CNN-based Salient Object Detection on Hyperspectral Images using Extended Morphology

• Published in: IEEE geoscience and remote sensing letters 2022, Vol.19, p.1-1
• Main Authors: Chhapariya, Koushikey, Buddhiraju, Krishna Mohan, Kumar, Anil
• Format: Article
• Language: English
• Subjects: Artificial neural networks; CNN; Computer vision; Convolutional neural networks; Data mining; Datasets; Detection; EMP; Error analysis; Extended Morphology; Feature extraction; hyperspectral image; Hyperspectral imaging; Image processing; Imagery; Information processing; Methods; Morphology; Neural networks; Object detection; Object recognition; Principal component analysis; Salience; Salient object detection; spectral-spatial classification; Training
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2022.3220601

Salient object detection in hyperspectral images 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 hyperspectral image. This paper proposes a Convolutional Neural Network (CNN) based salient object detection method using hyperspectral imagery to utilise spatial and spectral information simultaneously. The proposed methodology incorporates Extended Morphological Profile (EMP) followed by a CNN to utilise 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 generalisation ability, viz. 1) Hyperspectral Salient Object Detection Dataset (HS-SOD) and 2) Pavia University 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 AUC (Area Under receiver operating characteristic Curve) and F-measure and lower mean absolute error for both datasets.