Image denoising and classification are typically conducted separately and sequentially according to their respective objectives. In such a setup, where the two tasks are decoupled, the denoising operation does not optimally serve the classification …
Recent deep-learning-based classification models for hyperspectral images (HSIs) yield near-perfect classification accuracy on benchmark data sets. However, applying them in real scenarios often requires programming skills and machine learning …
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 …
In this paper, we first introduce a general approach for context-aware patch-based image inpainting, where textural descriptors are used to guide and accelerate the search for well-matching (candidate) patches. A novel top-down splitting procedure …
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 …
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 …
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 …
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 …
This paper presents a two-stage denoising method for hyperspectral image (HSI) by combining kernel principal component analysis (KPCA) and total variation (TV). In the first stage, we use KPCA denoising to reduce spectrally uncorrelated noise. In the …
When using morphological features for the classification of high resolution hyperspectral images from urban areas, one should consider two important issues. The first one is that classical morphological openings and closings degrade the object …