Salient object detection in optical remote sensing images (RSI-SOD) has recently become a key area of research, driven by the unique challenges posed by the variability in remote sensing imagery. Traditional approaches, largely based on Convolutional Neural Networks (CNNs), are limited in handling the diverse scenarios of remote sensing due to their static network construction and reliance on local feature extraction. To tackle these limitations, we present the Adaptive Semantic Network (ASNet), a novel framework specifically designed for RSI-SOD. ASNet innovatively integrates Transformer and CNN technologies in a Dual Branch Encoder, which captures both global dependencies and local fine-grained image details. The network also features an Adaptive Semantic Matching Module (ASMM) for dynamically harmonizing filter responses to global and local contexts, an Adaptive Feature Enhancement Module (AFEM) that effectively enhances salient region features while restoring image resolution, and a Multi-scale Fine-grained Inference Module (MFIM) which refines high-level semantic features by integrating detailed low-level information, leading to the generation of precise, high-quality saliency maps. These components work in concert to adaptively respond to the complex nature of remote sensing images. Extensive experimental evaluations confirm that ASNet substantially outperforms existing models in the RSI-SOD task.

Optical remote sensing images (ORSI) have various applications in different fields, and salient target detection (ORSI-SOD) of ORSI has become an important research topic in recent years. However, ORSI-SOD is a challenging problem due to the variable and complex backgrounds, large differences in levels, mixed backgrounds, and diverse topological shapes of ORSI. In this paper, we propose a novel model called multi-scale feature refinement aggregation network (MFANet), which consists of a multi-scale feature refinement module (MFR) and a context aggregation module (CFA). The MFR module extracts semantic information of ORSI across different dimensions in the multi-scale feature extraction stage. In the feature refinement stage, we use the proposed self-refinement module under the guidance of attention and reverse attention to progressively refine the prediction results. The CFA module introduces the hybrid attention module to gradually aggregate and extract salient regions from the context extraction module. To adapt to dense scenes, we develop a hybrid loss function that enables network optimization of multi-scale objectives in a self-adaptive manner. Our method outperforms most state-of-the-art salient object detection methods proposed in recent years in terms of accuracy.

Salient object detection in optical remote sensing images (RSI-SOD) is critical in remote sensing, yet it faces challenges such as dependency on intensive pixel-level annotations and limited research on low-cost, weakly supervised methods. These challenges are compounded by difficulties in handling complex backgrounds and varying salient object features with existing CNN-based methods. We introduce the Global-Local Semantic Interaction Network (GLSIN), a high-performance, cost-effective RSI-SOD approach based on scribble supervision. GLSIN employs an encoder-decoder framework, blending a Transformer and CNN to create a Dual Branch Encoder that effectively captures both global and local features of images. The Global-Local Affinity Block (GLAB) and Feature Shrinkage Decoder with the Global-Local Fusion Block (GLFB) are integrated to enhance feature interaction and precision in saliency map generation. Experimental results on two public datasets show that our method achieves F ^max _β , E ^max _ξ , S _α , and M scores of 86.6%, 96.5%, 91.8%, and 0.7% on the EORSSD dataset, and 90.1%, 97.2%, 91.7%, and 1.1% on the ORSSD dataset, respectively. The performance surpasses existing weakly-supervised or unsupervised SOD methods and even some fully-supervised models.