A TwoStream Symmetric Network with Bidirectional Ensemble for Aerial Image Matching In this paper, we propose a novel method to precisely match two aerial images that were obtained in different environments via a twostream deep network. By internally augmenting the target image, the network considers the twostream with the three input images and reflects the additional augmented pair in the training. As a result, the training process of the deep network is regularized and the network becomes robust for the variance of aerial images. Furthermore, we introduce an ensemble method that is based on the bidirectional network, which is motivated by the isomorphic nature of the geometric transformation. We obtain two global transformation parameters without any additional network or parameters, which alleviate asymmetric matching results and enable significant improvement in performance by fusing two outcomes. For the experiment, we adopt aerial images from Google Earth and the International Society for Photogrammetry and Remote Sensing (ISPRS). To quantitatively assess our result, we apply the probability of correct keypoints (PCK) metric, which measures the degree of matching. The qualitative and quantitative results show the sizable gap of performance compared to the conventional methods for matching the aerial images. All code and our trained model, as well as the dataset are available online.

Bigradient Verification for GradCAM Towards Accurate Visual Explanation for Remote Sensing Images Gradientweighted Class Activation Mapping (GradCAM) has been a successful technique to produce visual explanation for CNNbased models. In this paper, we verify its applicability in the task of remote sensing image classification. The results show GradCAM gives contradictory localization of the important regions for some remote sensing images. To solve this problem, we propose a new strategy, bidirectional gradient verification (BiGradV), to rectify the visual explanation produced by GradCAM. The BiGradV is based on the fact both positive and negative gradients can be sensitive to class discrimination of remote sensing images. It designs an internal feature map occlusion with confidence drop decision to verifying which directional gradients works for certain class. The verified gradients are then used to gain the correct visual explanation. Experiments on both remote sensing image dataset and general image datasets demonstrate our proposed strategy is effective and generalized. It could provide a good complement to the GradCAM based methods.