Results 41 to 50 of about 49,813 (252)
Remote Sensing, 2019 In recent decades, in order to enhance the performance of hyperspectral image classification, the spatial information of hyperspectral image obtained by various methods has become a research hotspot. For this work, it proposes a new classification method
Jianshang Liao, Liguo Wang
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A DIVERSIFIED DEEP BELIEF NETWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 In recent years, researches in remote sensing demonstrated that deep architectures with multiple layers can potentially extract abstract and invariant features for better hyperspectral image classification. Since the usual real-world hyperspectral image
P. Zhong, Z. Q. Gong, C. Schönlieb
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Robust hyperspectral image classification with rejection fields
, 2015 In this paper we present a novel method for robust hyperspectral image classification using context and rejection. Hyperspectral image classification is generally an ill-posed image problem where pixels may belong to unknown classes, and obtaining ...
Bioucas-Dias, Jose +2 more
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Linear vs Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Image [PDF]
, 2017 As a new machine learning approach, extreme learning machine (ELM) has received wide attentions due to its good performances. However, when directly applied to the hyperspectral image (HSI) classification, the recognition rate is too low. This is because
Cao, Faxian +4 more
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Hyperspectral Image Classification [PDF]
, 2012 One objective of hyperspectral data processing is to classify collected imagery into distinct material constituents relevant to particular applications, and produce classification maps that indicate where the constituents are present. Such information products can include land-cover maps for environmental remote sensing, surface mineral maps for ...
openaire +1 more source
Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism
Remote Sensing, 2021 In recent years, image classification on hyperspectral imagery utilizing deep learning algorithms has attained good results. Thus, spurred by that finding and to further improve the deep learning classification accuracy, we propose a multi-scale residual
Yuhao Qing, Wenyi Liu
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Advanced Materials, EarlyView.AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll +19 more
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Programmable Spectrometry -- Per-pixel Classification of Materials using Learned Spectral Filters
, 2019 Many materials have distinct spectral profiles. This facilitates estimation of the material composition of a scene at each pixel by first acquiring its hyperspectral image, and subsequently filtering it using a bank of spectral profiles.
Sankaranarayanan, Aswin C. +1 more
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Two‐Dimensional Reconfigurable Photodiode for In‐Sensor Color Filtering and Spectral Logic
Advanced Materials, EarlyView.By harnessing the photodoping of different aggregates, the device exhibits wavelength‐dependent volatile‐to‐nonvolatile photoresponses that can be reconfigured via bias modulation. This enables in‐sensor color filtering and spectral‐encrypted information processing, eliminating reliance on external optical filters or post‐processing algorithms ...
Xiaokun Guo +7 more
wiley +1 more source
EVALUATING THE INITIALIZATION METHODS OF WAVELET NETWORKS FOR HYPERSPECTRAL IMAGE CLASSIFICATION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 The idea of using artificial neural network has been proven useful for hyperspectral image classification. However, the high dimensionality of hyperspectral images usually leads to the failure of constructing an effective neural network classifier.
P.-H. Hsu
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