Semi-Supervised SAR Target Detection Based on an Improved Faster R-CNN

In the remote sensing image processing field, the synthetic aperture radar (SAR) target-detection methods based on convolutional neural networks (CNNs) have gained remarkable performance relying on large-scale labeled data. However, it is hard to obtain many labeled SAR images. Semi-supervised learning is an effective way to address the issue of limited labels on SAR images because it uses unlabeled data. In this paper, we propose an improved faster regions with CNN features (R-CNN) method, with a decoding module and a domain-adaptation module called FDDA, for semi-supervised SAR target detection. In FDDA, the decoding module is adopted to reconstruct all the labeled and unlabeled samples. In this way, a large number of unlabeled SAR images can be utilized to help structure the latent space and learn the representative features of the SAR images, devoting attention to performance promotion. Moreover, the domain-adaptation module is further introduced to utilize the unlabeled SAR images to promote the discriminability of features with the assistance of the abundantly labeled optical remote sensing (ORS) images. Specifically, the transferable features between the ORS images and SAR images are learned to reduce the domain discrepancy via the mean embedding matching, and the knowledge of ORS images is transferred to the SAR images for target detection. Ultimately, the joint optimization of the detection loss, reconstruction, and domain adaptation constraints leads to the promising performance of the FDDA. The experimental results on the measured SAR image datasets and the ORS images dataset indicate that our method achieves superior SAR target detection performance with limited labeled SAR images.

Early Labeled and Small Loss Selection Semi-Supervised Learning Method for Remote Sensing Image Scene Classification

The classification of aerial scenes has been extensively studied as the basic work of remote sensing image processing and interpretation. However, the performance of remote sensing image scene classification based on deep neural networks is limited by the number of labeled samples. In order to alleviate the demand for massive labeled samples, various methods have been proposed to apply semi-supervised learning to train the classifier using labeled and unlabeled samples. However, considering the complex contextual relationship and huge spatial differences, the existing semi-supervised learning methods bring different degrees of incorrectly labeled samples when pseudo-labeling unlabeled data. In particular, when the number of labeled samples is small, it affects the generalization performance of the model. In this article, we propose a novel semi-supervised learning method with early labeled and small loss selection. First, the model learns the characteristics of simple samples in the early stage and uses multiple early models to screen out a small number of unlabeled samples for pseudo-labeling based on this characteristic. Then, the model is trained in a semi-supervised manner by combining labeled samples, pseudo-labeled samples, and unlabeled samples. In the training process of the model, small loss selection is used to further eliminate some of the noisy labeled samples to improve the recognition accuracy of the model. Finally, in order to verify the effectiveness of the proposed method, it is compared with several state-of-the-art semi-supervised classification methods. The results show that when there are only a few labeled samples in remote sensing image scene classification, our method is always better than previous methods.

Editorial for the Special Issue “New Advances on Sub-Pixel Processing: Unmixing and Mapping Methods”

Innovative remote sensing image processing techniques have been progressively studied due to the increasing availability of remote sensing images, powerful techniques of data analysis, and computational power [...]

CANet: A Combined Attention Network for Remote Sensing Image Change Detection

Change detection (CD) is one of the essential tasks in remote sensing image processing and analysis. Remote sensing CD is a process of determining and evaluating changes in various surface objects over time. The impressive achievements of deep learning in image processing and computer vision provide an innovative concept for the task of CD. However, existing methods based on deep learning still have problems detecting small changed regions correctly and distinguishing the boundaries of the changed regions. To solve the above shortcomings and improve the efficiency of CD networks, inspired by the fact that an attention mechanism can refine features effectively, we propose an attention-based network for remote sensing CD, which has two important components: an asymmetric convolution block (ACB) and a combined attention mechanism. First, the proposed method extracts the features of bi-temporal images, which contain two parallel encoders with shared weights and structures. Then, the feature maps are fed into the combined attention module to reconstruct the change maps and obtain refined feature maps. The proposed CANet is evaluated on the two publicly available datasets for challenging remote sensing image CD. Extensive empirical results with four popular metrics show that the designed framework yields a robust CD detector with good generalization performance. In the CDD and LEVIR-CD datasets, the F1 values of the CANet are 3.3% and 1.3% higher than those of advanced CD methods, respectively. A quantitative analysis and qualitative comparison indicate that our method outperforms competitive baselines in terms of both effectiveness and robustness.