Automatic remote sensing image registration has achieved great accomplishment. However, it is still a vital challenging problem to develop a robust and accurate registration method due to the negative effects of noise and imaging differences between images. For these images, it is difficult to guarantee the accuracy and robustness at the same time for one-step registration methods. To address this issue, we introduce an effective coarse-to-fine strategy and develop a new two-step registration method based on deep and local features in this paper. The first step is to calculate the approximate spatial relationship, which is obtained by a convolutional neural network. This step makes full use of the deep features to match and can generate stable results. For the second step, a matching strategy considering spatial relationship is applied to the local feature-based method. In addition, this step adopts more accurate features in location to adjust the results of the previous step. A variety of homologous and multimodal remote sensing images, including optical, synthetic aperture radar, and general map images, are used to evaluate the proposed method. The comparison experiments demonstrate that our method can apparently increase the correct correspondences, can improve the ratio of correct correspondences, and is highly robust and accurate.

Recently, multicategory object detection in high-resolution remote sensing images is still a challenge. First, objects with significant scale differences exist in one scene simultaneously, so it is generally difficult for the detectors to balance the detection performance of large and small objects. Second, because of the complex background and the objects’ densely distributed characteristics in the remote sensing images, the extracted features usually have noise and blurred boundaries, which interfere with the detection performance of the object detectors. With this observation, we propose an end-to-end scale-aware network called feature split–merge–enhancement network (SME-Net) for remote sensing object detection, composed of the feature split-and-merge (FSM) module, the offset-error rectification (OER) module, and the object saliency enhancement (OSE) strategy. FSM eliminates salient information of large objects to highlight the features of small objects in the shallow feature maps. It also transmits the effective detailed features of large objects to the deep feature maps, alleviating feature confusion between multiscale objects. OER corrects the inconsistency of the features spatial layout among the multilayer feature maps by the proposed offset loss, so as to achieve supervised elimination and transmission in FSM. OSE enhances the features of interests and suppresses the background information by the proposed membership function, thus preventing false detection and missed detection caused by noise and blurred boundaries. The effectiveness of the proposed algorithm has been verified on multiple datasets. Our code is available at: https://github.com/Momuli/SMENet.git

In remote sensing images, boundary information plays a crucial role in land-cover segmentation. However, it is a challenging problem that sufficiently extracts complete and sharp boundaries from complex very-high-resolution (VHR) remote sensing images. To tackle this problem, we propose a semantic boundary awareness network (SBANet). The SBANet captures refined boundary information of land covers in feature extraction and then supervises its learning with a designed boundary loss. The key of SBANet includes boundary attention module (BA-module) and adaptive weights of multitask learning (AWML). The BA-module is proposed to capture land-cover boundary information from hierarchical features aggregation in a bottom-up manner. It emphasizes useful boundary information and relieves noise information in low-level features with the guidance of high-level features. To directly learn the boundary information, AWML adds a boundary loss to the original semantic loss by an adaptive fusion manner. This multitask learning enables the semantic information and the boundary information to work collaboratively and promote each other. Note that the BA-module and AWML are plug-and-play. Experimental results demonstrate the effectiveness of the proposed SBANet on the available ISPRS 2-D semantic labeling Potsdam and Vaihingen data sets. The SBANet also achieves the state-of-the-art performance in terms of overall accuracy (OA) and mean $F_{1}$ score (m- $F_{1}$ ).

Rapid development of remote sensing (RS) imaging technology makes the acquired images have larger size, higher resolution, and more complex structure, which goes beyond the reach of classical hand-crafted feature-based matching. In this paper, we propose a feature learning approach based on two-branch networks to transform the image matching task into a two-class classification problem. To match two key points, two image patches centered at the key points are entered into the proposed network. The network aims to learn discriminative feature representations for patch matching, so that more matching pairs can be obtained on the premise of maintaining higher subpixel matching accuracy. The proposed network adopts a two-stage training mode to deal with the complex characteristics of RS images. An adaptive sample selection strategy is proposed to determine the size of each patch by the scale of its central key point. Thus, each patch can preserve the texture structure around its key point rather than all patches have a predetermined size. In the matching prediction stage, two strategies, namely, superpixel-based sample graded strategy and superpixel-based ordered spatial matching, are designed to improve the matching efficiency and matching accuracy, respectively. The experimental results and theoretical analysis demonstrate the feasibility, robustness, and effectiveness of the proposed method.

With the development of remote sensing technology, panchromatic images (PANs) and multispectral images (MSs) can be easily obtained. PAN has higher spatial resolution, while MS has more spectral information. So how to use the two kinds of images' characteristics to design a network has become a hot research field. In this article, a multi-scale progressive collaborative attention network (MPCA-Net) is proposed for PAN and MS's fusion classification. Compared to the traditional multi-scale convolution operations, we adopt an adaptive dilation rate selection strategy (ADR-SS) to adaptively select the dilation rate to deal with the problem of category area's excessive scale differences. For the traditional pixel-by-pixel sliding window sampling strategy, the patches which are generated by adjacent pixels but belonging to different categories contain a considerable overlap of information. So we change original sampling strategy and propose a center pixel migration (CPM) strategy. It migrates the center pixel to the most similar position of the neighborhood information for classification, which reduces network confusion and increases its stability. Moreover, due to the different spatial and spectral characteristics of PAN and MS, the same network structure for the two branches ignores their respective advantages. For a certain branch, as the network deepens, characteristic has different representations in different stages, so using the same module in multiple feature extraction stages is inappropriate. Thus we carefully design different modules for each feature extraction stage of the two branches. Between the two branches, because the strong mapping methods of directly cascading their features are too rough, we design collaborative progressive fusion modules to eliminate the differences. The experimental results verify that our proposed method can achieve competitive performance.

With the rapid development of earth observation technology, panchromatic (PAN) and multispectral (MS) images have also become easier to obtain. The multiresolution classification of PAN and MS images as a basic MS image analysis task has become a research hotspot. The main challenge in this field is how to process data and extract features to improve classification accuracy effectively. In this article, we design a novel adaptive hybrid fusion network (AHF-Net) for multiresolution remote sensing image classification. It includes two parts: data fusion and feature fusion. In the data fusion part, we propose an adaptive weighted intensity-hue-saturation (AWIHS) strategy, which can reduce the difference between MS and PAN images by adaptively adding each other’s unique information from the perspective of information sharing. In the feature fusion part, starting from the second-order correlation of features, we propose a correlation-based attention feature fusion (CAFF) module. It can improve the discrimination of fusion features by adaptively determining the fusion coefficient according to the importance of the input feature channel. Based on AWIHS and CAFF, inspired by the idea of feature pyramid, we combine the multilevel feature fusion and the dual-branch residual network as the backbone network of AHF-Net. By combining AWIHS and CAFF modules with the backbone network, our AHF-Net can effectively improve the classification accuracy of multiresolution remote sensing images. The effectiveness of the proposed algorithm has been verified on multiple data sets. Our code and model are available at https://github.com/1826133674/AHF-Net .