Spatial-spectral approach with spatially adaptive classification of hyperspectral images is proposed. The rotation-invariant spatial texture information for each object is exploited and incorporated into the classifier by using the modified local Gabor binary pattern to distinguish different types of classes of interest. The proposed method can effectively suppress anisotropic texture in spatially separate classes as well as improve the discrimination among classes. Moreover, it becomes more robust with the within-class variation. Experimental results on the classification of three real hyperspectral remote sensing images demonstrate the effectiveness of the proposed approach.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1364/OL.38.000815 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Harnessing Spectral Libraries From AVIRIS-NG Data for Precise PFT Classification: A Deep Learning Approach.
Plant Cell Environ
January 2025
Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India.
The generation of spectral libraries using hyperspectral data allows for the capture of detailed spectral signatures, uncovering subtle variations in plant physiology, biochemistry, and growth stages, marking a significant advancement over traditional land cover classification methods. These spectral libraries enable improved forest classification accuracy and more precise differentiation of plant species and plant functional types (PFTs), thereby establishing hyperspectral sensing as a critical tool for PFT classification. This study aims to advance the classification and monitoring of PFTs in Shoolpaneshwar wildlife sanctuary, Gujarat, India using Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and machine learning techniques.
View Article and Find Full Text PDFSeed Protein Content Estimation with Bench-Top Hyperspectral Imaging and Attentive Convolutional Neural Network Models.
Sensors (Basel)
January 2025
Department of Earth, Environment and Geospatial Sciences, Saint Louis University, Saint Louis, MO 63108, USA.
Wheat is a globally cultivated cereal crop with substantial protein content present in its seeds. This research aimed to develop robust methods for predicting seed protein concentration in wheat seeds using bench-top hyperspectral imaging in the visible, near-infrared (VNIR), and shortwave infrared (SWIR) regions. To fully utilize the spectral and texture features of the full VNIR and SWIR spectral domains, a computer-vision-aided image co-registration methodology was implemented to seamlessly align the VNIR and SWIR bands.
View Article and Find Full Text PDFNon-destructive prediction and pixel-level visualization of polysaccharide-based properties in ancient paper using SWNIR hyperspectral imaging and machine learning.
Carbohydr Polym
March 2025
Plant Fiber Material Science Research Center, State Key Laboratory of Pulp and Paper Engineering, School of Light Industry and Engineering, South China University of Technology, Guangzhou 510640, China; Guangdong Provincial Key Laboratory of Plant Resources Biorefinery, No. 100, West Outer Ring Road, Guangzhou University Town, Panyu District, Guangzhou 510006, China.
Ancient documents and artworks are invaluable cultural heritage artworks that require careful preservation. Traditional methods for assessing their physical and chemical properties-such as tearing index, tensile index, water absorption, and pH-are often destructive, risking irreversible damage. This study introduces a novel, non-destructive approach using Short-Wave Near-Infrared (SWNIR) hyperspectral imaging (HSI) combined with advanced machine learning models.
View Article and Find Full Text PDFUsing near-infrared hyperspectral imaging combined with machine learning to predict the components and the origin of Radix Paeoniae Rubra.
Anal Methods
January 2025
College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
The efficacy and safety of drugs are closely related to the geographical origin and quality of the raw materials. This study focuses on using near-infrared hyperspectral imaging (NIR-HSI) combined with machine learning algorithms to construct content prediction models and origin identification models to predict the components and origin of Radix Paeoniae Rubra (RPR). These models are quick, non-destructive, and accurate for assessing both component content and origin.
View Article and Find Full Text PDFBroadband CARS Hyperspectral Classification of Single Immune Cells.
J Biophotonics
January 2025
Department of Electronic Engineering, Maynooth University, Kildare, Ireland.
Broadband CARS is a coherent Raman scattering technique that provides access to the full biological vibrational spectrum within milliseconds, facilitating the recording of widefield hyperspectral Raman images. In this work, BCARS hyperspectral images of unstained cells from two different cell lines of immune lineage (T cell [Jurkat] and pDCs [CAL-1]) were recorded and analyzed using multivariate statistical algorithms in order to determine the spectral differences between the cells. A classifier was trained which could distinguish the known cells with a 97% out-of-bag accuracy.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!