Results 1 to 10 of about 1,244,965 (221)

The AMEE-PPI Method to Extract Typical Outcrop Endmembers from GF-5 Hyperspectral Images [PDF]

Sensors
Mixed pixels remain a central obstacle to reliable endmember extraction from hyperspectral imagery. We present AMEE–PPI, a hybrid method that embeds the Pure Pixel Index (PPI) within morphological structuring elements and propagates spectral purity via ...
Lin Hu, Jiankai Hu, Shu Gan, Xiping Yuan, Yu Lu, Hailong Zhao, Guang Han   +6 more
doaj   +2 more sources

The Successive Projection Algorithm (SPA), an Algorithm with a Spatial Constraint for the Automatic Search of Endmembers in Hyperspectral Data

Sensors, 2008
Spectral mixing is a problem inherent to remote sensing data and results in fewimage pixel spectra representing "pure" targets. Linear spectral mixture analysis isdesigned to address this problem and it assumes that the pixel-to-pixel variability in ...
D. M. Rogge, Benoit Rivard, Jinkai Zhang
doaj  

Hyperspectral Unmixing Using Frequency-Adaptive Convolutional-Mamba Network

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
In recent years, deep learning (DL) has achieved remarkable progress in hyperspectral unmixing (HU) owing to its powerful feature extraction and modeling capabilities.
Zhuoyi Zhao, Xiang Xu, Chuiyi Deng, Yanyin Guo, Junwei Li, Antonio Plaza   +5 more
doaj   +1 more source

A Phenological Approach to Spectral Differentiation of Low-Arctic Tundra Vegetation Communities, North Slope, Alaska

Remote Sensing, 2017
Arctic tundra ecosystems exhibit small-scale variations in species composition, micro-topography as well as significant spatial and temporal variations in moisture.
Alison Leslie Beamish, Nicholas Coops, Sabine Chabrillat, Birgit Heim   +3 more
doaj   +1 more source

Hyperspectral and Multispectral Image Fusion with Automated Extraction of Image-Based Endmember Bundles and Sparsity-Based Unmixing to Deal with Spectral Variability [PDF]

, 2023
Salah Eddine Brezini, Yannick Deville
openalex   +1 more source

Generative Simplex Mapping: Non-Linear Endmember Extraction and Spectral Unmixing for Hyperspectral Imagery

Remote Sensing
We introduce a new model for non-linear endmember extraction and spectral unmixing of hyperspectral imagery called Generative Simplex Mapping (GSM).
John Waczak, David J. Lary
doaj   +1 more source

Geometric Abundance Estimation Using Variable Endmembers for Hyperspectral Imagery

International Journal of Hybrid Information Technology, 2014
Abundance estimation is an important step of quantitative analysis of hyperspectral remote sensing data. Due to physical interpretation, sum-to-one and non-negativity constraints are generally imposed on the abundances of materials. This paper presents a geometric approach to fully constrained linear spectral unmixing using variable endmember sets for ...
Huadong Yang, Jubai An
openaire   +1 more source

Soil Moisture Content Estimation From Hyperspectral Remote Sensing Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Because of its significant absorption power, especially in the shortwave infrared optical region, water dominates the optical reflectance properties of water-bearing materials.
Ketaki Vinay Jambhali, Bikram Koirala, Zakaria Bnoulkacem, Paul Scheunders   +3 more
doaj   +1 more source

A Novel Change Detection Approach Based on Spectral Unmixing from Stacked Multitemporal Remote Sensing Images with a Variability of Endmembers [PDF]

, 2021
Ke Wu, Tao Chen, Ying Xu, Dongwei Song, Haishan Li   +4 more
openalex   +1 more source

Hyperspectral Unmixing With Multi-Scale Convolution Attention Network

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral unmixing is to decompose the mixed pixel into the spectral signatures (endmembers) with their corresponding abundances. However, the ignorance of endmember variability in hyperspectral unmixing results in low performance.
Sheng Hu, Huali Li
doaj   +1 more source