Deep learning techniques have become increasingly popular for classifying large-scale image and video data. Remote sensing applications require robust search engines to retrieve similar information dependent on an example-based query instead of a tag-based query. Deep features can be extracted automatically by training raw data without having any domain-specific knowledge. However, the training time for a massive amount of multimedia datasets is high. Training complexity is reduced using pre-trained GoogleNet weights for initial feature extraction. To fine-tune the feature vector and reduce the dimensionality, a one dimension convolutional neural network (1D-CNN) is applied. There is a loss of information while resizing the input image to a pre-trained network with an acceptable input size. We proposed a new feature set by integrating handcrafted features at detailed scales and deep features to improve the system’s efficiency. The curvelet transform was used to decompose the image into coarse and detailed scales. Haralick texture features were extracted from the detail coefficients in four directions and fused with fine-tuned deep features. The proposed feature set was assessed using standard performance metrics from the literature. The proposed technique achieved improved performance with 89% accuracy for retrieval of the top 50 relevant results.

中文翻译：

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通过结合使用Curvelet变换的自动深度特征提取和手工特征来进行遥感图像检索

深度学习技术已变得越来越流行，用于对大型图像和视频数据进行分类。遥感应用程序需要强大的搜索引擎来检索相似的信息，这些信息依赖于基于示例的查询而不是基于标签的查询。通过训练原始数据可以自动提取深层功能，而无需任何特定领域的知识。但是，大量多媒体数据集的训练时间很长。使用预先训练的GoogleNet权重进行初始特征提取，可以降低训练的复杂性。为了微调特征向量并降低维数，应用了一维卷积神经网络（1D-CNN）。在将输入图像调整为具有可接受的输入大小的预训练网络时，会丢失信息。我们提出了一个新功能集，该功能集通过将详细比例和深层功能的手工功能集成在一起来提高系统效率。Curvelet变换用于将图像分解为粗略和详细比例。从四个方向的细节系数中提取Haralick纹理特征，并与微调的深层特征融合。使用来自文献的标准性能指标评估了建议的功能集。所提出的技术以89％的精度检索了前50个相关结果，从而提高了性能。从四个方向的细节系数中提取Haralick纹理特征，并与微调的深层特征融合。使用来自文献的标准性能指标评估了建议的功能集。所提出的技术以89％的精度检索了前50个相关结果，从而提高了性能。从四个方向的细节系数中提取Haralick纹理特征，并与微调的深层特征融合。使用来自文献的标准性能指标评估了建议的功能集。所提出的技术以89％的精度检索了前50个相关结果，从而提高了性能。