An Improved Manifold Learning Algorithm for Dimension Reduction of Fusion Data for Multispectral and Synthetic Aperture Radar Images

In the application of remote sensing data, the data obtained by a single sensor often cannot meet the actual needs, because the information contained in these data is often limited. Data fusion can improve the availability of these data, but it also brings the problem of information redundancy. In this paper, a whole set of multi-source remote sensing image-processing frameworks was constructed first and aimed at the problem of neighborhood selection in an isometric mapping (ISOMAP) algorithm; an improved ISOMAP algorithm based on the L1 norm (a sparse optimization technique that minimizes absolute value sums) was proposed to mine the inherent low-dimensional structure of multi-source remote sensing data and reduce the dimension of the data. In this paper, the traditional manifold learning algorithm is improved by developing a version of ISOMAP based on the L1 norm (L1-ISOMAP) for dimensionality reduction tasks. After dimensionality reduction, the support vector machine (SVM) and random forest (RF) are used to classify the dimensionality reduction data. The experimental results demonstrate that compared with other dimensionality reduction methods such as Pairwise Controlled Manifold Approximation Projection (PaCMAP), Uniform Manifold Approximation and Projection (UMAP), traditional ISOMAP, and other improved ISOMAP variants (achieving 96.90% overall accuracy with SVM and 98.69% with RF, with kappa coefficients of 0.96 and 0.98, respectively), our L1-ISOMAP achieves superior overall classification accuracy and exhibits stronger robustness in processing multi-source remote sensing data.