Change detection is a basic task of remote sensing image processing. The research objective is to identify the change information of interest and filter out the irrelevant change information as interference factors. Recently, the rise in deep learning has provided new tools for change detection, which have yielded impressive results. However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudo-change information. To overcome the lack of resistance in current methods to pseudo-changes, in this paper, we propose a new method, namely, dual attentive fully convolutional Siamese networks (DASNet), for change detection in high-resolution images. Through the dual attention mechanism, long-range dependencies are captured to obtain more discriminant feature representations to enhance the recognition performance of the model. Moreover, the imbalanced sample is a serious problem in change detection, i.e., unchanged samples are much more abundant than changed samples, which is one of the main reasons for pseudo-changes. We propose the weighted double-margin contrastive loss to address this problem by punishing attention to unchanged feature pairs and increasing attention to changed feature pairs. The experimental results of our method on the change detection dataset (CDD) and the building change detection dataset (BCDD) demonstrate that compared with other baseline methods, the proposed method realizes maximum improvements of 2.9% and 4.2%, respectively, in the F1 score. Our PyTorch implementation is available at https://github.com/lehaifeng/DASNet.

The spatial resolution of remote sensing images is continuously improved by the development of remote sensing satellite and sensor technology. Hence, background information in an image becomes increasingly complex and causes considerable interference to the object detection task. Can we pay as much attention to the object in an image as human vision does? This letter proposes a multi-scale spatial and channel-wise attention (MSCA) mechanism to answer this question. MSCA has two advantages that help improve object detection performance. First, attention is paid to the spatial area related to the foreground, and compared with other channels, more attention is given to the feature channel with a greater response to the foreground region. Second, for objects with different scales, MSCA can generate an attention distribution map that integrates multi-scale information and applies it to the feature map of the deep network. MSCA is a flexible module that can be easily embedded into any object detection model based on deep learning. With the attention exerted by MSCA, the deep neural network can efficiently focus on objects of different backgrounds and sizes in remote sensing images. Experiments show that the mean average precision of object detection is improved after the addition of MSCA to the current object detection model.

Semantic segmentation is a key technique involved in automatic interpretation of high-resolution remote sensing (HRS) imagery and has drawn much attention in the remote sensing community. Deep convolutional neural networks (DCNNs) have been successfully applied to the HRS imagery semantic segmentation task due to their hierarchical representation ability. However, the heavy dependency on a large number of training data with dense annotation and the sensitiveness to the variation of data distribution severely restrict the potential application of DCNNs for the semantic segmentation of HRS imagery. This study proposes a novel unsupervised domain adaptation semantic segmentation network (MemoryAdaptNet) for the semantic segmentation of HRS imagery. MemoryAdaptNet constructs an output space adversarial learning scheme to bridge the domain distribution discrepancy between source domain and target domain and to narrow the influence of domain shift. Specifically, we embed an invariant feature memory module to store invariant domain-level prototype information because the features obtained from adversarial learning only tend to represent the variant feature of current limited inputs. This module is integrated by a category attention-driven invariant domain-level memory aggregation module to current pseudo invariant feature for further augmenting the representations. An entropy-based pseudo label filtering strategy is used to update the memory module with high-confident pseudo invariant feature of current target images. Extensive experiments under three cross-domain tasks indicate that our proposed MemoryAdaptNet is remarkably superior to the state-of-the-art methods.

Semantic segmentation is an important task of analysis and understanding of high-resolution remote sensing images (HRSIs). The deep convolutional neural network (DCNN)-based model shows their excellent performance in remote sensing image semantic segmentation. Most of the existing HRSI semantic segmentation methods are only designed for a very limited data domain, that is, the training and test images are from the same dataset. The accuracy drops sharply once a model trained on a certain dataset is used for cross-domain prediction due to the difference in feature distribution of the dataset. To this end, this article proposes an unsupervised domain adaptation framework based on adversarial learning for HRSI semantic segmentation. This framework uses high-level feature alignment to narrow the difference between the source and target domains at the semantic level. It uses the category-certainty attention module to reduce the attention of the classifier on category-level aligned features and increase the attention on category-level unaligned features. Experimental results show that the proposed method performs favorably against the state-of-the-art methods in cross-domain segmentation.

The lack of pixel-level labeling limits the practicality of deep learning-based building semantic segmentation. Weakly supervised semantic segmentation based on image-level labeling results in incomplete object regions and missing boundary information. This paper proposes a weakly supervised semantic segmentation method for building detection. The proposed method takes the image-level label as supervision information in a classification network that combines superpixel pooling and multi-scale feature fusion structures. The main advantage of the proposed strategy is its ability to improve the intactness and boundary accuracy of a detected building. Our method achieves impressive results on two 2D semantic labeling datasets, which outperform some competing weakly supervised methods and are close to the result of the fully supervised method.

Buildings represent the most relevant features of human activity in urban regions, but their change detection using very-high-resolution (VHR) remote sensing imagery is still a major challenge. Effective representation of the building is the key point in building change detection. The linear feature can indirectly represent the structure and distribution of man-made objects. Thus, this study proposes a shape feature-based building change detection method. Specifically, a line-constrained shape (LCS) feature is developed to capture the shape characteristics of buildings. This feature improves the discriminability between buildings and other ground objects by integrating the pixel shape feature and line segments. The building candidate area (BCA) is created in accordance with the distribution of the line segments in two-phase images. The problem space is constrained in a high-likelihood region of buildings because of the BCA. Comparative experimental results demonstrate that the combination of the spectral feature and the developed LCS feature achieves the best performance in object-based building change detection in VHR imagery.