scholarly journals SUBPIXEL MAPPING OF HYPERSPECTRAL IMAGE BASED ON LINEAR SUBPIXEL FEATURE DETECTION AND OBJECT OPTIMIZATION

2018 ◽  
Vol IV-3 ◽  
pp. 161-165
Author(s):  
Zhaoxin Liu ◽  
Liaoying Zhao ◽  
Xiaorun Li ◽  
Shuhan Chen
Keyword(s):  
Template Matching ◽  
Feature Detection ◽  
Hyperspectral Image ◽  
Mapping Method ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Binary Particle Swarm Optimization ◽  
Mixed Pixels ◽  
Subpixel Mapping ◽  
Treat All

Owing to the limitation of spatial resolution of the imaging sensor and the variability of ground surfaces, mixed pixels are widesperead in hyperspectral imagery. The traditional subpixel mapping algorithms treat all mixed pixels as boundary-mixed pixels while ignoring the existence of linear subpixels. To solve this question, this paper proposed a new subpixel mapping method based on linear subpixel feature detection and object optimization. Firstly, the fraction value of each class is obtained by spectral unmixing. Secondly, the linear subpixel features are pre-determined based on the hyperspectral characteristics and the linear subpixel feature; the remaining mixed pixels are detected based on maximum linearization index analysis. The classes of linear subpixels are determined by using template matching method. Finally, the whole subpixel mapping results are iteratively optimized by binary particle swarm optimization algorithm. The performance of the proposed subpixel mapping method is evaluated via experiments based on simulated and real hyperspectral data sets. The experimental results demonstrate that the proposed method can improve the accuracy of subpixel mapping.

scholarly journals Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska

2021 ◽  
Vol 13 (9) ◽  
pp. 1693
Author(s):  
Anushree Badola ◽  
Santosh K. Panda ◽  
Dar A. Roberts ◽  
Christine F. Waigl ◽  
Uma S. Bhatt ◽  
...  
Keyword(s):  
Land Cover ◽  
Spectral Resolution ◽  
Hyperspectral Image ◽  
Spectral Response ◽  
Spectral Characteristics ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
High Spectral Resolution ◽  
Fuel Mapping ◽  
Sentinel 2

Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest.

scholarly journals Combining Spectral Unmixing and 3D/2D Dense Networks with Early-Exiting Strategy for Hyperspectral Image Classification

2020 ◽  
Vol 12 (5) ◽  
pp. 779 ◽  
Author(s):  
Bei Fang ◽  
Yunpeng Bai ◽  
Ying Li
Keyword(s):  
Deep Learning ◽  
Network Performance ◽  
Hyperspectral Image ◽  
Spectral Unmixing ◽  
Classification Methods ◽  
Dense Network ◽  
Training Samples ◽  
Mixed Pixels ◽  
Benchmark Datasets ◽  
Hard Samples

Recently, Hyperspectral Image (HSI) classification methods based on deep learning models have shown encouraging performance. However, the limited numbers of training samples, as well as the mixed pixels due to low spatial resolution, have become major obstacles for HSI classification. To tackle these problems, we propose a resource-efficient HSI classification framework which introduces adaptive spectral unmixing into a 3D/2D dense network with early-exiting strategy. More specifically, on one hand, our framework uses a cascade of intermediate classifiers throughout the 3D/2D dense network that is trained end-to-end. The proposed 3D/2D dense network that integrates 3D convolutions with 2D convolutions is more capable of handling spectral-spatial features, while containing fewer parameters compared with the conventional 3D convolutions, and further boosts the network performance with limited training samples. On another hand, considering the existence of mixed pixels in HSI data, the pixels in HSI classification are divided into hard samples and easy samples. With the early-exiting strategy in these intermediate classifiers, the average accuracy can be improved by reducing the amount of computation cost for easy samples, thus focusing on classifying hard samples. Furthermore, for hard samples, an adaptive spectral unmixing method is proposed as a complementary source of information for classification, which brings considerable benefits to the final performance. Experimental results on four HSI benchmark datasets demonstrate that the proposed method can achieve better performance than state-of-the-art deep learning-based methods and other traditional HSI classification methods.

scholarly journals Subpixel Mapping Method of Hyperspectral Images Based on Modified Binary Quantum Particle Swarm Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Shuhan Chen ◽  
Xiaorun Li ◽  
Liaoying Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Mapping Method ◽  
Spectral Unmixing ◽  
Optimization Strategy ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization ◽  
Subpixel Mapping

Subpixel mapping technology can determine the specific location of different objects in the mixed pixel and effectively solve the uncertainty of the ground features spatial distribution in traditional classification technology. Existing methods based on linear optimization encounter the premature and local convergence of the optimization algorithm. This paper proposes a subpixel mapping method based on modified binary quantum particle swarm optimization (MBQPSO) to solve the above issues. The initial subpixel mapping imagery is obtained according to spectral unmixing results. We focus mainly on the discretization of QPSO, which is implemented by modifying the discrete update process of particle location, to minimize the objective function, which is formulated based on different connected regional perimeter calculating methods. To reduce time complexity, a target optimization strategy of global iteration combined with local iteration is performed. The MBQPSO is tested on standard test functions and results show that MBQPSO has the best performance on global optimization and convergent rate. Then, we analyze the proposed algorithm qualitatively and quantitatively by simulated and real experiment; results show that the method combined with MBQPSO and objective function, which is formulated based on the gap length between region and background, has the best performance in accuracy and efficiency.

scholarly journals Subpixel Mapping Algorithms Based on Block Structural Self-Similarity Learning

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Liwei Chen ◽  
Tieshen Wang ◽  
Haifeng Zhu
Keyword(s):  
Spatial Distribution ◽  
Block Structure ◽  
Mapping Method ◽  
Similarity Learning ◽  
Remote Sensing Images ◽  
Self Similarity ◽  
Image Block ◽  
Mapping Algorithms ◽  
Mixed Pixels ◽  
Subpixel Mapping

Subpixel mapping (SPM) algorithms effectively estimate the spatial distribution of different land cover classes within mixed pixels. This paper proposed a new subpixel mapping method based on image structural self-similarity learning. Image structure self-similarity refers to similar structures within the same scale or different scales in image itself or its downsampled image, which widely exists in remote sensing images. Based on the similarity of image block structure, the proposed method estimates higher spatial distribution of coarse-resolution fraction images and realizes subpixel mapping. The experimental results show that the proposed method is more accurate than existing fast subpixel mapping algorithms.

scholarly journals Low-Rank and Spectral-Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Fan Li
Keyword(s):  
Hyperspectral Image ◽  
Structural Information ◽  
Real Data ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Low Rank ◽  
Superior Performance ◽  
Dimensional Structure ◽  
Spatial Continuity ◽  
Spatial Weights

Sparse unmixing is an important technique for hyperspectral data analysis. Most sparse unmixing algorithms underutilize the spatial and spectral information of the hyperspectral image, which is unfavourable for the accuracy of endmember identification and abundance estimation. We propose a new spectral unmixing method based on the low-rank representation model and spatial-weighted collaborative sparsity, aiming to exploit structural information in both the spatial and spectral domains for unmixing. The spatial weights are incorporated into the collaborative sparse regularization term to enhance the spatial continuity of the image. Meanwhile, the global low-rank constraint is employed to maintain the spatial low-dimensional structure of the image. The model is solved by the well-known alternating direction method of multiplier, in which the abundance coefficients and the spatial weights are updated iteratively in the inner and outer loops, respectively. Experimental results obtained from simulation and real data reveal the superior performance of the proposed algorithm on spectral unmixing.

scholarly journals Spectral Unmixing of Hyperspectral Remote Sensing Imagery via Preserving the Intrinsic Structure Invariant

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3528 ◽  
Author(s):  
Yang Shao ◽  
Jinhui Lan ◽  
Yuzhen Zhang ◽  
Jinlin Zou
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Image Data ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Intrinsic Structure ◽  
Close Link ◽  
Manifold Structure ◽  
Data Space ◽  
Projection Equation

Hyperspectral unmixing, which decomposes mixed pixels into endmembers and corresponding abundance maps of endmembers, has obtained much attention in recent decades. Most spectral unmixing algorithms based on non-negative matrix factorization (NMF) do not explore the intrinsic manifold structure of hyperspectral data space. Studies have proven image data is smooth along the intrinsic manifold structure. Thus, this paper explores the intrinsic manifold structure of hyperspectral data space and introduces manifold learning into NMF for spectral unmixing. Firstly, a novel projection equation is employed to model the intrinsic structure of hyperspectral image preserving spectral information and spatial information of hyperspectral image. Then, a graph regularizer which establishes a close link between hyperspectral image and abundance matrix is introduced in the proposed method to keep intrinsic structure invariant in spectral unmixing. In this way, decomposed abundance matrix is able to preserve the true abundance intrinsic structure, which leads to a more desired spectral unmixing performance. At last, the experimental results including the spectral angle distance and the root mean square error on synthetic and real hyperspectral data prove the superiority of the proposed method over the previous methods.

scholarly journals How Hyperspectral Image Unmixing and Denoising Can Boost Each Other

2020 ◽  
Vol 12 (11) ◽  
pp. 1728
Author(s):  
Behnood Rasti ◽  
Bikram Koirala ◽  
Paul Scheunders ◽  
Pedram Ghamisi
Keyword(s):  
Noise Reduction ◽  
Hyperspectral Image ◽  
Optimization Criterion ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Low Rank ◽  
Hyperspectral Unmixing ◽  
Selection Strategies ◽  
Linear Unmixing ◽  
The One

Hyperspectral linear unmixing and denoising are highly related hyperspectral image (HSI) analysis tasks. In particular, with the assumption of Gaussian noise, the linear model assumed for the HSI in the case of low-rank denoising is often the same as the one used in HSI unmixing. However, the optimization criterion and the assumptions on the constraints are different. Additionally, noise reduction as a preprocessing step in hyperspectral data analysis is often ignored. The main goal of this paper is to study experimentally the influence of noise on the process of hyperspectral unmixing by: (1) investigating the effect of noise reduction as a preprocessing step on the performance of hyperspectral unmixing; (2) studying the relation between noise and different endmember selection strategies; (3) investigating the performance of HSI unmixing as an HSI denoiser; (4) comparing the denoising performance of spectral unmixing, state-of-the-art HSI denoising techniques, and the combination of both. All experiments are performed on simulated and real datasets.

scholarly journals Application of Spectral Unmixing on Hyperspectral Data of the Historic Volcanic Products of Mt. Etna (Italy)

Proceedings ◽  
2018 ◽  
Vol 2 (7) ◽  
pp. 329 ◽  
Author(s):  
Vasiliki Daskalopoulou ◽  
Olga Sykioti ◽  
Catherine Karagiannopoulou ◽  
Konstantinos Koutroumbas ◽  
Athanasios Rontogiannis
Keyword(s):  
Similarity Index ◽  
Structural Similarity ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Use Of Time ◽  
Flow Characterization ◽  
Mt Etna ◽  
Mixed Pixels ◽  
The Fourier Transform ◽  
Mixing Problem

Considering that ground areas with intense compositional variability appear as mixed pixels in hyperspectral data, we focus on the mixing problem imposed by the various volcanic products found in the vicinity of Mt. Etna’s volcanic craters. Mt. Etna, which is one of the most active volcanoes globally, is a generator of diverse mineralogical environments. Therefore, the inherent abundant information of hyperspectral imagery of the volcanic edifice calls for the use of time-efficient and accurate spectral unmixing methods in order to unravel the data. Lava flows (LFs) and related products from the historical 1536–1669 era were selected based on their distinct spatial distribution and lava field segregation. Based on the selection of appropriate pixel representatives, distinct optimizing signal transformations were implemented, with the most dominant being the Fourier transform, in order to use the data in the linear least squares unmixing (LLSU) and bilinear unmixing (BLU) methods. We thus report the results of the historic lava flow characterization and respective abundance analysis qualitatively and quantitatively, evaluated through the structural similarity index (SSIM) of each method. Ultimately, method intercomparison provided the optimum selection for volcanic product segregation.

scholarly journals Cluster-Wise Weighted NMF for Hyperspectral Images Unmixing with Imbalanced Data

2021 ◽  
Vol 13 (2) ◽  
pp. 268
Author(s):  
Xiaochen Lv ◽  
Wenhong Wang ◽  
Hongfu Liu
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Imbalanced Data ◽  
Reconstruction Error ◽  
Hyperspectral Data ◽  
Weight Matrix ◽  
Hyperspectral Images ◽  
Mixed Data ◽  
Sparsity Constraints ◽  
Additional Constraints

Hyperspectral unmixing is an important technique for analyzing remote sensing images which aims to obtain a collection of endmembers and their corresponding abundances. In recent years, non-negative matrix factorization (NMF) has received extensive attention due to its good adaptability for mixed data with different degrees. The majority of existing NMF-based unmixing methods are developed by incorporating additional constraints into the standard NMF based on the spectral and spatial information of hyperspectral images. However, they neglect to exploit the nature of imbalanced pixels included in the data, which may cause the pixels mixed with imbalanced endmembers to be ignored, and thus the imbalanced endmembers generally cannot be accurately estimated due to the statistical property of NMF. To exploit the information of imbalanced samples in hyperspectral data during the unmixing procedure, in this paper, a cluster-wise weighted NMF (CW-NMF) method for the unmixing of hyperspectral images with imbalanced data is proposed. Specifically, based on the result of clustering conducted on the hyperspectral image, we construct a weight matrix and introduce it into the model of standard NMF. The proposed weight matrix can provide an appropriate weight value to the reconstruction error between each original pixel and the reconstructed pixel in the unmixing procedure. In this way, the adverse effect of imbalanced samples on the statistical accuracy of NMF is expected to be reduced by assigning larger weight values to the pixels concerning imbalanced endmembers and giving smaller weight values to the pixels mixed by majority endmembers. Besides, we extend the proposed CW-NMF by introducing the sparsity constraints of abundance and graph-based regularization, respectively. The experimental results on both synthetic and real hyperspectral data have been reported, and the effectiveness of our proposed methods has been demonstrated by comparing them with several state-of-the-art methods.

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