The AMEE-PPI Method to Extract Typical Outcrop Endmembers from GF-5 Hyperspectral Images

Mixed pixels remain a central obstacle to reliable endmember extraction from hyperspectral imagery. We present AMEE-PPI, a hybrid method that embeds the Pure Pixel Index (PPI) within morphological structuring elements and propagates spectral purity via dilation/erosion, thereby coupling spatial context with spectral cues while avoiding a user-fixed number of projections. On GaoFen-5 (GF-5) AHSI data from a geologically complex outcrop region, we benchmark AMEE-PPI against four widely used algorithms-PPI, OSP, VCA, and AMEE. The pipeline uses HySime for noise estimation and signal-subspace inference to set the endmember count prior to extraction and applies morphological elements spanning 3 × 3 to 15 × 15 to balance spatial support with local heterogeneity. Quantitatively, AMEE-PPI achieves the lowest spectral angle distance (SAD) for all outcrop types-purple-red: 0.135; yellow-brown: 0.316; gray: 0.191-surpassing the competing methods. It also attains the lowest spectral information divergence (SID)-purple-red: 0.028; yellow-brown: 0.184; gray: 0.055-confirming superior similarity to field reference spectra across materials. Visually, AMEE-PPI avoids the vegetation endmember leakage observed with several baselines on purple-red and gray outcrops, yielding cleaner, more representative endmembers. These results indicate that integrating spatial morphology with spectral purity improves robustness to illumination, mixing, and local variability in GF-5 imagery, with direct benefits for downstream unmixing, classification, and geological interpretation.