Abstract
Image scene recognition is a core technology for many aerial remote sensing applications. Different landforms are inputted as different scenes in aerial imaging, and all landform information is regarded as valuable for aerial image scene recognition. However, the conventional features of the Bag-of-Words model are designed using local points or other related information and thus are unable to fully describe landform areas. This limitation cannot be ignored when the aim is to ensure accurate aerial scene recognition. A novel superpixel-based feature is proposed in this study to characterize aerial image scenes. Then, based on the proposed feature, a scene recognition method of the Bag-of-Words model for aerial imaging is designed. The proposed superpixel-based feature that utilizes landform information establishes top-task superpixel extraction of landforms to bottom-task expression of feature vectors. This characterization technique comprises the following steps: simple linear iterative clustering based superpixel segmentation, adaptive filter bank construction, Lie group-based feature quantification, and visual saliency model-based feature weighting. Experiments of image scene recognition are carried out using real image data captured by an unmanned aerial vehicle (UAV). The recognition accuracy of the proposed superpixel-based feature is 95.1%, which is higher than those of scene recognition algorithms based on other local features.
Highlights
Recent developments in unmanned aerial vehicles (UAVs), platforms, positional and attitudinal measurement sensors, imaging sensors, and processing approaches have opened up considerable opportunities for applying remote sensing in national environmental protection [1], land use survey [2], marine environmental monitoring [3], water resource development, crop growth monitoring and assessment [4], wildlife multispecies remote sensing [5], forest protection and monitoring [6], natural disaster monitoring and evaluation [7], target surveillance [8], and Digital Earth
This study evaluates and compares the expression capability of the proposed superpixel-based features with the six commonly used features of Bag-of-Words modeling
The superpixel-based feature description method proposed in this study can be applied to image scene recognition for aerial remote sensing applications
Summary
Recent developments in unmanned aerial vehicles (UAVs), platforms, positional and attitudinal measurement sensors, imaging sensors, and processing approaches have opened up considerable opportunities for applying remote sensing in national environmental protection [1], land use survey [2], marine environmental monitoring [3], water resource development, crop growth monitoring and assessment [4], wildlife multispecies remote sensing [5], forest protection and monitoring [6], natural disaster monitoring and evaluation [7], target surveillance [8], and Digital Earth. The most common task of aerial remote sensing applications, is the process of marking images according to semantic categories, such as seashore, forest, field, mountain, and city scenes
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