Remote-sensing scene classification (RSSC) is crucial for remote-sensing image interpretation and has become a research hotspot in recent years. However, the high complexity of remote-sensing scenes causes most RSSC models to fail to accurately capture key objects, resulting in low classification accuracy. Meanwhile, it is intractable to effectively distinguish similar scenes, such as forest and meadow, whose semantic labels are mainly determined by wide-scale features. In addition, existing remote-sensing attention mechanisms are heuristic settings, which require expert knowledge and extensive experiments. To solve the above problems, a novel plug-and-play hyperparameter-free attention module (HFAM) based on feature map mathematical calculation is proposed in this work. HFAM uses statistical indicators to quantitatively characterize the fluctuations of feature maps that can accurately locate key features and distinguish different scenes, alleviating the problems of intraclass diversity and interclass similarity. Moreover, HFAM adaptively acquires attention weights by performing simple mathematical calculations on the feature maps, which solves the problem of difficult adjustment of hyperparameters. Our proposed HFAM can be expediently inserted into the existing ConvNet models without increasing the number of model’s parameters. Extensive contrast experiments with several famous plug-and-play attention modules on three mainstream datasets reveal the superiority of our HFAM in accuracy, number of parameters, and calculation amount. Moreover, compared with state-of-the-art methods, it also demonstrated considerable competitiveness.

Oriented object detectors have achieved great success in aerial detection tasks with the help of ample labeled data. Unlabeled images are easier and less expensive to obtain than labeled aerial images. Therefore, semi-supervised oriented object detection (SSOOD) is becoming a hot task, which can leverage both labeled and unlabeled data to train oriented detectors. Most SSOOD approaches focus on well-designed approaches to generate high-quality pseudo labels (PLs) or positive learning regions, which are limited to complex and variable aerial scenes. This study first analyzes key factors influencing the performance of SSOOD and proposes a global focal learning method (termed as focal teacher) without artificial priori design. It relies on global region and soft regression approaches to blur the boundaries between positive and negative samples, mainly through localization focal loss to achieve. It leverages the localization consistency between the teacher and student model to focus more on hard regions. Moreover, we organize a large remote sensing unlabeled (RSUL) dataset to exploit the performance potential of oriented detectors on mainstream aerial detection datasets (DOTA and DIOR). Adequate experiments reveal that the proposed method achieves the best performance compared with other mainstream SSOOD methods, including partly, fully, and additional data settings on DOTA and DIOR datasets. Semi-supervised mechanisms without preset learning regions can be better applied in dense and complex aerial scenes.

Rotation object detection is of great importance in remote sensing imagery where the orientation is arbitrary and objects are densely distributed. However, there are several challenges that need to be overcome, such as the angular boundary problem in the regression-based methods and the square-like problem in the classification-based methods. For square-like object rotation detection, classification-based methods [e.g., circular smooth label (CSL)] suffer from inconsistencies between angular coding and evaluation mechanisms due to the variation of aspect ratio. To address the angular inconsistencies of square-like object existing in current classification methods, we design a novel angular encoding mechanism based on aspect ratio. We make optimizations and improvements in the following two aspects: 1) proposing an aspect ratio-based bidirectional coded label (AR-BCL) to replace CSL for angle coding of square-like object, which significantly improves detection accuracy for square-like object and ii) designing a cross-fusion decoupled head (CF-DH) based on angular classification to replace the existing coupled head (CH), which can help extract features that suitable for angular classification. Extensive experiments on DOTA, a large-scale public dataset for aerial images, demonstrate the effectiveness of our method for square-like object detection.

Oriented object detection plays an important role in aerial image interpretation. Image processing speed is also essential due to massive amounts of aerial images. Anchor-free oriented detectors with fast processing speed are generally accepted despite the absence of pre-set anchors, contributing to their performance gap with anchor-based detectors. Most anchor-free oriented detectors are carefully designed by defining samples according to target characteristics, which require substantial prior knowledge, to realize improved performance. This study proposes an anchor-free oriented detector (termed as rfpoint) that requires minimal prior knowledge. Moreover, this study mainly aims to introduce a dynamic sample definition strategy. This strategy is modeled as a dynamic regulating process, wherein the classification and box regression interact until the model converges. A rotating quality-driven loss (RQDL) and adaptive-weight box loss (AWBL) are also proposed to realize the aforementioned process. RQDL redefines positive and negative attributes of samples according to the distribution of rotating Intersection-over-Unit (IoU) between predictions and ground truth. AWBL adjusts the importance degree of candidate samples in the box regression through classification scores. The proposed method is then tested on three mainstream aerial image datasets (DOTA, DIOR, and HRSC2016). Results reveal that the proposed method achieves the best performance compared with other oriented detectors, whose mAP are 79.92%, 70.88%, and 90.67%. Moreover, the method maintains the inference speed advantage of anchor-free detectors. An effective sample definition method can bridge the performance gap of anchor-free oriented detectors without minimizing inference speed.

Road segmentation in remote sensing images has been widely used in many fields. Semantic segmentation, based on deep learning, has become a hot topic for road segmentation. With the deepening of convolutional neural network (CNN) structures, features in the convolution layer that has more semantic information become more important for road segmentation. However, the spatial resolution of the convolutional layer reduced as the CNN network deepens, which causes the extracted roads to lose some important location information. To solve this problem, this letter proposes a novel end-to-end road segmentation method to effectively utilize the different levels of convolutional layers to enhance the model’s ability to precisely perceive road edges and shapes. The model includes an encoder and a decoder. The encoder encodes the image to obtain the features of different levels and scales. The decoder consists of two modules: scale fusion module and scale sensitive module. In the scale fusion module, features in pooling layers of different scales are fused to obtain a fusion feature. In a scale sensitive module, a weight tensor at the end of the network is learned to evaluate the importance of fusion features. This road segmentation network has been experimentally verified using public data sets, which greatly improves the road segmentation accuracy and achieves good performance.