Morphological segmentation of hyperspectral images
This work addresses segmentation challenges in hyperspectral imaging, which is important for applications in remote sensing and environmental monitoring, but it appears incremental as it adapts existing techniques like watershed and gradients to this domain.
The paper tackles the problem of segmenting hyperspectral images with many channels by developing a watershed-based methodology that combines spectral classification for markers and vectorial gradients for spatial information, achieving relevant segmentation results across different spaces like factor and parameter spaces.
The present paper develops a general methodology for the morphological segmentation of hyperspectral images, i.e., with an important number of channels. This approach, based on watershed, is composed of a spectral classification to obtain the markers and a vectorial gradient which gives the spatial information. Several alternative gradients are adapted to the different hyperspectral functions. Data reduction is performed either by Factor Analysis or by model fitting. Image segmentation is done on different spaces: factor space, parameters space, etc. On all these spaces the spatial/spectral segmentation approach is applied, leading to relevant results on the image.