Abstract
The mission of classifying remote sensing pictures based on their contents has a range of applications in a variety of areas. In recent years, a lot of interest has been generated in researching remote sensing image scene classification. Remote sensing image scene retrieval, and scene-driven remote sensing image object identification are included in the Remote sensing image scene understanding (RSISU) research. In the last several years, the number of deep learning (DL) methods that have emerged has caused the creation of new approaches to remote sensing image classification to gain major breakthroughs, providing new research and development possibilities for RS image classification. A new network called Pass Over (POEP) is proposed that utilizes both feature learning and end-to-end learning to solve the problem of picture scene comprehension using remote sensing imagery (RSISU). This article presents a method that combines feature fusion and extraction methods with classification algorithms for remote sensing for scene categorization. The benefits (POEP) include two advantages. The multi-resolution feature mapping is done first, using the POEP connections, and combines the several resolution-specific feature maps generated by the CNN, resulting in critical advantages for addressing the variation in RSISU data sets. Secondly, we are able to use Enhanced pooling to make the most use of the multi-resolution fea-ture maps that include second-order information. This enables CNNs to better cope with (RSISU) issues by providing more representative feature learning. The data for this paper is stored in a UCI dataset with 21 types of pictures. In the beginning, the picture was pre-processed, then the features were retrieved using RESNET-50, Alexnet, and VGG-16 integration of architectures. After characteristics have been amalgamated and sent to the attention layer, after this characteristic has been fused, the process of classifying the data will take place. We utilize an ensemble classifier in our classification algorithm that utilizes the architecture of a Decision Tree and a Random Forest. Once the optimum findings have been found via performance analysis and comparison analysis.