Alegavi Sujata, Sedamkar Raghvendra
Internet of Things Department, Thakur College of Engineering and Technology, Mumbai 400101, Maharashtra, India.
Computer Engineering Department, Thakur College of Engineering and Technology, Mumbai 400101, Maharashtra, India.
J Imaging. 2025 May 28;11(6):179. doi: 10.3390/jimaging11060179.
The contemporary challenge in remote sensing lies in the precise retrieval of increasingly abundant and high-resolution remotely sensed images (RS image) stored in expansive data warehouses. The heightened spatial and spectral resolutions, coupled with accelerated image acquisition rates, necessitate advanced tools for effective data management, retrieval, and exploitation. The classification of large-sized images at the pixel level generates substantial data, escalating the workload and search space for similarity measurement. Semantic-based image retrieval remains an open problem due to limitations in current artificial intelligence techniques. Furthermore, on-board storage constraints compel the application of numerous compression algorithms to reduce storage space, intensifying the difficulty of retrieving substantial, sensitive, and target-specific data. This research proposes an innovative hybrid approach to enhance the retrieval of remotely sensed images. The approach leverages multilevel classification and multiscale feature extraction strategies to enhance performance. The retrieval system comprises two primary phases: database building and retrieval. Initially, the proposed Multiscale Multiangle Mean-shift with Breaking Ties (MSMA-MSBT) algorithm selects informative unlabeled samples for hyperspectral and synthetic aperture radar images through an active learning strategy. Addressing the scaling and rotation variations in image capture, a flexible and dynamic algorithm, modified Deep Image Registration using Dynamic Inlier (IRDI), is introduced for image registration. Given the complexity of remote sensing images, feature extraction occurs at two levels. Low-level features are extracted using the modified Multiscale Multiangle Completed Local Binary Pattern (MSMA-CLBP) algorithm to capture local contexture features, while high-level features are obtained through a hybrid CNN structure combining pretrained networks (Alexnet, Caffenet, VGG-S, VGG-M, VGG-F, VGG-VDD-16, VGG-VDD-19) and a fully connected dense network. Fusion of low- and high-level features facilitates final class distinction, with soft thresholding mitigating misclassification issues. A region-based similarity measurement enhances matching percentages. Results, evaluated on high-resolution remote sensing datasets, demonstrate the effectiveness of the proposed method, outperforming traditional algorithms with an average accuracy of 86.66%. The hybrid retrieval system exhibits substantial improvements in classification accuracy, similarity measurement, and computational efficiency compared to state-of-the-art scene classification and retrieval methods.
遥感领域当前面临的挑战在于如何从存储在大型数据仓库中的日益丰富且高分辨率的遥感影像（RS影像）中精确检索信息。空间和光谱分辨率的提高，以及图像采集速率的加快，需要先进的工具来进行有效的数据管理、检索和利用。在像素级别对大尺寸图像进行分类会产生大量数据，这增加了相似性度量的工作量和搜索空间。由于当前人工智能技术的局限性，基于语义的图像检索仍然是一个未解决的问题。此外，机载存储限制迫使应用多种压缩算法来减少存储空间，这加剧了检索大量、敏感和特定目标数据的难度。本研究提出了一种创新的混合方法来提高遥感影像的检索效果。该方法利用多级分类和多尺度特征提取策略来提升性能。检索系统包括两个主要阶段：数据库构建和检索。首先，所提出的带打破平局的多尺度多角度均值漂移算法（MSMA-MSBT）通过主动学习策略为高光谱和合成孔径雷达图像选择信息丰富的未标记样本。针对图像采集过程中的缩放和旋转变化，引入了一种灵活的动态算法——使用动态内点的改进深度图像配准算法（IRDI）进行图像配准。鉴于遥感影像的复杂性，特征提取分两个级别进行。使用改进的多尺度多角度完整局部二值模式算法（MSMA-CLBP）提取低级特征以捕获局部纹理特征，而高级特征则通过结合预训练网络（Alexnet、Caffenet、VGG-S、VGG-M、VGG-F、VGG-VDD-16、VGG-VDD-19）和全连接密集网络的混合卷积神经网络结构获得。低级和高级特征的融合有助于最终的类别区分，软阈值处理可减轻误分类问题。基于区域的相似性度量提高了匹配百分比。在高分辨率遥感数据集上进行评估的结果表明了所提方法的有效性，其平均准确率为86.66%，优于传统算法。与现有最先进的场景分类和检索方法相比，该混合检索系统在分类准确率、相似性度量和计算效率方面有显著提升。
