Feature extraction

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 …

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 …

Hyperspectral and LiDAR data fusion: outcome of the 2013 GRSS data fusion contest

The 2013 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society aimed at investigating the synergistic use of hyperspectral and Light Detection And Ranging (LiDAR) data. The data …

Semisupervised local discriminant analysis for feature extraction in hyperspectral images

We propose a novel semisupervised local discriminant analysis method for feature extraction in hyperspectral remote sensing imagery, with improved performance in both ill-posed and poor-posed conditions. The proposed method combines unsupervised …

Vehicle matching in smart camera networks using image projection profiles at multiple instances

Image projection clues for improved real-time vehicle tracking in tunnels

Vehicle tracking is of great importance for tunnel safety. To detect incidents or disturbances in traffic flow it is necessary to reliably track vehicles in real-time. The tracking is a challenging task due to poor lighting conditions in tunnels and …

Feature extraction for hyperspectral images based on semi-supervised local linear discriminant analysis

We propose a novel semi-supervised local discriminant analysis (SELD) method for feature extraction in hyperspectral remote sensing imagery. The proposed method combines a supervised method (Linear Discriminant Analysis (LDA)) and an unsupervised …

Real-time vehicle matching for multi-camera tunnel surveillance

Tracking multiple vehicles with multiple cameras is a challenging problem of great importance in tunnel surveillance. One of the main challenges is accurate vehicle matching across the cameras with non-overlapping fields of view. Since systems …

A fast iterative kernel PCA feature extraction for hyperspectral images

A fast iterative Kernel Principal Component Analysis (KPCA) is proposed to extract features from hyperspectral images. The proposed method is a kernel version of the Candid Covariance-Free Incremental Principal Component Analysis, which solves the …