Lightweight object detector is currently gaining more and more popularity in remote sensing. In general, it is hard for lightweight detectors to achieve competitive performance compared with the traditional deep models, while knowledge distillation (KD) is a promising training method to tackle the issue. Since the background is more complicated and the object size varies extremely in remote sensing images, it will deliver lots of noise and affect the training performance when directly applying the existing KD methods. To tackle the above problems, we propose an adaptive reinforcement supervision distillation (ARSD) framework to promote the detection capability of the lightweight model. First, we put forward a multiscale core features imitation (MCFI) module for transferring the knowledge of features, which can adaptively select the multiscale core features of objects for distillation and focus more on the features of small objects by an area-weighted strategy. In addition, a strict supervision regression distillation (SSRD) module is designed to select the optimal regression results for distillation, which facilitates the student to effectively imitate the more precise regression output of the teacher network. Massive experiments on a large-scale Dataset for Object deTection in Aerial images (DOTA), object DetectIon in Optical Remote sensing images (DIOR), and Northwestern Polytechnical University Very-High-Resolution 10-class (NWPU VHR) datasets prove that ARSD outperforms the existing distillation state-of-the-art (SOTA) methods. Moreover, the performance of the lightweight model trained with our method transcends other classic heavy and lightweight detectors, which beneficiates the development of lightweight models.

Boundary information is essential for the semantic segmentation of remote sensing images. However, most existing methods were designed to establish strong contextual information while losing detailed information, making it challenging to extract and recover boundaries accurately. In this paper, a boundary-sensitive network (BSNet) is proposed to address this problem via dynamic hybrid gradient convolution (DHGC) and coordinate sensitive attention (CSA). Specifically, in the feature extraction stage, we propose dynamic hybrid gradient convolution (DHGC) to replace vanilla convolution, which adaptively aggregates one vanilla convolution kernel and two gradient convolution kernels (GCKs) into a new operator to enhance boundary information extraction. The GCKs are proposed to explicitly encode boundary information, which are inspired by traditional Sobel operators. In the feature recovery stage, the coordinate sensitive attention (CSA) is introduced. This module is used to reconstruct the sharp and detailed segmentation results by adaptively modeling the boundary information and long-range dependencies in the low-level features as the assistance of high-level features. Note that DHGC and CSA are plug-and-play modules. We evaluate the proposed BSNet on three public data sets: the ISPRS 2-D semantic labeling Vaihingen, Potsdam benchmark and iSAID data set. The experimental results indicate that BSNet is a highly effective architecture that produces sharper predictions around object boundaries and significantly improves the segmentation accuracy. Our method demonstrates superior performance on the Vaihingen, Potsdam benchmark and iSAID data set, in terms of the mean F1, with improvements of 4.6%, 2.3% and 2.4% over strong baselines, respectively. The code and models will be made publicly available.

Synthetic aperture radar (SAR) ship detection plays an important role in the field of maritime security. However, certain unique imaging properties make it challenging to extract the shape features of ships, such as speckle noise and strong scattering interference from irrelevant objects. These factors result in inaccurate ship localization and obvious false alarms under complex large-scale inshore scenes. To address this issue, we propose the scattering region topology network (SRT-Net), which can dynamically capture the comprehensive global context and enhance the ship saliency. This is achieved through two key modules, namely the scattering region topological structure pyramid (SRTP) and the ship saliency enhancement (SSE) module. The former provides richer semantic information to distinguish the object from the background, while the latter offers an extra pixel-level classification task to guide accurate bounding box regression. Thanks to the guidance of richer information, the proposed method can achieve fewer false alarms and enhance location accuracy. Additionally, we introduce a scale feature adaptive (SFA) loss to balance the attention to ships with various scales, which improves the robustness of multiscale ship detection. The proposed method achieves state-of-the-art performance under complex inshore scenes, and its effectiveness is verified by experiments on a large-scale SAR ship detection dataset (LSSDD) and a public SAR ship detection dataset (SSDD+).

Object detection and recognition (OD&R) based on deep learning is a hot topic in the application of synthetic aperture radar (SAR). These methodologies based on deep learning are inherently data-driven, which means that their performance is subjected to the corresponding datasets. Although existing datasets have included some common targets collected from real-valued intensity SAR images, they still exist some limitations in terms of quantity, categories, diversities, data domain. Hence, it is urgent to establish a large-quantity benchmark for fine-grained OD&R on complex-valued SAR images, which contains rich signal-domain features well coupled with classical physical modeling. Additionally, considering the unique imaging characteristics and diverse imaging conditions, some important attribute information, such as incidence and attitude angles, is necessary to be attached. In this paper, we propose a novel benchmark dataset with more than 340k instances for Fine-grAIned object detection and Recognition based on single look Complex SAR images, which is named as FAIR-CSAR. We collected complex-valued SAR images with a resolution of 1m to 5m from 175 entire images of Gaofen-3 covering 32 cities and multiple sea areas worldwide. All instances in the FAIR-CSAR are annotated by oriented bounding boxes (OBB), covering 5 major categories and 22 subcategories. Compared with existing datasets dedicated to OD&R, the FAIR-CSAR dataset has four particular advantages: (1) it contains complex-valued SAR images from various acquisition modes and polarization modes, including full-scale signal-domain features for object recognition; (2) it is much larger than other existing OD&R datasets in terms of quantity of instances; (3) it provides more fine-grained category annotation, and more detailed attribute information; (4) it provides more challenging images with some common imaging phenomena, such as speckle noise, azimuth ambiguities and so on. To establish a baseline adapted for single look complex SAR images, a multi-domain feature extraction and fusion network (MDNet) is proposed as a novel framework to mine detailed information underlying in various domains. And a series of state-of-the-art (SOTA) algorithms are applied on the FAIR-CSAR to build the fine-grained OD&R benchmark. Experimental results indicate that FAIR-CSAR is closer to practical application and more challenging than existing datasets for SAR images.

Synthetic aperture radar (SAR) possesses significant advantages in aircraft detection due to its all-day and all-weather monitoring capability, but some unique problems in SAR images decrease the performance of aircraft detection. The speckle effect and excessive dynamic range are the most common problems that interfere with the visual features in SAR images and deteriorate detection performance. However, there lacks a detection-oriented image enhancement algorithm to collaboratively solve these two problems. An adaptive image enhancement algorithm is proposed to the improve the performance of aircraft detection in SAR images. The proposed image enhancement algorithm provides a pseudo-color image through speckle suppression and channel combination, which consists of the speckle noise suppression channel, strong scattering feature enhancement channel, and weak scattering feature enhancement channel. The speckle noise suppression is achieved by a despeckle network and the radiational feature enhancement channels are derived from an adaptive quantization method based on the characteristics of amplitude distribution. By optimizing the quality of the input image, the proposed image enhancement algorithm improves the performance of aircraft detection. Experiments based on datasets acquired by GaoFen-3 satellites indicate that the proposed algorithms significantly improve the detection performance of various types of detectors. The source project is available at https://github.com/suoyuxi/ChannelEnhancement.

Semantic segmentation of remote sensing images encounters four significant difficulties: 1) complex backgrounds, 2) large-scale differences, 3) numerous small objects, and 4) extreme foreground-background imbalance. However, the existing generic semantic segmentation models mainly focus on the modeling context information and rarely focus on these four issues. This article presents an enhanced remote sensing image semantic segmentation framework to solve these problems through the Hierarchical Atrous Pyramid module (HASP) and spatial-adaptive convolution based FPN decoder framework (SPA-FPN). On the one hand, HASP solved the problem of complex backgrounds and large-scale differences by further enlarging the receptive field of the network through the cascade of atrous convolution with various rates. On the other hand, spatial adaptive convolution is embedded in FPN decoder framework step by step to solve the problems of numerous small objects and extreme foreground-background imbalance. Besides, a boundary-based loss function is constructed to help the network optimize the relevant segmentation results. Extensive experiments over iSAID and ISPRS Vaihingen datasets reflect the superiority of the presented structure to conventional state-of-the-art semantic segmentation approaches. The code will be accessible.