This article focuses on end-to-end image matching through joint key-point detection and descriptor extraction. To find repeatable and high discrimination key points, we improve the deep matching network from the perspectives of network structure and network optimization. First, we propose a concurrent multiscale detector (CS-det) network, which consists of several parallel convolutional networks to extract multiscale features and multilevel discriminative information for key-point detection. Moreover, we introduce an attention module to fuse the response maps of various features adaptively. Importantly, we propose two novel rank consistent losses (RC-losses) for network optimization, significantly improving image matching performances. On the one hand, we propose a score rank consistent loss (RC-S-loss) to ensure that the key points have high repeatability. Different from the score difference loss merely focusing on the absolute score of an individual key point, our proposed RC-S-loss pays more attention to the relative score of key points in the image. On the other hand, we propose a score-discrimination RC-loss to ensure that the key point has high discrimination, which can reduce the confusion from other key points in subsequent matching and then further enhance the accuracy of image matching. Extensive experimental results demonstrate that the proposed CS-det improves the mean matching result of deep detector by 1.4%-2.1%, and the proposed RC-losses can boost the matching performances by 2.7%-3.4% than score difference loss. Our source codes are available at https://github.com/iquandou/CS-Net.

Learning-based methods for robotic grasping have been shown to yield high performance. However, they rely on expensive-to-acquire and well-labeled datasets. In addition, how to generalize the learned grasping ability across different scenarios is still unsolved. In this paper, we present a novel grasp adaptation strategy to transfer the learned grasping ability to new domains based on visual data using a new grasp feature representation. We present a conditional generative model for visual data transformation. By leveraging the deep feature representational capacity from the well-trained grasp synthesis model, our approach utilizes feature-level contrastive representation learning and adopts adversarial learning on output space. This way we bridge the domain gap between the new domain and the training domain while keeping consistency during the adaptation process. Based on transformed input grasp data via the generator, our trained model can generalize to new domains without any fine-tuning. The proposed method is evaluated on benchmark datasets and based on real robot experiments. The results show that our approach leads to high performance in new scenarios.

Due to the complementary information between multi-modal images, they are widely used in various applications. However, there are significant differences in appearance caused by different imaging mechanisms, which bring great challenges to multi-modal image patch matching. To solve this problem, this paper proposes a deep modality independent descriptor learning network (DMID-Net) for multi-modal image patch matching. DMID-Net computes the self-similarity of deep features as the structure descriptor for image patch matching, which is independent of image modality and shared between multi-modal images. Thus, the acquired deep modality independent descriptor(DMID) can reduce the influence of significant differences between multi-modal images, further improving the matching performances. Experimental results on a large number of optical and SAR image-pairs demonstrate the effectiveness of DMID-Net on multi-modal image patch matching.

Deep descriptors have advantages over handcrafted descriptors on local image patch matching. However, due to the complex imaging mechanism of remote sensing images and the significant differences in appearance between multi-modal images, existing deep learning descriptors are unsuitable for multi-modal remote sensing image registration directly. To solve this problem, this paper proposes a deep feature correlation learning network (Cnet) for multi-modal remote sensing image registration. Firstly, Cnet builds a feature learning network based on the deep convolutional network with the attention learning module, to enhance the feature representation by focusing on meaningful features. Secondly, this paper designs a novel feature correlation loss function for Cnet optimization. It focuses on the relative feature correlation between matching and non-matching samples, which can improve the stability of network training and decrease the risk of overfitting. Additionally, the proposed feature correlation loss with a scale factor can further enhance the network training and accelerate the network convergence. Extensive experimental results on image patch matching (Brown, HPatches), cross-spectral image registration (VIS-NIR), multi-modal remote sensing image registration, and single-modal remote sensing image registration have demonstrated the effectiveness and robustness of the proposed method.

Optical and SAR image registration is important for multi-modal remote sensing image information fusion. Recently, deep matching networks have shown better performances than traditional methods on image matching. However, due to significant differences between optical and SAR images, the performances of existing deep learning methods still need to be further improved. This paper proposes a self-distillation feature learning network (SDNet) for optical and SAR image registration, improving performance from network structure and network optimization. Firstly, we explore the impact of different weight-sharing strategies on optical and SAR image matching. Then, we design a partially unshared feature learning network for multi-modal image feature learning. It has fewer parameters than the fully unshared network and has more flexibility than the fully shared network. Additionally, the limited binary supervised information (matching or non-matching) is insufficient to train the deep matching networks for optical-SAR image registration. Thus, we propose a self-distillation feature learning method to exploit more similarity information for deep network optimization enhancing, such as the similarity ordering between a series of non-matching patch-pairs. The exploited rich similarity information will significantly enhance network training and improve matching accuracy. Finally, considering that existing deep learning methods brute-force constrain the features of the matching optical and SAR image patches are similar, which will be lost many discriminative information, degenerating matching performances. Thus, we build an auxiliary task reconstruction learning to optimize the feature learning network to keep more discriminative information. Extensive experiments demonstrate the effectiveness of our proposed method on multi-modal image registration.

Multi-modal remote sensing images have complementary information which is conducive to enhancing the performance of various applications. Image patch matching plays a crucial role in the combination of multi-modal images. However, there are great differences in appearance and texture of multi-modal images, which brings great difficulties to image patching matching. To solve this problem, we propose a novel feature decomposition framework for multi-modal image patch matching. It aims to eliminate the hinder caused by the significant difference in multi-modal images. Specifically, this paper proposes to decompose the feature of images into common feature and modal private feature. Then, only the common feature is used for image patch matching, so as to improve the matching accuracy. Experimental results on optical and SAR images demonstrate that our proposed feature decomposition framework can significantly improve the performance of multi-modal image patch matching.

Due to the different imaging mechanisms, there is a significant non-line difference between multi-modal images, which brings difficulties to multi-modal image registration. The traditional methods based on grayscale and handcraft features are difficult with obtain common features between different source images. The performances of deep local features matching methods rely on the quality and quantity of the detected keypoints, which can be quite time-consuming to register images. To achieve fast and accurate multi-modal image registration, we propose a deep global feature-based template matching method (GFTM) which uses a deep convolutional network to extract common global deep features from multi-modal images. Then, fast template matching is performed on global deep features to search the position with maximal similarity. Additionally, we build a similarity label map and design three losses to optimize our network, including contrast loss, error loss and peak loss. Extensive experimental results on optical and SAR images demonstrated that our proposed method is effective on multi-modal image registration.