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Deep learning-based (DL-based) Synthetic Aperture Radar (SAR) image classification is an open problem when training samples are scarce. Transfer learning-based (TL-based) few-shot methods is effective to deal with this problem by transferring knowledge from the Electro-Optical (EO) to the SAR domain.The performance of such methods relies on extra SAR samples such as unlabeled novel classes samples or labeled similar classes samples. However, it is unrealistic to collect sufficient extra SAR samples in some application scenarios, namely the extreme few-shot case. In this case, the performance of such methods degrades seriously. Therefore, few-shot methods that reduce the dependence on extra SAR samples are critical. Motivated by this, a novel few-shot transfer learning method for SAR image classification in the extreme few-shot case is proposed: 1) we propose the connection-free attention module to selectively transfer features shared between EO and SAR samples from a source network to a target network to supplement the loss of information brought by extra SAR samples; 2) Based on the Bayesian convolutional neural network (Bayesian-CNN), we propose a training strategy for the extreme few-shot case, which focuses on updating important parameters, namely the accurately updating important parameters (AUIP). The experimental results on three real SAR datasets demonstrate the superiority of our method.

The analysis of built-up areas has always been a popular research topic for remote sensing applications. However, automatic extraction of built-up areas from a wide range of regions remains challenging. In this article, a fully convolutional network (FCN)–based strategy is proposed to address built-up area extraction. The proposed algorithm can be divided into two main steps. First, divide the remote sensing image into blocks and extract their deep features by a lightweight multi-branch convolutional neural network (LMB-CNN). Second, rearrange the deep features into feature maps that are fed into a well-designed FCN for image segmentation. Our FCN is integrated with multi-branch blocks and outputs multi-channel segmentation masks that are utilized to balance the false alarm and missing alarm. Experiments demonstrate that the overall classification accuracy of the proposed algorithm can achieve 98.75% in the test data set and that it has a faster processing compared with the existing state-of-the-art algorithms.

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