An Efficient and Robust Integrated Geospatial Object Detection Framework for High Spatial Resolution Remote Sensing Imagery

Abstract

Geospatial object detection from high spatial resolution (HSR) remote sensing imagery is a significant and challenging problem when further analyzing object-related information for civil and engineering applications. However, the computational efficiency and the separate region generation and localization steps are two big obstacles for the performance improvement of the traditional convolutional neural network (CNN)-based object detection methods. Although recent object detection methods based on CNN can extract features automatically, these methods still separate the feature extraction and detection stages, resulting in high time consumption and low efficiency. As a significant influencing factor, the acquisition of a large quantity of manually annotated samples for HSR remote sensing imagery objects requires expert experience, which is expensive and unreliable. Despite the progress made in natural image object detection fields, the complex object distribution makes it difficult to directly deal with the HSR remote sensing imagery object detection task. To solve the above problems, a highly efficient and robust integrated geospatial object detection framework based on faster region-based convolutional neural network (Faster R-CNN) is proposed in this paper. The proposed method realizes the integrated procedure by sharing features between the region proposal generation stage and the object detection stage. In addition, a pre-training mechanism is utilized to improve the efficiency of the multi-class geospatial object detection by transfer learning from the natural imagery domain to the HSR remote sensing imagery domain. Extensive experiments and comprehensive evaluations on a publicly available 10-class object detection dataset were conducted to evaluate the proposed method.

Deformable ConvNet with Aspect Ratio Constrained NMS for Object Detection in Remote Sensing Imagery

“…Accuracy is meaningless as an indicator. Moreover, the accuracy in Han et al [16] does not match the equation when TN is zero. Therefore, accuracy was not applied in our work.…”

“…Figure 7 displays the recall values of the proposed deformable R-FCN, obtaining an overall recall rate of 87.96%. Compared to R-P-Faster R-CNN R-CNN [16] and RICNN [13], the average recall rate is lower than R-P-Faster R-CNN with the VGG model and RICNN, but higher than R-P-Faster R-CNN with the ZF model. Overall, deformable R-FCN had increased precision in object detection of VHR remote sensing images, especially for geometrically variant objects such as bridges, vehicles, and baseball diamonds.…”

“…Except AP for value, Han et el. [16] used accuracy from image classification for performance comparison as follows:…”

“…Han et al [16] reported that CNNs attained an accuracy of about 70% in object detection. Objects such as airplanes, baseball diamonds, and ground track fields have higher precision than objects like storage tanks and bridges.…”

“…To evaluate deformable ConvNet in comparison to RICNN and R-P-Faster R-CNN, we established the same experiment environments as Han et al [16]. The ratios of training, validation and testing dataset were set to 20%, 20% and 60%, respectively.…”