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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Efficient Saliency-Based Object Detection in Remote Sensing Images Using Deep Belief Networks
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Efficient Saliency-Based Object Detection in Remote Sensing Images Using Deep Belief Networks

机译：使用深信度网络在遥感图像中基于显着性的有效对象检测

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摘要

Object detection has been one of the hottest issues in the field of remote sensing image analysis. In this letter, an efficient object detection framework is proposed, which combines the strength of the unsupervised feature learning of deep belief networks (DBNs) and visual saliency. In particular, we propose an efficient coarse object locating method based on a saliency mechanism. The method could avoid an exhaustive search across the image and generate a small number of bounding boxes, which can locate the object quickly and precisely. After that, the trained DBN is used for feature extraction and classification on subimages. The feature learning of the DBN is operated by pretraining each layer of restricted Boltzmann machines (RBMs) using the general layerwise training algorithm. An unsupervised blockwise pretraining strategy is introduced to train the first layer of RBMs, which combines the raw pixels with a saliency map as inputs. This makes an RBM generate local and edge filters. The precise edge position information and pixel value information are more efficient to build a good model of images. Comparative experiments are conducted on the data set acquired by QuickBird with a 60-cm resolution. The results demonstrate the accuracy and efficiency of our method.
机译：目标检测一直是遥感图像分析领域中最热门的问题之一。在这封信中，提出了一种有效的对象检测框架，该框架结合了深度信念网络（DBN）的无监督特征学习和视觉显着性的优势。特别地，我们提出了一种基于显着性机制的有效的粗目标定位方法。该方法可以避免在图像上进行详尽的搜索，并生成少量的边界框，从而可以快速，准确地定位对象。之后，将训练有素的DBN用于子图像的特征提取和分类。 DBN的特征学习是通过使用常规的分层训练算法对受限制的Boltzmann机器（RBM）的每一层进行预训练来进行的。引入了无监督的块状预训练策略来训练RBM的第一层，该层将原始像素与显着性图相结合作为输入。这使得RBM生成局部和边缘滤波器。精确的边缘位置信息和像素值信息更有效地建立了良好的图像模型。对QuickBird以60厘米分辨率获取的数据集进行了对比实验。结果证明了我们方法的准确性和效率。

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