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Domain adaptive segmentation has recently gained more and more attention in the remote sensing field. However, current methods often generate a significant number of uncertain examples, i.e., noisy pseudo-labels, in the target domain, which adversely affects model convergence. To solve this issue, an uncertain example mining network is proposed for domain adaptive segmentation of remote sensing images. Specifically, a novel strategy called multilevel pseudo-label correcting (MPC) is proposed to correct the pseudo-labels in class, pixel, and superpixel levels. In this way, more reliable pseudo-labels can be selected for the subsequent training stage. Furthermore, a noise-robust example mining strategy, termed uncertainty-based valuable example mining (UVEM), is proposed to prioritize confident examples with significant gradients for training effectively. Extensive empirical evaluations on IsprsDA and LoveDA datasets demonstrate that the proposed method outperforms previous approaches, establishing state-of-the-art results in domain adaptive remote sensing image segmentation (RSIS). The code will be available at https://github.com/StuLiu/UemDA.

Hyperspectral images (HSIs) offer detailed and abundant spectral-spatial information, holding great potential for ground object change detection (CD). Currently, numerous deep learning-based hyperspectral CD (HCD) models have been studied. However, these methods only consider single difference information or temporal information while neglecting the complementary advantage between difference and temporal information. To solve this issue, this work proposes a multitemporal symmetric fusion network (MTSFNet) for HCD, which involves three steps. First, the difference and temporal data are calculated by a subtraction operation and concatenation operation. Then, a dual-branch convolutional network is developed to capture the difference and temporal features. Finally, a symmetric feature fusion scheme is designed to integrate the extracted features followed by a fully connected layer to derive the detection results. Experiments conducted on multiple well-known datasets reveal that the proposed MTSFNet outperforms other advanced CD approaches in terms of qualitative and quantitative results.

In the past few years, there has been significant progress in hyperspectral image classification (HSIC). However, when the trained classifier on the source scene is directly applied to a new scene, the classification performance tends to dramatically decrease because of the spectral shift phenomenon. Most existing techniques use feature alignment to learn knowledge from labeled scenes to unlabeled scenes, often overlooking the impact of noisy samples and outliers. To tackle this issue, the classwise prototype-guided alignment network (CPGAN) is proposed for cross-scene HSIC. The core idea is that classwise prototypes across scenes are employed as alignment intermediaries to guide cross-scene feature alignment. Specifically, first, spectral-spatial features from different scenes are extracted with a common feature extractor. Then, an uncertainty-aware pseudolabel selection (UPS) is designed to obtain high-confidence pseudolabels for unlabeled target scenes. Finally, a novel classwise prototype-guided alignment method is proposed to simultaneously achieve interdomain and intradomain alignment (IntraDA). The experimental results conducted on three datasets show that our method achieves superior performance compared to other cutting-edge classification algorithms.

Nowadays, high-resolution remote sensing images provide rich data sources and deep learning models show powerful feature representation capability for remote sensing object detection. However, due to the complex object structure as well as the changeable rotation angle, how to efficiently estimate the oriented bounding box regarding the accurate location of objects is still an open issue. Focused on this, a new one-stage structure-adaptive oriented object detection (SOOD) network is proposed, in this article. First, we designed a new rotation angle encoder (RAE), where an angle coordinate system is adopted and periodic angle correction is conducted. Different from the traditional longe-edge definition for angle estimation, the RAE can mitigate boundary discontinuity and square-like problems. Then, structure-adaptive label assignment (SALA) and confidence estimation (SACE) are introduced, to locate the position of objects more accurately. On the one hand, the anchor box determines the label assignment according to the affiliation relationship between the center point and the object’s inner ellipse boundary. By constraining the ellipse boundary and employing non-parametric label assignment, high-quality anchor boxes are initially selected, and low-quality anchor boxes are suppressed. On the other hand, the integration of intersection over union (IoU) prediction and uncertainty prediction constructs a quality evaluation function to guide. In this manner, this function dynamically evaluates the localization and classification ability of each prediction box. Extensive experiments on publicly available datasets such as DOTA1.0, DOTA1.5, DIOR, and MAR20 demonstrate the effectiveness of the proposed model. The source code will be available at https://github.com/fan609/SOOD.

Remote sensing image change detection can analyze alterations on the Earth’s surface within a specific region. However, the accuracy of change detection has consistently been hindered by the style differences in captured images caused by seasonal or lighting variations, as well as the challenge of distinguishing similar features between the background and foreground in the scene. To this end, a multidomain differential excavating network (MDENet) for change detection is introduced. Using the novel multidomain differential collaboration module (MDCM) to precisely capture object features on the frequency and spatial domains across diverse temporal domains, it enables simultaneous querying of global and local change information. Moreover, the multineighborhood frequency gate attention (MFGatt) is devised to eliminate the impact of image style relevance information and consolidate attention toward object localization, thereby enhancing the adaptability of the network to variations in image style. Extensive experiments have illustrated that our proposed network achieves better detection accuracy compared with current state-of-the-art (SOTA) methods on various datasets.

In this article, a two-dimensional (2-D) spectral representation is proposed for the visualization and classification of hyperspectral images (HSIs). First, several sequence data processing methods, i.e., Gramian angular field (GAF) algorithm, Markov transition field (MTF), and recurrence plot (REP), are applied to obtain multiple 2-D features of a one-dimensional (1-D) spectrum. Second, the 2-D spectral features are stacked together to form the final 2-D spectral representation. Finally, many excellent classifiers in computer vision field are applied on the 2-D spectral representation to obtain the final classification result. Furthermore, 114 target spectral visualization maps are established based on their 1-D spectra. Experimental results reveal that the 2-D spectral representation has multiple advantages in terms of better visual quality and classification accuracies. The code of this work is available at https://github.com/zhuyongxiang1/two-dimensional-spectral-representation.

Hyperspectral image (HSI) classification methods have made great progress in recent years. However, most of these methods are rooted in the closed-set assumption that the class distribution in the training and testing stages is consistent, which cannot handle the unknown class in open-world scenes. In this work, we propose a feature consistency-based prototype network (FCPN) for open-set HSI classification, which is composed of three steps. First, a three-layer convolutional network is designed to extract the discriminative features, where a contrastive clustering module is introduced to enhance the discrimination. Then, the extracted features are used to construct a scalable prototype set. Finally, a prototype-guided open-set module (POSM) is proposed to identify the known samples and unknown samples. Extensive experiments reveal that our method achieves remarkable classification performance over other state-of-the-art classification techniques.