most citedIllumination invariant hyperspectral image unmixing based on a digital surface model

29 citations · 45 across the 4 of their papers we have counts for

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5 papers · 1 filter

cs.CV2020

Unmixing Convolutional Features for Crisp Edge Detection

Linxi Huan, Nan Xue, Xianwei Zheng +3

This paper presents a context-aware tracing strategy (CATS) for crisp edge detection with deep edge detectors, based on an observation that the localization ambiguity of deep edge…

cs.CV202029 cited

Illumination invariant hyperspectral image unmixing based on a digital surface model

Tatsumi Uezato, Naoto Yokoya, Wei He

Although many spectral unmixing models have been developed to address spectral variability caused by variable incident illuminations, the mechanism of the spectral variability is s…

cs.CV20203 cited

Breaking the Limits of Remote Sensing by Simulation and Deep Learning for Flood and Debris Flow Mapping

Naoto Yokoya, Kazuki Yamanoi, Wei He +4

We propose a framework that estimates inundation depth (maximum water level) and debris-flow-induced topographic deformation from remote sensing imagery by integrating deep learnin…

cs.CV2018

Non-local Meets Global: An Integrated Paradigm for Hyperspectral Denoising

Wei He, Quanming Yao, Chao Li +2

Non-local low-rank tensor approximation has been developed as a state-of-the-art method for hyperspectral image (HSI) denoising. Unfortunately, with more spectral bands for HSI, wh…

cs.CV2018

Multi-temporal Sentinel-1 and -2 Data Fusion for Optical Image Simulation

Wei He, Naoto Yokoya

In this paper, we present the optical image simulation from a synthetic aperture radar (SAR) data using deep learning based methods. Two models, i.e., optical image simulation dire…