Domain generalization (DG), which tries to improve the performance of models trained with known domains but applied to unknown domains, is an important step towards practical solutions in real-world scenarios. In this paper, we tackle a much more difficult scenario called single domain generation in scene classification problem, where only one source domain is available during training. Existing DG methods usually focus on extracting invariant features from different known domains and often suffer from overfitting issues. Therefore, to tackle the above challenge, we propose a randomly-stylized data augmentation method, which enables randomized style perturbation of the training data, to alleviate the overfitting problem and to improve the robustness of the resulting model. On a multidomain scene classification benchmark, our method achieves an accuracy improvement of 0.4%-2.5% compared to other DG methods.

With the increasing spatial and temporal resolutions of obtained remote sensing (RS) images, effective compression becomes critical for storage, transmission, and large-scale in-memory processing. Although image compression methods achieve a series of breakthroughs for daily images, a straightforward application of these methods to RS domain underutilizes the properties of the RS images, such as content duplication, homogeneity, and temporal redundancy. This paper proposes a Spatial-Temporal Context model (STCM) for RS image compression, jointly leveraging context from a broader spatial scope and across different temporal images. Specifically, we propose a stacked diagonal masked module to expand the contextual reference scope, which is stackable and maintains its parallel capability. Furthermore, we propose spatial-temporal contextual adaptive coding to enable the entropy estimation to reference context across different temporal RS images at the same geographic location. Experiments show that our method outperforms previous state-of-the-art compression methods on rate-distortion (RD) performance. For downstream tasks validation, our method reduces the bitrate by 52 times for single temporal images in the scene classification task while maintaining accuracy.

Land Cover Mapping (LCM) is an important task to detect and understand the change of the earth surface. However, most LCM methods adopt supervised classifiers, and suffer from a lack of labels at a large scale. In this paper, we propose Fast-LCM, a scalable and weakly-supervised LCM method on a new Sunway supercomputer to achieve large-scale land cover mapping, requiring no manual annotations. Fast-LCM includes two major parts: (1) a distance-guided k-means module that combines textural, spectral, and temporal features, and (2) an automatic voting-based merging strategy to give each cluster a real meaning of classification system. Through careful parallelization, our Fast-LCM method scales to over 38 million cores, and provides a sustained performance for the task of China LCM. We produce a 10m resolution land cover map of China within only 3 minutes, including 1.2 minutes for IO and only 55 seconds to finish the computation. Fast-LCM achieves an accuracy of 68.85% (25-class), with 3.64% to 7.05% higher than best existing products.

High-resolution land cover mapping (LCM) is an important application for studying and understanding the change of the earth surface. While deep learning (DL) methods demonstrate great potential in analyzing satellite images, they largely depend on massive high-quality labels. This paper proposes SW-LCM, a Scalable and Weakly-supervised two-stage Land Cover Mapping method on a new Sunway Supercomputer. Our method consists of a k-means clustering module as a first stage, and an iterative deep learning module as a second stage. With the k-means module providing a good enough starting point (taking inaccurate results as noisy labels), the deep learning module improves the classification results in an iterative way, without any labelling efforts required for processing large scenarios. To achieve efficiency for country-level land cover mapping, we design a customized data partition scheme and an on-the-fly assembly for k-means. Through careful parallelization and optimization, our k-means module scales to 98,304 computing nodes (over 38 million cores), and provides a sustained performance of 437.56 PFLOPS, in a real LCM task of the entire region of China; the iterative updating part scales to 24,576 nodes, with a performance of 11 PFLOPS. We produce a 10-m resolution land cover map of China, with an accuracy of 83.5% (10-class) or 73.2% (25-class), 7% to 8% higher than best existing products, paving ways for finer land surveys to support sustainability-related applications.

Although domain adaptation approaches have been proposed to tackle cross-regional, multitemporal, and multisensor remote sensing applications since they do not require any human interpretation in the target domain, most current works assume identical label space across the source and the target domains. However, in real-world applications, we often transfer knowledge from a large-scale dataset with rich annotations to a small-scale target dataset with scarcity of labels. In most cases, the label space of the source domain is usually large enough to subsume that of the target domain, which is termed partial domain adaptation. In this article, we propose a new partial domain adaptation algorithm for remote sensing scene classification and our proposed method contains three major parts. First, we employ a progressive auxiliary domain module to alleviate the negative transfer effect caused by outlier classes. Second, we adopt an improved domain adversarial neural network (DANN) with multiweights to better encourage domain confusion. Last but not least, we design an attentive complement entropy regularization to improve the prediction confidence for samples and avoid untransferable samples (such as the samples belonging to outlier classes in the source domain) being mistakenly classified. We collect three common remote sensing datasets to evaluate our proposed method. Our method achieves an average accuracy of 79.36%, which considerably outperforms other state-of-the-art partial domain adaptation methods with an average accuracy improvement of 1.90%–12.45% and attaining a 13.67% gain compared to the straightforward deep learning model (ResNet-50). The experiment results indicate that our approach shows promising prospects for solving more general and practical domain adaptation problems where the label space of the source domain subsumes that of the target domain.

Counting and detecting oil palm trees from high-resolution remotely sensed images is a significant work for improving economy of several countries such as Malaysia, Indonesia, etc. However, rare attention have been paid on accelerating tree crown detection algorithms on high performance platforms. In this paper, we design a parallel approach for oil palm tree detection on a SW26010 many-core processor, which is used in a world-leading supercomputer, Sunway TaihuLight. Our parallel framework contains three steps, local maximum filtering, oil palm tree crown center reassignment and oil palm tree crown center merging. Experimental results indicates that our parallel approach of oil palm tree detection obtains the speedup of 32.30 times and 1.74 times for a QuickBird image with a size of$12,188\times 12,576$pixels compared with the well-optimized software implementation of the original algorithm on an Intel 12-core CPU and FPGAs.

Over the past decade, domain adaptation (DA) algorithms have been proposed to address domain gap problems as they do not need any interpretation in the target domain. However, most existing efforts focus on scenarios with only one source domain and one target domain. In this article, we explore the scenario with one source domain and mixed multiple target domains for remote sensing applications and propose a new algorithm, named the two-stage adaptation network (TSAN). First, we utilize the adversarial learning approach to confuse the classifier to discriminate between the source domain and the whole mixed-multiple-target domain. Second, we adopt self-supervised learning to divide the mixed-multiple-target domain with automated generation of “pseudo”-domain labels, which guides our network to learn intrinsic features of multiple target domains. Finally, these two steps are combined as an iterative procedure. We integrate a test dataset that includes five remote sensing datasets and ten classes. Our method achieves an average accuracy of 63.25% and 73.68% with two typical backbones, considerably outperforming other DA methods with an average accuracy improvement of 4.84%–20.19% and 9.06%–17.04%, respectively. Furthermore, we identify the negative transfer effect in existing mainstream DA methods in remote sensing image classification with multiple different domains.

Although Deep Convolutional Neural Network (DCNN) has been admittedly witnessed as an enormous success in a wide range of applications, most of them require sufficient annotations with time-consuming and labor-exhausting efforts. Existing domain adaptation (DA) approaches delve into designing an effective loss module to minimize the distribution gap between the source and target domains. However, few studies pay attention to improve the backbone or network architecture for DA issues. In this paper, we propose a new backbone for DA specially, i.e., Transferable ResNet (TransResNet). TransResNet remedies the residual block in ResNet, separating source and target input features and highlighting more transferable channels in each block. It can be easily applied to all kinds of DA methods, without adding any extra learning parameters. We conduct substantial experiments on two general DA datasets and embed TransResNet into two seminal DA methods, including DANN and CDAN. Experimental results demonstrate TransResNet improves the transferability of the architecture, indicating that it is a great substitute for ResNet as a network backbone in DA issues.