Synthetic aperture radar (SAR) is an important means to detect ocean oil spills which cause serious damage to the marine ecosystem. However, the look-alikes, which have a similar behavior to oil slicks in SAR images, will reduce the oil spill detection accuracy. Therefore, a novel oil spill detection method based on multiple features of polarimetric SAR data is proposed to improve the detection accuracy in this paper. In this method, the self-similarity parameter, which is sensitive to the randomness of the scattering target, is introduced to enhance the discrimination ability between oil slicks and look-alikes. The proposed method uses the Random Forest classification combing self-similarity parameter with seven well-known features to improve oil spill detection accuracy. Evaluations and comparisons were conducted with Radarsat-2 and UAVSAR polarimetric SAR datasets, which shows that: (1) the oil spill detection accuracy of the proposed method reaches 92.99% and 82.25% in two datasets, respectively, which is higher than three well-known methods. (2) Compared with other seven polarimetric features, self-similarity parameter has the better oil spill detection capability in the scene with lower wind speed close to 2–3 m/s, while, when the wind speed is close to 9–12 m/s, it is more suitable for oil spill detection in the downwind scene where the microwave incident direction is similar to the sea surface wind direction and performs well in the scene with incidence angle range from 29.7° to 43.5°.

Change detection is an important method of analyzing information about changes in geographical features. However, existing deep learning feature difference methods often lead to the loss of detailed information. Differences in features can arise from factors like illumination or geometric variations rather than actual change regions, resulting in inaccurate change detection. This leads to poor detection of fine-grained boundaries and internal hole problems. To alleviate this, we propose a novel change detection network guided by change boundary awareness and incorporating the concept of boundary-to-center. Our network introduces a change boundary-aware module (CBM) to capture boundary information of change regions. This module enhances boundaries, reducing the influence of noise in feature differences and providing rich contextual information to improve the accuracy of change boundaries. Additionally, we propose a bi-temporal feature aggregation module (BFAM) based on spatial-temporal features. The BFAM aggregates multiple receptive fields features and complements texture information. Both modules utilize the SimAM attention mechanism to enhance the fine-grained nature of the features. In addition, we introduce a deep feature extraction module (DFEM) to extract deep features and minimize information loss during the decoupling process. The proposed change detection network in this paper is guided by change boundary perception, progressively integrating semantic and spatial texture information to refine edges and enhance internal integrity. The performance and efficiency of B2CNet have been validated on four publicly available remote sensing image change detection datasets. Through extensive experiments, the effectiveness of the proposed method has been demonstrated. For example, in terms of IOU for LEVIR, WHU, SYSU and HRCUS datasets, the improvements compared to the baseline are 1.89%, 2.86%, 4.70% and 3.79%, respectively. The code of the proposed approach can be found at https://github.com/bao11seven/B2CNet.

With the advent of very-high-resolution remote sensing images, semantic change detection (SCD) based on deep learning has become a research hotspot in recent years. SCD aims to observe the change in the Earth’s land surface and plays a vital role in monitoring the ecological environment, land use and land cover. Existing research mainly focus on single-task semantic change detection; the problem they face is that existing methods are incapable of identifying which change type has occurred in each multi-temporal image. In addition, few methods use the binary change region to help train a deep SCD-based network. Hence, we propose a dual-task semantic change detection network (GCF-SCD-Net) by using the generative change field (GCF) module to locate and segment the change region; what is more, the proposed network is end-to-end trainable. In the meantime, because of the influence of the imbalance label, we propose a separable loss function to alleviate the over-fitting problem. Extensive experiments are conducted in this work to validate the performance of our method. Finally, our work achieves a 69.9% mIoU and 17.9 Sek on the SECOND dataset. Compared with traditional networks, GCF-SCD-Net achieves the best results and promising performances.

Target detection plays a crucial role in the intelligent interpretation of remote sensing images and has a wide range of potential applications. However, in the presence of targets with high aspect ratios and significant scale variations in remotely sensed images, existing methods prefer CNN or transformer architectures but suffer from the limitations of overly fixed receptive fields or excessive computational complexity. Recently, Mamba-based methods have become hot in the field of target detection and show significant potential in capturing remote dependencies with linear complexity but lack in-depth customization for remote sensing targets. To address the above challenges, we propose a new target detection framework for complex remote sensing images, LS-MambaNet. Specifically, firstly, a group fusion strategy is combined with the introduction of large-band convolution to adaptively adjust the receptive domains of the features, which enhances the spatial context information extraction for objects with high aspect ratios. In addition, a Multi-Granularity Spatial Mamba Block is proposed, and this employs a multi-granularity scanning strategy to reduce the computational cost and feature redundancy on different scanning paths and is able to efficiently model the global contextual information of the target. Experimental results show that LS-MambaNet outperforms baselines on DOTA1.0 and HRSC2016 datasets. In particular, LS-MambaNet significantly improves the inference speed and achieves a higher FPS while maintaining state-of-the-art detection accuracy.

Land cover classification is a multiclass segmentation task to classify each pixel into a certain natural or human-made category of the earth’s surface, such as water, soil, natural vegetation, crops, and human infrastructure. Limited by hardware computational resources and memory capacity, most existing studies preprocessed original remote sensing images by downsampling or cropping them into small patches less than 512 × 512 pixels before sending them to a deep neural network. However, downsampling incurs a spatial detail loss, renders small segments hard to discriminate, and reverses the spatial resolution progress obtained by decades of efforts. Cropping images into small patches causes a loss of long-range context information, and restoring the predicted results to their original size brings extra latency. In response to the above weaknesses, we present an efficient lightweight semantic segmentation network termed MKANet. Aimed at the characteristics of top view high-resolution remote sensing imagery, MKANet utilizes sharing kernels to simultaneously and equally handle ground segments of inconsistent scales, and also employs a parallel and shallow architecture to boost inference speed and friendly support image patches more than 10× larger. To enhance boundary and small segment discrimination, we also propose a method that captures category impurity areas, exploits boundary information, and exerts an extra penalty on boundaries and small segment misjudgments. Both visual interpretations and quantitative metrics of extensive experiments demonstrate that MKANet obtains a state-of-the-art accuracy on two land-cover classification datasets and infers 2× faster than other competitive lightweight networks. All these merits highlight the potential of MKANet in practical applications.

Since the data generation rate of high-resolution satellites is increasing rapidly, to relieve the stress of data downloading and processing systems while enhancing the time efficiency of information acquisition, it is important to deploy on-board edge computing on satellites. However, the volume, weight, and computability of on-board systems are strictly limited by the harsh space environment. Therefore, it is very difficult to match the computability and the requirements of diversified intelligent applications. Currently, this problem has become the first challenge of the practical deployment of on-board edge computing. To match the actual requirements of the Luojia3 satellite of Wuhan University, this manuscript proposes a three-level edge computing architecture based on a System-on-Chip (SoC) for low power consumption and expandable on-board processing. First, a transfer level is designed to focus on hardware communications and Input/Output (I/O) works while maintaining a buffer to store image data for upper levels temporarily. Second, a processing framework that contains a series of libraries and Application Programming Interfaces (APIs) is designed for the algorithms to easily build parallel processing applications. Finally, an expandable level contains multiple intelligent remote sensing applications that perform data processing efficiently using base functions, such as instant geographic locating and data picking, stream computing balance model, and heterogeneous parallel processing strategy that are provided by the architecture. It is validated by the performance improvement experiment that following this architecture, using these base functions can help the Region of Interest (ROI) system geometric correction fusion algorithm to be 257.6 times faster than the traditional method that processes scene by scene. In the stream computing balance experiment, relying on this architecture, the time-consuming algorithm ROI stabilization production can maintain stream computing balance under the condition of insufficient computability. We predict that based on this architecture, with the continuous development of device computability, the future requirements of on-board computing could be better matched.