Recently, many studies exploit deep neural networks to promote terrain segmentation in polarimetric synthetic aperture radar (PolSAR) images. However, these works usually inherit the nature-scene approaches directly and may not be robust for the PolSAR image segmentation task. The main limitations include single-type feature construction, weak feature consistency, and geometry-agnostic collection of scattering information. In this article, we present the DENet, a double-encoder network with feature refinement and region adaption for the terrain segmentation in PolSAR images. First, a double-encoder architecture is proposed to leverage the multitype information of PolSAR images, which can provide more discriminative features than the previous methods using the single-type feature. Second, considering that the polarization information has strong consistency over the category-identical regions, a polarization-guided refinement module is proposed to maintain the feature consistency in the PolSAR segmentation model. This design alleviates the phenomenon of incomplete and fragmented segmentation results. Third, in view of the rich targets’ characteristics in the scattering information, a region-adaptive convolution module is developed to facilitate the scattering information collected over the geometry-irregular regions. This design can improve the segmentation accuracy on the geometry-irregular regions. Extensive experiments are conducted on six PolSAR images to verify the effectiveness of the DENet. Compared with the previous works, our method achieves competitive performance.

Object detection in aerial images is important for a wide range of applications. The most challenging dilemma in this task is the arbitrary orientation of objects, and many deep-learning-based methods are proposed to address this issue. In previous works on oriented object detection, the regression-based method for object localization has limited performance due to the shortage of spatial information. And the models suffer from the divergence of feature construction for object recognition and localization. In this article, we propose a novel architecture, i.e., point-based estimator to remedy these problems. To utilize the spatial information explicitly, the detector encodes an oriented object with a point-based representation and operates a fully convolutional network for point localization. To improve localization accuracy, the detector takes the manner of coarse-to-fine to lessen the quantization error in point localization. To avoid the discrepancy of feature construction, the detector decouples localization and recognition with individual pathways. In the pathway of object recognition, the instance-alignment block is involved to ensure the alignment between the feature map and oriented region. Overall, the point-based estimator can be easily embedded into the region-based detector and leads to significant improvement on oriented object detection. Extensive experiments have demonstrated the effectiveness of our point-based estimator. Compared with existing works, our method shows state-of-the-art performance on oriented object detection in aerial images.

Feature pyramid is the mainstream method for multi-scale object detection. In most detectors with feature pyramid, each proposal is predicted based on feature grids pooled from only one feature level, which is assigned heuristically. Recent studies report that the feature representation extracted using this method is sub-optimal, since they ignore the valid information exists on other unselected layers of the feature pyramid. To address this issue, researchers present to fuse valid information across all feature levels. However, these methods can be further improved: the feature fusion strategies, which use common operation (element-wise max or sum) in most detectors, should be replaced by a more flexible way. In this work, a novel method called feature adaptive selection subnetwork (FAS-Net) is proposed to construct effective features for detecting objects of different scales. Particularly, its adaption consists of two level: global attention and local adaptive selection. First, we model the global context of each feature map with global attention based feature selection module (GAFSM), which can strengthen the effective features across each layer adaptively. Then we extract the features of each region of interest (RoI) on the entire feature pyramid to construct a RoI feature pyramid. Finally, the RoI feature pyramid is sent to the feature adaptive selection module (FASM) to integrate the strengthened features according to the input adaptively. Our FAS-Net can be easily extended to other two-stage object detectors with feature pyramid, and supports to analyze the importance of different feature levels for multi-scale objects quantitatively. Besides, FAS-Net can also be further applied to instance segmentation task and get consistent improvements. Experiments on PASCAL07/12 and MSCOCO17 demonstrate the effectiveness and generalization of the proposed method.

The performance of object instance segmentation in remote sensing images has been greatly improved through the introduction of many landmark frameworks based on convolutional neural network. However, the object densely issue still affects the accuracy of such segmentation frameworks. Objects of the same class are easily confused, which is most likely due to the close docking between objects. We think context information is critical to address this issue. So, we propose a novel framework called SLCMASK-Net, in which a sequence local context module (SLC) is introduced to avoid confusion between objects of the same class. The SLC module applies a sequence of dilation convolution blocks to progressively learn multi-scale context information in the mask branch. Besides, we try to add SLC module to different locations in our framework and experiment with the effect of different parameter settings. Comparative experiments are conducted on remote sensing images acquired by QuickBird with a resolution of 0.5m - 1m and the results show that the proposed method achieves state-of-the-art performance.