Oriented object detection is a practical and challenging task in remote sensing image interpretation. Nowadays, oriented detectors mostly use horizontal boxes as intermedium to derive oriented boxes from them. However, the horizontal boxes are inclined to get small Intersection-over-Unions (IoUs) with ground truths, which may have some undesirable effects, such as introducing redundant noise, mismatching with ground truths, and detracting from the robustness of detectors. In this article, we propose a novel anchor-free oriented proposal generator (AOPG) that abandons horizontal box-related operations from the network architecture. AOPG first produces coarse oriented boxes by a coarse location module (CLM) in an anchor-free manner and then refines them into high-quality oriented proposals. After AOPG, we apply a Fast Region-based Convolutional Neural Network (R-CNN) head to produce the final detection results. Furthermore, the shortage of large-scale datasets is also a hindrance to the development of oriented object detection. To alleviate the data insufficiency, we release a new dataset on the basis of our DIOR dataset and name it DIOR-R. Massive experiments demonstrate the effectiveness of AOPG. Particularly, without bells and whistles, we achieve the accuracy of 64.41%, 75.24%, and 96.22% mAP on the DIOR-R, DOTA, and HRSC2016 datasets, respectively. Code and models are available at https://github.com/jbwang1997/AOPG .

Despite the progress made by few-shot segmentation (FSS) in low-data regimes, the generalization capability of most previous works could be fragile when countering hard query samples with seen-class objects. This paper proposes a fresh and powerful scheme to tackle such an intractable bias problem, dubbed base and meta (BAM). Concretely, we apply an auxiliary branch (base learner) to the conventional FSS framework (meta learner) to explicitly identify base-class objects, i.e., the regions that do not need to be segmented. Then, the coarse results output by these two learners in parallel are adaptively integrated to derive accurate segmentation predictions. Considering the sensitivity of meta learner, we further introduce adjustment factors to estimate the scene differences between support and query image pairs from both style and appearance perspectives, so as to facilitate the model ensemble forecasting. The remarkable performance gains on standard benchmarks (PASCAL-5 $^{i}$ , COCO-20 $^{i}$ , and FSS-1000) manifest the effectiveness, and surprisingly, our versatile scheme sets new state-of-the-arts even with two plain learners. Furthermore, in light of its unique nature, we also discuss several more practical but challenging extensions, including generalized FSS, 3D point cloud FSS, class-agnostic FSS, cross-domain FSS, weak-label FSS, and zero-shot segmentation. Our source code is available at https://github.com/chunbolang/BAM .

In this paper, a novel satellite image segmentation technique based on dynamic Harris hawks optimization with a mutation mechanism (DHHO/M) is proposed. Compared with the original Harris hawks optimization (HHO), the dynamic control parameter strategy and mutation operator used in DHHO/M can avoid falling into the local optimum and efficiently enhance the search capability. To evaluate the performance of the proposed method, a series of experiments are carried out on various satellite images. Eight advanced thresholding approaches are selected for comparison. Three criteria are adopted to determine the segmentation thresholds, namely Kapur’s entropy, Tsallis entropy, and Otsu between-class variance. Furthermore, four oil pollution images are used to further assess the practicality and feasibility of the proposed method on real engineering problem. The experimental results illustrate that the DHHO/M based thresholding technique is superior to others in the following three aspects: fitness function evaluation, image segmentation effect, and statistical tests.

Conventional deep CNN-based segmentation approaches have achieved satisfactory performance in recent years, however, they are essentially big data-driven technologies and are difficult to generalize to unseen categories. Few-shot segmentation is subsequently developed to perform pertinent operations in a low-data regime. Unfortunately, due to the training paradigm and network architecture factors, existing methods are prone to overfit the targets of base categories and yield inaccurate segmentation boundaries, which impedes the research progress to some extent. In this paper, we propose a Holistic Prototype Activation (HPA) network to alleviate these problems. Its novel designs can be summarized in three aspects: 1) A training-free scheme to derive the prior representations of base categories. 2) Prototype Activation Module (PAM) that generates reliable activation maps and well-matched query features by filtering the objects of irrelevant classes with high confidence. 3) Cross-Referenced Decoder (CRD) for interacted feature reweighting and multi-level feature aggregation. Extensive experiments on standard few-shot segmentation benchmarks (PASCAL-5$^{i}$ and COCO-20$^{i}$) verify the effectiveness of our method. On top of that, the superior performance on multiple extended tasks, such as weak-label segmentation, zero-shot segmentation, and video object segmentation, also illustrates its flexibility and versatility. Our code is publicly available at https://github.com/chunbolang/HPA.

Few-shot image classification has attracted extensive attention, which aims to recognize unseen classes given only a few labeled samples. Due to the large intraclass variances and interclass similarity of remote sensing scenes, the task under such circumstance is much more challenging than general few-shot image classification. Most existing prototype-based few-shot algorithms usually calculate prototypes directly from support samples and ignore the validity of prototypes, which results in a decline in the accuracy of subsequent inferences based on prototypes. To tackle this problem, we propose a Siamese-prototype network (SPNet) with prototype self-calibration (SC) and intercalibration (IC). First, to acquire more accurate prototypes, we utilize the supervision information from support labels to calibrate the prototypes generated from support features. This process is called SC. Second, we propose to consider the confidence scores of the query samples as another type of prototypes, which are then used to predict the support samples in the same way. Thus, the information interaction between support and query samples is implicitly a further calibration for prototypes (so-called IC). Our model is optimized with three losses, of which two additional losses help the model to learn more representative prototypes and make more accurate predictions. With no additional parameters to be learned, our model is very lightweight and convenient to employ. The experiments on three public remote sensing image datasets demonstrate competitive performance compared with other advanced few-shot image classification approaches. The source code is available at https://github.com/zoraup/SPNet .