Remote sensing plays a pivotal role in environmental monitoring, disaster relief, and urban planning, where accurate scene classification of aerial images is essential. However, conventional convolutional neural networks (CNNs) struggle with long-range dependencies and preserving high-resolution features, limiting their effectiveness in complex aerial image analysis. To address these challenges, we propose a Hybrid HRNet-Swin Transformer model that synergizes the strengths of HRNet-W48 for high-resolution segmentation and the Swin Transformer for global feature extraction. This hybrid architecture ensures robust multi-scale feature fusion, capturing fine-grained details and broader contextual relationships in aerial imagery. Our methodology begins with preprocessing steps, including normalization, histogram equalization, and noise reduction, to enhance input data quality. The HRNet-W48 backbone maintains high-resolution feature maps throughout the network, enabling precise segmentation, while the Swin Transformer leverages hierarchical self-attention to model long-range dependencies efficiently. By integrating these components, our model achieves superior performance in segmentation and classification tasks compared to traditional CNNs and standalone transformer models. We evaluate our approach on two benchmark datasets: UC Merced and WHU-RS19. Experimental results demonstrate that the proposed hybrid model outperforms existing methods, achieving state-of-the-art accuracy while maintaining computational efficiency. Specifically, it excels in preserving fine spatial details and contextual understanding, critical for applications like land-use classification and disaster assessment.

Advances in machine vision systems have revolutionized applications such as autonomous driving, robotic navigation, and augmented reality. Despite substantial progress, challenges persist, including dynamic backgrounds, occlusion, and limited labeled data. To address these challenges, we introduce a comprehensive methodology to enhance image classification and object detection accuracy. The proposed approach involves the integration of multiple methods in a complementary way. The process commences with the application of Gaussian filters to mitigate the impact of noise interference. These images are then processed for segmentation using Fuzzy C-Means segmentation in parallel with saliency mapping techniques to find the most prominent regions. The Binary Robust Independent Elementary Features (BRIEF) characteristics are then extracted from data derived from saliency maps and segmented images. For precise object separation, Oriented FAST and Rotated BRIEF (ORB) algorithms are employed. Genetic Algorithms (GAs) are used to optimize Random Forest classifier parameters which lead to improved performance. Our method stands out due to its comprehensive approach, adeptly addressing challenges such as changing backdrops, occlusion, and limited labeled data concurrently. A significant enhancement has been achieved by integrating Genetic Algorithms (GAs) to precisely optimize parameters. This minor adjustment not only boosts the uniqueness of our system but also amplifies its overall efficacy. The proposed methodology has demonstrated notable classification accuracies of 90.9% and 89.0% on the challenging Corel-1k and MSRC datasets, respectively. Furthermore, detection accuracies of 87.2% and 86.6% have been attained. Although our method performed well in both datasets it may face difficulties in real-world data especially where datasets have highly complex backgrounds. Despite these limitations, GA integration for parameter optimization shows a notable strength in enhancing the overall adaptability and performance of our system.

In the last decade, there has been remarkable progress in the areas of object detection and recognition due to high-quality color images along with their depth maps provided by RGB-D cameras. They enable artificially intelligent machines to easily detect and recognize objects and make real-time decisions according to the given scenarios. Depth cues can improve the quality of object detection and recognition. The main purpose of this research study to find an optimized way of object detection and identification we propose techniques of object detection using two RGB-D datasets. The proposed methodology extracts image normally from depth maps and then performs clustering using the Modified Watson Mixture Model (mWMM). mWMM is challenging to handle when the quality of the image is not good. Hence, the proposed RGB-D-based system uses depth cues for segmentation with the help of mWMM. Then it extracts multiple features from the segmented images. The selected features are fed to the Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) for detecting objects. We achieved 92.13% of mean accuracy over NYUv1 dataset and 90.00% of mean accuracy for the Redweb_v1 dataset. Finally, their results are compared and the proposed model with CNN outperforms other state-of-the-art methods. The proposed architecture can be used in autonomous cars, traffic monitoring, and sports scenes.