remote sensing

A structural subspace clustering approach for hyperspectral band selection

Band selection, which removes irrelevant bands from hyperspectral images (HSIs) and keeps essential spectral information contained in a relatively few bands, allows huge savings in data storage, computation time, and imaging hardware. In this …

Heterogeneous regularization-based tensor subspace clustering for hyperspectral band selection

Band selection (BS) reduces effectively the spectral dimension of a hyperspectral image (HSI) by selecting relatively few representative bands, which allows efficient processing in subsequent tasks. Existing unsupervised BS methods based on subspace …

Hybrid-hypergraph regularized multiview subspace clustering for hyperspectral images

Clustering algorithms play an essential and unique role in classification tasks, especially when annotated data are unavailable or very scarce. Current clustering approaches in remote sensing are mostly designed for a single data source, such as …

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 …