Hyperspectral Image Classification Based on Active Learning and Spectral-Spatial Feature Fusion Using SpatialCoordinates
Objective
To enhance hyperspectral image classification accuracy and efficiency by integrating active learning, spectral-spatial feature fusion with spatial coordinates, adaptive feature extraction techniques, and MRF-based spatial smoothing.
Abstract
Abstract:
This paper presents SSFFSC-AL+, an improved extension of the Spectral-Spatial Feature Fusion with Spatial Coordinates and Active Learning (SSFFSC-AL) method for hyperspectral image classification. The base method combines dual SVM classifiers for spectral and spatial features with uncertainty-based active learning. Our extension introduces adaptive configuration profiles, enhanced feature extraction through Extended Morphological Attribute Profiles (EMAP), Gabor filters, and Local Binary Patterns (LBP), along with Markov Random Field (MRF) post-processing for spatial smoothing. Experimental results on Indian Pines and Pavia University datasets demonstrate significant improvements in both Overall Accuracy and Average Accuracy metrics. The extension achieves these improvements while maintaining computational efficiency through adaptive parameter selection based on dataset characteristics. Training time is reduced on certain datasets while simultaneously improving accuracy, demonstrating both performance and efficiency gains. These results confirm SSFFSC-AL+ as a robust and efficient solution for hyperspectral image classification tasks across diverse datasets and application scenarios.
Keywords: Hyperspectral Image Classification, Active Learning Spectral-Spatial Feature, Fusion Markov Random Field Support Vector MachineNOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software: Matlab 2022b or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is an Image/pixel?
· About image formats
· Introduction to Image Processing
· How digital image is formed
· Importing the image via image acquisition tools
· Analyzing and manipulation of image.
· Phases of image processing:
o Acquisition
o Image enhancement
o Image restoration
o Color image processing
o Image compression
o Morphological processing
o Segmentation etc.,
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
