Abstract:
Salient object detection in hyperspectral images (hsis) is of interest in various image processing and computer vision applications. Many studies considering spectral information have been reported, extracting only low-level features from a hsi. This letter proposes a convolutional neural network (cnn) based salient object detection method using hyperspectral imagery to utilize spatial and spectral information simultaneously. The proposed methodology incorporates an extended morphological profile (emp) followed by a cnn to utilize the information from nearby pixels and high-level features simultaneously. We have evaluated the performance of the proposed approach on two independent datasets to verify the generalization ability, viz.: 1) hyperspectral salient object detection dataset (hs-sod) and 2) pavia university (pu) dataset. An extensive quantitative analysis of the results revealed that the proposed method significantly outperforms other state-of-the-art methods by approximately ≥2% of the area under receiver operating characteristic (roc) curve (auc) and f-measure and lower mean absolute error for both datasets. © 2004-2012 ieee.