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Found 14 papers, 12 papers with code
Fast Semi-supervised Unmixing using Non-convex Optimization
Our experimental results validate that enforcing the convexity constraint outperforms the sparsity prior for the endmember library.
PCB-Vision: A Multiscene RGB-Hyperspectral Benchmark Dataset of Printed Circuit Boards
Addressing the critical theme of recycling electronic waste (E-waste), this contribution is dedicated to developing advanced automated data processing pipelines as a basis for decision-making and process control.
Masking Hyperspectral Imaging Data with Pretrained Models
The presence of undesired background areas associated with potential noise and unknown spectral characteristics degrades the performance of hyperspectral data processing.
Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package
Additionally, we draw a critical comparison between advanced and conventional techniques from the three categories.
SUnAA: Sparse Unmixing using Archetypal Analysis
Unlike most conventional sparse unmixing methods, here the minimization problem is non-convex.
Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing
In this paper, we introduce a new algorithm based on archetypal analysis for blind hyperspectral unmixing, assuming linear mixing of endmembers.
HyDe: The First Open-Source, Python-Based, GPU-Accelerated Hyperspectral Denoising Package
Furthermore, we present a method for training DNNs for denoising HSIs which are not spatially related to the training dataset, i. e., training on ground-level HSIs for denoising HSIs with other perspectives including airborne, drone-borne, and space-borne.
Deep Hyperspectral Unmixing using Transformer Network
In this article, we harness the power of transformers to conquer the task of hyperspectral unmixing and propose a novel deep unmixing model with transformers.
Multimodal Fusion Transformer for Remote Sensing Image Classification
Vision transformers (ViTs) have been trending in image classification tasks due to their promising performance when compared to convolutional neural networks (CNNs).
Unsupervised Data Fusion With Deeper Perspective: A Novel Multisensor Deep Clustering Algorithm
In this article, we propose a multisensor deep clustering (MDC) algorithm for the joint processing of multisource imaging data.
Fusion of Dual Spatial Information for Hyperspectral Image Classification
In the spatial optimization stage, a pixel-level classifier is used to obtain the class probability followed by an extended random walker-based spatial optimization technique.
Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep (Overview and Toolbox)
The advances in feature extraction have been inspired by two fields of research, including the popularization of image and signal processing as well as machine (deep) learning, leading to two types of feature extraction approaches named shallow and deep techniques.
Multisource and Multitemporal Data Fusion in Remote Sensing
The sharp and recent increase in the availability of data captured by different sensors combined with their considerably heterogeneous natures poses a serious challenge for the effective and efficient processing of remotely sensed data.
Hyperspectral Subspace Identification Using SURE
However, due to spectral and spatial redundancy the true hyperspectral signal lies on a subspace of much lower dimension than the original data.
