A Novel Fuzzy Inference System-Based Endmember Extraction in Hyperspectral Images

Spectral unmixing helps to identify different components present in the spectral mixtures which occur in the uppermost layer of the area owing to the low spatial resolution of hyperspectral images. Most spectral unmixing methods are globally based and do not consider the spectral variability among its endmembers that occur due to illumination, atmospheric, and environmental conditions. Here, endmember bundle extraction plays a major role in overcoming the above-mentioned limitations leading to more accurate abundance fractions. Accordingly, a two-stage approach is proposed to extract endmembers through endmember bundles in hyperspectral images. The divide and conquer method is applied as the first step in subset images with only the non-redundant bands to extract endmembers using the Vertex Component Analysis (VCA) and N-FINDR algorithms. A fuzzy rule-based inference system utilizing spectral matching parameters is proposed in the second step to categorize endmembers. The endmember with the minimum error is chosen as the final endmember in each specific category. The proposed method is simple and automatically considers endmember variability in hyperspectral images. The efficiency of the proposed method is evaluated using two real hyperspectral datasets. The average spectral angle and abundance angle are used to analyze the performance measures.