TL;DR
A hybrid model termed RCL-SAM is proposed, which is built upon SAM and integrates parameter-efficient fine-tuning (PEFT) techniques, incorporating multiple innovative designs, and significantly improves the performance of SAM for single-instance cultivated land parcel segmentation in remote sensing imagery.
Frequency and Edge-guided SAM (FE-SAM) is proposed, a scalable and efficient framework for RSISS that adaptively decomposes and modulates frequency-domain features based on the input data and designs EGRefiner, which integrates multi-scale edge-enhanced information extracted from the input image.
The results demonstrate that AB-SAM provides a practical parameter-efficient framework for automated, hint-free landslide segmentation, although further evaluation across additional regions, sensors, and landslide-size distributions remains necessary.
Experimental results on the ISPRS Potsdam and Vaihingen benchmark datasets show that the proposed CNN-Transformer model attains highly competitive segmentation performance while maintaining computational efficiency.
The accurate segmentation of remote sensing imagery is critical for precision agriculture but challenging due to spectral complexity and ambiguous interclass boundaries. The convolutional neural networks are limited in modeling global context, while transformer-based methods incur high computational overhead. This lett...
Compared with several existing segmentation approaches, the proposed model delivers better overall performance in mIoU, F1-score, and recognition accuracy, particularly in scenes where multiple land-cover categories are heavily interlaced, suggesting good potential for practical deployment in land monitoring and ecolog...
These findings validate the effectiveness of combining CNNs and Transformer mechanisms in advancing automatic land use recognition and provide a promising pathway for scalable applications in large-scale remote sensing analysis.
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