We propose an unsupervised algorithm that utilizes information derived from spectral, gradient, and textural attributes for spatially segmenting multi/hyperspectral remotely sensed imagery. Our methodology commences by determining the magnitude of spectral intensity variations across the input scene, using a multiband gradient detection scheme optimized for handling remotely sensed image data. The resultant gradient map is employed in a dynamic region growth process that is initiated in pixel locations with small gradient magnitudes and is concluded at sites with large gradient magnitudes, yielding a map comprised of an initial set of regions. This region map is combined with several co-occurrence matrix-derived textural descriptors along with intensity and gradient features in a multivariate analysis-based region merging procedure that fuses the regions with similar characteristics to yield the final segmentation output. Our approach was tested on several multi/hyperspectral datasets, and the results show a favorable performance in comparison with state-of-the-art techniques.