Novel Algorithms for UAV- and Satellite-Based Hyperspectral, Multispectral, Infrared, and SAR Image Analysis

Dear Colleagues,

With the rapid development of UAV and satellite remote sensing technologies, the integrated use of hyperspectral, multispectral, infrared (IR), visible, and synthetic aperture radar (SAR) imagery has greatly improved our ability to perceive and analyze the Earth’s surface. These diverse imaging modalities capture complementary spectral, spatial, and temporal information, enabling precise characterization of materials, terrain, and targets in mountainous, forested, and landing-zone environments—areas where complex illumination, occlusion, and environmental variability pose significant challenges.

However, effectively integrating and interpreting these heterogeneous data sources remains a key scientific problem due to sensor discrepancies, spectral redundancy, and nonlinear interactions. Recent advances in deep learning, physics-informed modeling, and multimodal data fusion have paved the way for more robust and interpretable solutions in classification, unmixing, fusion, segmentation, detection, and change analysis. These advancements support a wide range of remote sensing applications, including environmental monitoring, resource mapping, precision agriculture, and defense reconnaissance, offering enhanced situational awareness in complex natural and tactical environments.

This Special Issue aims to showcase innovative algorithms, models, and practical applications for UAV- and satellite-based hyperspectral, multispectral, infrared, and SAR image analysis. The focus lies in developing novel methods for classification, unmixing, fusion, segmentation, detection, and change detection across diverse environmental conditions. In accordance with the scope of Remote Sensing, this Special Issue emphasizes cross-sensor fusion, AI-driven spectral–spatial modeling, and physics-aware learning frameworks to advance accuracy, generalization, and interpretability. It seeks to promote next-generation remote sensing approaches that bridge theory and application, enabling comprehensive environmental perception and decision support.

Suggested Themes and Article Types for Submissions

• Classification: advanced spectral–spatial algorithms for terrain and object classification using hyperspectral, multispectral, infrared, and SAR data.
• Unmixing: innovative spectral unmixing and material decomposition techniques for complex and dynamic environments.
• Fusion: multimodal fusion of hyperspectral, multispectral, infrared, SAR, LiDAR, and visible imagery for joint feature representation.
• Segmentation: high-precision segmentation and scene parsing methods based on spectral–spatial and cross-modal learning.
• Detection: robust target, anomaly, and small-object detection under challenging natural or tactical conditions.
• Change Detection: multitemporal and multimodal change analysis for environmental monitoring, disaster assessment, and urban development.
• Remote Sensing Applications: UAV–satellite collaborative sensing, vegetation and crop monitoring, geological mapping, and defense surveillance.

Dr. Minghua Wang
Dr. Xiaobin Zhao
Prof. Dr. Lianru Gao
Prof. Dr. Antonio Plaza
Guest Editors

Manuscript Submission Information

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