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

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 Ne...

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Bibliographic Details
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
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Summary: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.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2022.3220601