Scene classification for Remote sensing image has attracted great attention because of its difficulties and wide application. There exits several limitations for traditional CNN-based methods, such as insufficient feature extraction ability and complex target of remote sensing image features. In addition, the experimental data is based on the overhead view, which is characterized by fuzzy semantics, small differences between classes and significant differences within classes. To address those issues, we realize several classic network improvement methods such as transfer learning and introduce the attention mechanism Squeeze-and-Excitation (SE) module. We carry out the fine-grained analysis of the space-based view scene image, specifically using the progressive multi-granularity puzzle training for scene recognition. We also propose a semantic-driven scene fine-grained enhancement based on the classic classification network and the progressive multi-granularity puzzle training. To verify the effectiveness of the proposed semantic-driven scene fine-grained enhancement model, we conduct comparative experiments based on several widely used CNN models and a public remote sensing image scene classification data set, and achieve the state-of-the-art result on the data set.
As a newly proposed research topic in recent years, panoptic segmentation is the combination of semantic segmentation and instance segmentation. The difficulties of panoptic segmentation include not only the common problems in traditional segmentation tasks, such as small object segmentation and low edge segmentation accuracy, but also the two-way fusion and the determination of conflicts. Considering the defect of edge segmentation, we use the edge optimization module SegFix based on edge detection and direction prediction for edge optimization, which reaches higher accuracy and shortens the calculation time by 3 times compared with Dense CRF. Based on the dual CNN fusion, we select EfficientNet as the baseline, which is more efficient, with depthwise separable convolutions and Mask R-CNN to achieve two-way segmentation. In addition, we use SegFix in multiple panoptic segmentation models to verify its versatility in panoptic segmentation. Finally, our PQ on the Cityscapes validation set reaches 65.5, which achieves the state-of-the-art result with all other panoptic segmentation models under the same experimental conditions, and we confirm that the edge optimization algorithm we use is universal for panoptic segmentation, and its consequence is better than other edge optimization algorithms.
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