Unsupervised identification of the key endmembers in multi- and hyperspectral remote sensing images for image segmentation and analysis

A computer-implemented system comprising:

• memory storing application programming interface (API) information;
• and
• a server performing operations comprising:
• receiving a multispectral image corresponding to a region of interest, the multispectral image comprising aerial images of the region of interest captured by a multispectral sensor, the multispectral image comprises a plurality of pixels;
• processing the multispectral image, by applying a multidimensional edge detection filter to remove a portion of the plurality of pixels identified as impure pixels, a remaining portion of plurality of pixels comprising unmasked samples;
• determine a Euclidian minimum spanning tree of unmasked samples, wherein the unmasked samples are rotated to minimize a dimension of the unmasked samples;
• applying a single-link clustering to generate a dendrogram of pure materials;
• determining a cluster quality index for each cluster to sort the pure materials in order of significance and to select a portion of the pure materials as key materials based on the order of significance;
• applying a spectral unmixing procedure to generate a map of the key materials in the multispectral image;
• and
• identifying an action plan to remedy a risk associated with the map of key materials.

A non-transitory computer-readable media encoded with a computer program, the computer program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:

• receiving a multispectral image corresponding to a region of interest, the multispectral image comprising aerial images of the region of interest captured by a multispectral sensor, the multispectral image comprises a plurality of pixels;
• processing the multispectral image, by applying a multidimensional edge detection filter to remove a portion of the plurality of pixels identified as impure pixels, a remaining portion of plurality of pixels comprising unmasked samples;
• determine a Euclidian minimum spanning tree of unmasked samples, wherein the unmasked samples are rotated to minimize a dimension of the unmasked samples;
• applying a single-link clustering to generate a dendrogram of pure materials;
• determining a cluster quality index for each cluster to sort the pure materials in order of significance and to select a portion of the pure materials as key materials based on the order of significance;
• applying a spectral unmixing procedure to generate a map of the key materials in the multispectral image;
• and
• identifying an action plan to remedy a risk associated with the map of key materials.