WILD-SAM: Phase-Aware Expert Adaptation of SAM for Landslide Detection in Wrapped InSAR Interferograms

2026-04-15

AI Summary

Direct detection of slow-moving landslides from wrapped InSAR interferograms is challenged by phase ambiguity, coherence noise, and spectral domain shifts. To address these issues, this work proposes WILD-SAM, a parameter-efficient fine-tuning framework that integrates a phase-aware mixture-of-experts (PA-MoE) adapter into a frozen SAM encoder and employs a wavelet-guided subband enhancement (WGSE) strategy to explicitly decouple high-frequency subbands, thereby generating frequency-aware dense prompts. This approach effectively aligns the spectral distributions of natural images and interferometric phase data, significantly improving the topological completeness of landslide boundaries. Evaluated on the ISSLIDE and ISSLIDE+ benchmarks, WILD-SAM outperforms existing methods in both target completeness and contour fidelity.

Technology Category

• Computer Vision: Segmentation
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Detecting slow-moving landslides directly from wrapped Interferometric Synthetic Aperture Radar (InSAR) interferograms is crucial for efficient geohazard monitoring, yet it remains fundamentally challenged by severe phase ambiguity and complex coherence noise. While the Segment Anything Model (SAM) offers a powerful foundation for segmentation, its direct transfer to wrapped phase data is hindered by a profound spectral domain shift, which suppresses the high-frequency fringes essential for boundary delineation. To bridge this gap, we propose WILD-SAM, a novel parameter-efficient fine-tuning framework specifically designed to adapt SAM for high-precision landslide detection on wrapped interferograms. Specifically, the architecture integrates a Phase-Aware Mixture-of-Experts (PA-MoE) Adapter into the frozen encoder to align spectral distributions and introduces a Wavelet-Guided Subband Enhancement (WGSE) strategy to generate frequency-aware dense prompts. The PA-MoE Adapter exploits a dynamic routing mechanism across heterogeneous convolutional experts to adaptively aggregate multi-scale spectral-textural priors, effectively aligning the distribution discrepancy between natural images and interferometric phase data. Meanwhile, the WGSE strategy leverages discrete wavelet transforms to explicitly disentangle high-frequency subbands and refine directional phase textures, injecting these structural cues as dense prompts to ensure topological integrity along sharp landslide boundaries. Extensive experiments on the ISSLIDE and ISSLIDE+ benchmarks demonstrate that WILD-SAM achieves state-of-the-art performance, significantly outperforming existing methods in both target completeness and contour fidelity.

Problem

• landslide detection
• wrapped InSAR interferograms
• phase ambiguity
• coherence noise
• spectral domain shift

Innovation

• Phase-Aware Mixture-of-Experts
• Wavelet-Guided Subband Enhancement
• Wrapped InSAR Interferograms
• Parameter-Efficient Fine-Tuning
• Landslide Detection