A Lightweight Multistream Framework for Salient Object Detection in Optical Remote Sensing

Salient object detection (SOD) in optical remote sensing images (ORSIs) is challenging due to small object sizes, low contrast, and complex backgrounds. Existing methods often rely on computationally intensive architectures, limiting their efficiency in real-world applications. To address this, we propose LiteSalNet, a lightweight deep learning framework for ORSI SOD. LiteSalNet employs MobileNetV2 as a compact encoder and enhances multiscale feature representation through three modules: the adaptive spatial attention module (ASAM) for spatial attention (SA) refinement, the dual-scale feature enhancement module (DSFEM) for local-global feature integration, and the semantic context enhancement module (SCEM) for high-level semantic refinement. Additionally, a multistream progressively decoding framework (MSPDF) is introduced to decode saliency, edge, and skeleton maps in a supervised manner, improving boundary precision, suppressing background noise, and enhancing internal object consistency. Extensive experiments on two benchmark ORSI datasets demonstrate that LiteSalNet outperforms 19 state-of-the-art (SOTA) models across multiple evaluation metrics, including F-measure (F-m), S-measure, E-measure, and mean absolute error (MAE). Notably, LiteSalNet achieves these results with only 3.90 M parameters and 7.35 G floating-point operations per second (FLOPs), ensuring high computational efficiency. The code and results are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ai-kunkun/LiteSalNet</uri>.