The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLVIII-4/W1-2022
06 Aug 2022
 | 06 Aug 2022


P. Soille and P. Vogt

Keywords: spatial pattern analysis, connectivity, mathematical morphology, morphological image analysis, landscape analysis, MSPA, open source, open science

Abstract. The morphological segmentation of binary patterns provides an effective method for characterising spatial patterns with emphasis on connections between their parts as measured at varying analysis scales. The method is widely used for the analysis of landscape patterns such as those related to the fragmentation of forests or other natural land cover classes. This can be explained by its effectiveness at capturing the complexity of binary patterns and their connections by partitioning the foreground pixels of the corresponding binary images into mutually exclusive classes. While the principles of the method are conceptually simple, the definition of the classes relies on a series of advanced mathematical morphology operations whose actual implementation is not straightforward. In this paper, we propose an open source code for MSPA and detail its main components in the form of pseudo-code. We demonstrate its effectiveness for asynchronous processing of tera-pixel images and the synchronous exploratory analysis and rendering with Jupyter notebooks.