A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Ladder Networks improve semi-supervised hyperspectral image classification.
Active deep learning classification of hyperspectral images is considered in this paper. Deep learning has achieved success in many applications, but good-quality labeled samples are needed to construct a deep learning network. It is expensive getting good labeled samples in hyperspectral images for remote sensing appl…
Paper presents a graph-based semi-supervised method for hyperspectral image classification.
SaR-SVM-STV improves hyperspectral image classification with shape-adaptive reconstruction and denoising.
Novel graph-based framework for hyperspectral image classification using superpixels.
Deep learning for Venus images uses high-res hyperspectral data to simulate ground truth.
MDGCN improves hyperspectral image classification by dynamically updating graphs.
A novel framework combines deep metric learning and conditional random field for hyperspectral image classification.
JigsawHSI improves land-use classification using hyperspectral images.
Attention-based CNNs improve band selection in hyperspectral images.
A 3-stage method enhances hyperspectral image classification accuracy.
Herein, we present a system for hyperspectral image segmentation that utilizes multiple class--based denoising autoencoders which are efficiently trained. Moreover, we present a novel hyperspectral data augmentation method for labelled HSI data using linear mixtures of pixels from each class, which helps the system wit…
This paper improves land cover classification using global spatial features in CNN.
MPRI learns multiscale features for HSI classification.
Nowadays, hyperspectral image classification widely copes with spatial information to improve accuracy. One of the most popular way to integrate such information is to extract hierarchical features from a multiscale segmentation. In the classification context, the extracted features are commonly concatenated into a lon…
A new method classifies hyperspectral images using dynamic graph convolutional networks.
A new model combines spatial and spectral features for HSI classification.
Dictionary learning algorithms have been successfully used in both reconstructive and discriminative tasks, where the input signal is represented by a linear combination of a few dictionary atoms. While these methods are usually developed under sparsity constrain (prior) in the input domain, recent studies hav…
In this paper, we tackle the question of discovering an effective set of spatial filters to solve hyperspectral classification problems. Instead of fixing a priori the filters and their parameters using expert knowledge, we let the model find them within random draws in the (possibly infinite) space of possible filters…
In this paper, we propose a method using a three dimensional convolutional neural network (3-D-CNN) to fuse together multispectral (MS) and hyperspectral (HS) images to obtain a high resolution hyperspectral image. Dimensionality reduction of the hyperspectral image is performed prior to fusion in order to significantl…
We propose a novel approach for pixel classification in hyperspectral images, leveraging on both the spatial and spectral information in the data. The introduced method relies on a recently proposed framework for learning on distributions -- by representing them with mean elements in reproducing kernel Hilbert spaces (…
New algorithm clusters hyperspectral images at multiple scales.
This work presents a method to localize targets in hyperspectral images using robust PCA.
A deep clustering method for hyperspectral images improves clustering performance by constraining intra-class distances.
The paper classifies U.S. crop types using hyperspectral satellite imagery.
Proposes CMP method for reducing tensor object dimensions in binary classification.
A new DR method for HSI classification improves accuracy with limited samples.
Pixel-wise classification, where each pixel is assigned to a predefined class, is one of the most important procedures in hyperspectral image (HSI) analysis. By representing a test pixel as a linear combination of a small subset of labeled pixels, a sparse representation classifier (SRC) gives rather plausible results …
In this article we give our contribution to the problem of segmentation with plug-in procedures. We give general sufficient conditions under which plug in procedure are efficient. We also give an algorithm that satisfy these conditions. We give an application of the used algorithm to hyperspectral images segmentation. …
A new method classifies high-dimensional images with minimal labels using diffusion geometry.
Flexible band grouping and kernel fusion for hyperspectral image processing.
The lack of proper class discrimination among the Hyperspectral (HS) data points poses a potential challenge in HS classification. To address this issue, this paper proposes an optimal geometry-aware transformation for enhancing the classification accuracy. The underlying idea of this method is to obtain a linear proje…
Proposes a DR method for HSI classification with limited labeled data.
A new method clusters hyperspectral images using spatially regularized diffusion.
In hyperspectral images, some spectral bands suffer from low signal-to-noise ratio due to noisy acquisition and atmospheric effects, thus requiring robust techniques for the unmixing problem. This paper presents a robust supervised spectral unmixing approach for hyperspectral images. The robustness is achieved by writi…
The paper tackles HS target localization using robust PCA with dictionary-based approach.
When considering the problem of unmixing hyperspectral images, most of the literature in the geoscience and image processing areas relies on the widely used linear mixing model (LMM). However, the LMM may be not valid and other nonlinear models need to be considered, for instance, when there are multi-scattering effect…
Dimensionality reduction is an important step in processing the hyperspectral images (HSI) to overcome the curse of dimensionality problem. Linear dimensionality reduction methods such as Independent component analysis (ICA) and Linear discriminant analysis (LDA) are commonly employed to reduce the dimensionality of HS…
This paper addresses the problem of blind and fully constrained unmixing of hyperspectral images. Unmixing is performed without the use of any dictionary, and assumes that the number of constituent materials in the scene and their spectral signatures are unknown. The estimated abundances satisfy the desired sum-to-one …
Paper proposes ONTD for nonnegative tensor data.
The nonnegative matrix factorization (NMF) is widely used in signal and image processing, including bio-informatics, blind source separation and hyperspectral image analysis in remote sensing. A great challenge arises when dealing with a nonlinear formulation of the NMF. Within the framework of kernel machines, the mod…
Optimal clustering framework selects bands for hyperspectral images.
Unified analysis for robust PCA with applications to target localization in HS images.
The paper classifies soil texture using 1D CNNs on hyperspectral data.
Incorporating spatial information into hyperspectral unmixing procedures has been shown to have positive effects, due to the inherent spatial-spectral duality in hyperspectral scenes. Current research works that consider spatial information are mainly focused on the linear mixing model. In this paper, we investigate a …
This paper presents an unsupervised algorithm for nonlinear unmixing of hyperspectral images. The proposed model assumes that the pixel reflectances result from a nonlinear function of the abundance vectors associated with the pure spectral components. We assume that the spectral signatures of the pure components and t…
New methods detect targets from imprecisely labeled hyperspectral data.
Within a supervised classification framework, labeled data are used to learn classifier parameters. Prior to that, it is generally required to perform dimensionality reduction via feature extraction. These preprocessing steps have motivated numerous research works aiming at recovering latent variables in an unsupervise…