Vision-Language Multimodal State Space Network for UAV-Based Soil Moisture Change Detection

Wu K., Li Y., Fang K., ERCİŞLİ S.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2026 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1109/jstars.2026.3741947
  • Dergi Adı: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Compendex, Geobase, INSPEC, Directory of Open Access Journals, Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
  • Anahtar Kelimeler: Cross-modal fusion, multiscale features, semantics-driven, soil moisture, UAV remote sensing
  • Atatürk Üniversitesi Adresli: Evet

Özet

With the increasing application of UAV remote sensing technology in precision agriculture, the detection of changes in soil moisture content using high-resolution imagery faces challenges such as irregular patch shapes, a lack of semantic information, and difficulties in modeling long-range dependencies. To address these challenges, this paper proposes a Vision-Language Multimodal State Space Network (VLMSSNet). This method constructs a multiscale deformation-aware feature fusion module to accurately capture irregular soil boundaries and multiscale textures; introduces a vision-language crossmodal interaction module to reduce visual ambiguity using semantic priors; and designs a multipath fusion state space module to achieve efficient global long-range modeling with linear computational complexity. Experimental results demonstrate that VLM-SSNet performs exceptionally well in detecting changes in soil moisture content, achieving an F1-score of 90.92% and a mIoU of 80.14%, representing significant improvements over mainstream methods. Furthermore, the model exhibits excellent generalization capabilities under complex resolution conditions and in the presence of noise interference, providing reliable technical support for the precise monitoring of farmland.