Explore MATLAB source code curated for "遥感图像" with clean implementations, documentation, and examples.
PCA-based remote sensing image fusion with excellent results, suitable as introductory material for learning remote sensing image fusion techniques, featuring implementation insights about principal component analysis and image processing workflows.
This MATLAB-based program is designed for reading remote sensing images, specifically ENVI file formats, with enhanced image processing capabilities.
Image segmentation using texture metrics, particularly suitable for remote sensing imagery, primarily implemented through the K-means clustering algorithm with feature extraction and preprocessing techniques.
Fully Constrained Least Squares method for mixed pixel decomposition in remote sensing imagery, with implementation insights on constraint handling and optimization algorithms
A MATLAB-based GUI application utilizing class average clustering for identifying diverse features in various images (primarily remote sensing). The program offers three selectable distance functions, including main program, FIG interface file, and distance calculation modules for flexible image analysis.
Hyperspectral image interpretation serves as a vital component in remote sensing applications. Spectral interpretation techniques based on the Linear Spectral Mixture Model (LSMM) are gaining widespread adoption. Simulation results demonstrate that incorporating partial endmembers and implementing region-based interpretation significantly enhances hyperspectral image analysis outcomes. This approach involves implementing endmember extraction algorithms (e.g., N-FINDR) and region segmentation methods to optimize spectral unmixing performance.
Classification of remote sensing imagery using similarity-based methods for multispectral target recognition, with implementation details on feature extraction and comparison algorithms
Applying homomorphic filtering to images followed by local histogram equalization can achieve blur enhancement effects, suitable for applications such as thin cloud removal in remote sensing images. However, this approach requires further refinement and research for optimal results.
This research investigates a hybrid approach combining Contourlet transform and fractal compression techniques for remote sensing image processing, demonstrating practical applicability with experimental images included in the software package. Implementation involves multi-directional decomposition and iterative affine transformations for efficient image coding.
Implementation of object recognition in remote sensing images through fuzzy neural networks with code integration and algorithmic enhancements