Remote sensing

Fusion of spectral and spatial information for classification of hyperspectral remote-sensed imagery by local graph

Hyperspectral imagery contains a wealth of spectral and spatial information that can improve target detection and recognition performance. Conventional feature extraction methods cannot fully exploit both spectral and spatial information. Data fusion …

Fusion of thermal infrared hyperspectral image and visible RGB image for classification

Generalized graph-based fusion of hyperspectral and LiDAR data using morphological features

Nowadays, we have diverse sensor technologies and image processing algorithms that allow one to measure different aspects of objects on the Earth [e.g., spectral characteristics in hyperspectral images (HSIs), height in light detection and ranging …

Combining feature fusion and decision fusion for classification of hyperspectral and LiDAR data

This paper proposes a method to combine feature fusion and decision fusion together for multi-sensor data classification. First, morphological features which contain elevation and spatial information, are generated on both LiDAR data and the first …

Fusion of pixel-based and object-based features for classification of urban hyperspectral remote sensing data

Hyperspectral imagery contains a wealth of spectral and spatial information that can improve target detection and recognition performance. Typically, spectral information is inferred pixel-based, while spatial information related to texture, context …