The names have to be valid and exact, which means that a name ought to be taken from the Name column of the Available Lists. For the METRICS, put any number of items in a cell array. An example is here:

ALG = 'MAD'
DATASET = 'TaizhouDataset'
THRE_ALG = 'KMeans'
METRICS = {'OA', 'UA', 'Recall', 'FMeasure', 'AUC', 'Kappa'}

Then, type the arguments by assigning cell arrays to CONFIG_*. The assignment statements have to be there even if no argument is expected. Otherwise, it wil raise an error. For the zero-argument case, just leave the cell empty. Note that for CONFIG_DATASET, a path string that directs to the root directory of the dataset is always required. For CONFIG_METRICS, the arguments of each CDMetric module should be specified in separated sub-cells. And the number of the nested cells matches the length of MRTRICS. See this example:

CONFIG_ALG = {1, 2};
CONFIG_DATASET = {'E:\\Mydataset'};
CONFIG_THRE_ALG = {};
% MRTRICS = {'A', 'B', 'C'}
CONFIG_METRICS = {{'a'}, {}, {}};

Step 3

Run the code