Scene classification in remote sensing images is one of the urgent tasks that requires an improvement in recognition accuracy due to complex spatial structures and high inter-class similarity. Although feature extraction using convolutional neural networks provides high efficiency, combining deep features obtained from different architectures in a semantically consistent manner remains an important scientific problem. In this study, a DMCCA + SVM model is proposed, in which Discriminative Multiple Canonical Correlation Analysis (DMCCA) is applied to fuse multi-source deep features, and final classification is performed using a Support Vector Machine (SVM). Unlike conventional fusion methods, DMCCA projects heterogeneous features into a unified low-dimensional latent space by maximizing within-class correlation and minimizing between-class correlation, resulting in a more separable and compact feature space. The proposed approach was evaluated on three widely used benchmark datasets—NWPU-RESISC45, AID, and PatternNet—and achieved accuracy scores of 92.75%, 93.92%, and 99.35%, respectively. The results showed that the model outperforms modern individual CNN architectures. Additionally, the model’s stability and generalization capability were confirmed through K-fold cross-validation. Overall, the proposed DMCCA + SVM model was experimentally validated as an effective and reliable solution for high-accuracy classification of remote sensing scenes.

Retinal optical coherence tomography (OCT) classification is often affected by substantial class imbalance and by evaluation bias when images from the same patient appear in both training and test sets. This study proposes MCSP-LightP3R-MoE, a patient-aware framework that combines minority-guided cyclic sequential partition learning with progressive lightweight sparse Mixture-of-Experts routing for four-class retinal OCT classification. Group-disjoint training, validation, and test subsets were constructed from 84,484 OCT images covering CNV, DME, DRUSEN, and NORMAL classes. The model uses an ImageNet-pretrained ResNet18 backbone with three progressive LightMoE modules, heterogeneous lightweight experts, and Top-1 routing, and was evaluated over three independent seeds against conventional CNN, imbalance-handling, and ablation baselines. MCSP-LightP3R-MoE achieved 93.28 ± 0.29% balanced accuracy and 92.06 ± 0.37% macro-F1, while DRUSEN sensitivity reached 89.17 ± 1.92%, an 8.78 percentage-point improvement over ResNet18. Patient-cluster bootstrap analysis supported several gains in balanced accuracy and minority-class sensitivity relative to conventional CNN baselines. The model contains 11.57 million parameters, only 3.47% more than ResNet18. These results indicate that patient-aware sequential exposure and sparse expert routing can improve minority-sensitive OCT classification while retaining a single compact inference model.