Central to the looming paradigm shift toward data-intensive science, machine-learning techniques are becoming increasingly important. In particular, deep learning has proven to be both a major breakthrough and an extremely powerful tool in many fields. Shall we embrace deep learning as the key to everything? Or should we resist a black-box solution? These are controversial issues within the remote-sensing community. In this article, we analyze the challenges of using deep learning for remote-sensing data analysis, review recent advances, and provide resources we hope will make deep learning in remote sensing seem ridiculously simple. More importantly, we encourage remote-sensing scientists to bring their expertise into deep learning and use it as an implicit general model to tackle unprecedented, large-scale, influential challenges, such as climate change and urbanization.

Following the great success of deep convolutional neural networks (CNNs) in computer vision, this paper proposes a complex-valued CNN (CV-CNN) specifically for synthetic aperture radar (SAR) image interpretation. It utilizes both amplitude and phase information of complex SAR imagery. All elements of CNN including input-output layer, convolution layer, activation function, and pooling layer are extended to the complex domain. Moreover, a complex backpropagation algorithm based on stochastic gradient descent is derived for CV-CNN training. The proposed CV-CNN is then tested on the typical polarimetric SAR image classification task which classifies each pixel into known terrain types via supervised training. Experiments with the benchmark data sets of Flevoland and Oberpfaffenhofen show that the classification error can be further reduced if employing CV-CNN instead of conventional real-valued CNN with the same degrees of freedom. The performance of CV-CNN is comparable to that of existing state-of-the-art methods in terms of overall classification accuracy.

This paper proposes a novel method for ship detection and discrimination in complex background from synthetic aperture radar (SAR) images. It first implements a pixel-level land-sea segmentation with the aid of a global 250-m water mask. Then, an efficient multiscale constant false alarm rate (CFAR) detector with generalized Gamma distribution clutter model is designed to detect candidate targets in the sea. At last, eigenellipse discrimination and maximum-likelihood (ML) discrimination are designed to further exclude false alarm nonship objects in nearshore and harbor area. The proposed land-sea segmentation method is compared with multilevel Otsu method. The proposed multiscale ship detector is compared with conventional CFAR detectors. These contrast experiments show the good performance of our method. Finally, experiments undertaken on actual ALOS-2 SAR data show the efficacy of the proposed approach in detecting nearshore ship targets in a complex coastal environment.

Semantic image segmentation is one kindof end-to-end segmentation method which can classify the target region pixel by pixel. As a classic semantic segmentation network in optical images, DeepLabv3+ can achieve a good segmentation performance. However, when this network is directly used in the semantic segmentation of polarimetric synthetic aperture radar (PolSAR) image, it is hard to obtain the ideal segmentation results. The reason is that it is easy to yield overfitting due to the small PolSAR dataset. In this article, a lightweight complex-valued DeepLabv3+ (L-CV-DeepLabv3+) is proposed for semantic segmentation of PolSAR data. It has two significant advantages when compared with the original DeepLabv3+. First, the proposed network with the simplified structure and parameters can be suitable for the small PolSAR data, and thus, it can effectively avoid the overfitting. Second, the proposed complex-valued (CV) network can make full use of both amplitude and phase information of PolSAR data, which brings better segmentation performance than the real-valued (RV) network, and the related CV operations are strictly true in the mathematical sense. Experimental results about two Flevoland datasets and one San Francisco dataset show that the proposed network can obtain better overall average, mean intersection over union, and mean pixel accuracy than the original DeepLabv3+ and some other RV semantic segmentation networks.

Aircraft detection in synthetic aperture radar (SAR) images is a challenging task because of the discreteness, variability, and interference of aircraft scattering features. This article proposes a new hybrid approach of scattering information enhancement (SIE) and an attention pyramid network (APN). It first extracts strong scattering points (SSPs) of aircraft via an adapted Harris-Laplace detector. These SSPs are then clustered into candidate scattering regions by density-based spatial clustering of applications with noise (DBSCAN) and are then modeled with a Gaussian mixture model (GMM). Target scattering clusters are discriminated from background clutter by measuring the Kullback-Leibler divergence (KLD) to the known target templates. These target scattering clusters are enhanced in the preprocessing stage. All the SIE-preprocessed images are then fed into the APN for training and testing. It is composed of the multiscale feature pyramid network (FPN) and the modified convolutional block attention module (CBAM) to cope with the discreteness and variability of aircraft. In addition, focal loss (FL) is adopted to deal with the issue of unbalanced sample distribution and the interference from hard samples. Experiments conducted on the Gaofen-3 and TerraSAR-X data sets demonstrate the effectiveness of the proposed method with an average precision (AP) of 83.25%.