scholarly journals Water Turbidity Retrieval Based on UAV Hyperspectral Remote Sensing

Water ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 128
Author(s):  
Mengying Cui ◽  
Yonghua Sun ◽  
Chen Huang ◽  
Mengjun Li
Keyword(s):  
Hyperspectral Image ◽  
Spectral Response ◽  
Field Investigation ◽  
Hyperspectral Data ◽  
Liaoning Province ◽  
Water Quality Parameter ◽  
Retrieval Models ◽  
Band Ratio ◽  
Water Turbidity ◽  
Aquaculture Ponds

The water components affecting turbidity are complex and changeable, and the spectral response mechanism of each water quality parameter is different. Therefore, this study mainly aimed at the turbidity monitoring by unmanned aerial vehicle (UAV) hyperspectral technology, and establishes a set of turbidity retrieval models through the artificial control experiment, and verifies the model’s accuracy through UAV flight and water sample data in the same period. The results of this experiment can also be extended to different inland waters for turbidity retrieval. Retrieval of turbidity values of small inland water bodies can provide support for the study of the degree of water pollution. We collected the images and data of aquaculture ponds and irrigation ditches in Dawa District, Panjin City, Liaoning Province. Twenty-nine standard turbidity solutions with different concentration gradients (concentration from 0 to 360 NTU—the abbreviation of Nephelometric Turbidity Unit, which stands for scattered turbidity.) were established through manual control and we simultaneously collected hyperspectral data from the spectral values of standard solutions. The sensitive band to turbidity was obtained after analyzing the spectral information. We established four kinds of retrieval, including the single band, band ratio, normalized ratio, and the partial least squares (PLS) models. We selected the two models with the highest R2 for accuracy verification. The band ratio model and PLS model had the highest accuracy, and R2 was, respectively, 0.65 and 0.72. The hyperspectral image data obtained by UAV were combined with the PLS model, which had the highest R2 to estimate the spatial distribution of water turbidity. The turbidity of the water areas in the study area was 5–300 NTU, and most of which are 5–80 NTU. It shows that the PLS models can retrieve the turbidity with high accuracy of aquaculture ponds, irrigation canals, and reservoirs in Dawa District of Panjin City, Liaoning Province. The experimental results are consistent with the conclusions of the field investigation.

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 Study on Spectral Response and Estimation of Grassland Plants Dust Retention Based on Hyperspectral Data

2020 ◽  
Vol 12 (12) ◽  
pp. 2019
Author(s):  
Yibo Zhao ◽  
Shaogang Lei ◽  
Xingchen Yang ◽  
Chuangang Gong ◽  
Cangjiao Wang ◽  
...  
Keyword(s):  
Random Forest ◽  
Simulated Annealing Algorithm ◽  
Hyperspectral Image ◽  
Spectral Response ◽  
Hyperspectral Data ◽  
Correlation Spectroscopy ◽  
Estimation Model ◽  
Random Forest Regression ◽  
Grassland Plants ◽  
Dust Retention

Accurate monitoring of plant dust retention can provide a basis for dust pollution control and environmental protection. The aims of this study were to analyze the spectral response features of grassland plants to mining dust and to predict the spatial distribution of dust retention using hyperspectral data. The dust retention content was determined by an electronic analytical balance and a leaf area meter. The leaf reflectance spectrum was measured by a handheld hyperspectral camera, and the airborne hyperspectral data were obtained using an imaging spectrometer. We analyzed the difference between the leaf spectral before and after dust removal. The sensitive spectra of dust retention on the leaf- and the canopy-scale were determined through two-dimensional correlation spectroscopy (2DCOS). The competitive adaptive reweighted sampling (CARS) algorithm was applied to select the feature bands of canopy dust retention. The estimation model of canopy dust retention was built through random forest regression (RFR), and the dust distribution map was obtained based on the airborne hyperspectral image. The results showed that dust retention enhanced the spectral reflectance of leaves in the visible wavelength but weakened the reflectance in the near-infrared wavelength. Caused by the canopy structure and multiple scattering, a slight difference in the sensitive spectra on dust retention existed between the canopy and leaves. Similarly, the sensitive spectra of leaves and the canopy were closely related to dust and plant physiological parameters. The estimation model constructed through 2DCOS-CARS-RFR showed higher precision, compared with genetic algorithm-random forest regression (GA-RFR) and simulated annealing algorithm-random forest regression (SAA-RFR). Spatially, the amount of canopy dust increased and then decreased with increasing distance from the mining area, reaching a maximum within 300–500 m. This study not only demonstrated the importance of extracting feature bands based on the response of plant physical and chemical parameters to dust, but also laid a foundation for the rapid and non-destructive monitoring of grassland plant dust retention.

scholarly journals Cartografía de calizas con datos hiperespectrales AISA Eagle II en una zona montañosa con vegetación densa: cómo orientar geológicamente la corrección atmosférica

2018 ◽  
pp. 125
Author(s):  
J. Buzzi ◽  
E. Costa ◽  
A. Riaza ◽  
O. Fernández ◽  
D. García-Sellés ◽  
...  
Keyword(s):  
Iron Oxide ◽  
Wavelength Range ◽  
Hyperspectral Image ◽  
Spectral Response ◽  
Atmospheric Correction ◽  
Hyperspectral Data ◽  
Rock Outcrops ◽  
Visible Wavelength ◽  
Visible Wavelength Range ◽  
Geological Units

<p>Carbonated rocks are crucial targets for oil exploration, outcropping often in large areas with minimum spectral differences among geological units. The typical carbonate spectral absorptions in 2200 nm and 2300 nm, are excluded from the wavelength range of AISA Eagle II. AISA Eagle II hyperspectral data are processed in flight lines of 1024 swath pixels in the visible to near-infrared wavelength range (400 to 970 nm). The flight has a spatial resolution of 1 m and records a total of 128 channels with a spectral resolution of 4,8 nm. The area of study is a carbonate rocky mountain densely vegetated, covered by variably dense trees and bushes. Masking vegetation cover and shade effects is prior to any geological analysis using hyperspectral image processing. Carbonate units occur in mountain slopes, with small areas of ridges of rock outcrops and wide fans of loose material. The background soil of different geological units differ spectrally only by overall reflectance. Instead, limestone rocky outcrops display spectral responses with smooth typical iron oxide absorptions that distinguish them apart from loose boulders of limestone. Trying to enhance spectral differences in the visible wavelength range among carbonate geological units, an atmospheric correction using field spectra from geologically selected targets in a limestone quarry was performed. This way, it was possible to map apart lithologically similar detrital units dominated by carbonate in a river plain. The limy river bottom displays spectra with a straight line in the visible wavelength range due to abundant organic matter and small grain size. The spectra of the upper terraces record spectral absorption features related to iron oxide contents similar to the rock outcrops in ridges of mountains. The use of field spectra from geologically selected targets improves the mapping capability of hyperspectral imagery in areas with geological units with a homogeneous spectral response.</p>

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.

scholarly journals Hyperspectral Image Classification with Localized Graph Convolutional Filtering

2021 ◽  
Vol 13 (3) ◽  
pp. 526
Author(s):  
Shengliang Pu ◽  
Yuanfeng Wu ◽  
Xu Sun ◽  
Xiaotong Sun
Keyword(s):  
Hyperspectral Image ◽  
Principal Component ◽  
Representation Learning ◽  
Classification Performance ◽  
Hyperspectral Data ◽  
Spectral Graph Theory ◽  
Feature Reduction ◽  
Graph Representation ◽  
Novel Method ◽  
Local Graph

The nascent graph representation learning has shown superiority for resolving graph data. Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. Faced with hyperspectral image (HSI) classification, the priority problem might be how to convert hyperspectral data into irregular domains from regular grids. In this regard, we present a novel method that performs the localized graph convolutional filtering on HSIs based on spectral graph theory. First, we conducted principal component analysis (PCA) preprocessing to create localized hyperspectral data cubes with unsupervised feature reduction. These feature cubes combined with localized adjacent matrices were fed into the popular graph convolution network in a standard supervised learning paradigm. Finally, we succeeded in analyzing diversified land covers by considering local graph structure with graph convolutional filtering. Experiments on real hyperspectral datasets demonstrated that the presented method offers promising classification performance compared with other popular competitors.

scholarly journals Efficient Lossless Compression of Multitemporal Hyperspectral Image Data

2018 ◽  
Vol 4 (12) ◽  
pp. 142 ◽  
Author(s):  
Hongda Shen ◽  
Zhuocheng Jiang ◽  
W. Pan
Keyword(s):  
Data Compression ◽  
Hyperspectral Image ◽  
Image Data ◽  
Lossless Compression ◽  
Large Data ◽  
Hyperspectral Data ◽  
Compression Performance ◽  
Temporal Correlations ◽  
Sensing Applications ◽  
Data Volume

Hyperspectral imaging (HSI) technology has been used for various remote sensing applications due to its excellent capability of monitoring regions-of-interest over a period of time. However, the large data volume of four-dimensional multitemporal hyperspectral imagery demands massive data compression techniques. While conventional 3D hyperspectral data compression methods exploit only spatial and spectral correlations, we propose a simple yet effective predictive lossless compression algorithm that can achieve significant gains on compression efficiency, by also taking into account temporal correlations inherent in the multitemporal data. We present an information theoretic analysis to estimate potential compression performance gain with varying configurations of context vectors. Extensive simulation results demonstrate the effectiveness of the proposed algorithm. We also provide in-depth discussions on how to construct the context vectors in the prediction model for both multitemporal HSI and conventional 3D HSI data.

scholarly journals Flight Considerations and Hyperspectral Image Classifications for Dryland Vegetation Management from a Fixed-wing UAS

2016 ◽  
Vol 5 (2) ◽  
pp. 41 ◽  
Author(s):  
Jessica Mitchell ◽  
Nancy Glenn ◽  
Matthew Anderson ◽  
Ryan Hruska
Keyword(s):  
Hyperspectral Image ◽  
Distribution Patterns ◽  
Unsupervised Classification ◽  
Vegetation Management ◽  
Geometric Error ◽  
Hyperspectral Data ◽  
Unmanned Aerial Systems ◽  
Native Grasses ◽  
Management Objectives ◽  
Aerial Systems

<p class="emsd"><span lang="EN-GB">Unmanned Aerial Systems (UAS)-based hyperspectral remote sensing capabilities developed by the Idaho National Lab and Boise Center Aerospace Lab were tested via demonstration flights that explored the influence of altitude on geometric error, image mosaicking, and dryland vegetation classification. The motivation for this study was to better understand the challenges associated with UAS-based hyperspectral data for distinguishing native grasses such as Sandberg bluegrass (<em>Poa secunda</em>) from invasives such as burr buttercup (<em>Ranunculus testiculatus)</em> in a shrubland environment. The test flights successfully acquired usable flightline data capable of supporting classifiable composite images. Unsupervised classification results support vegetation management objectives that rely on mapping shrub cover and distribution patterns. However, supervised classifications performed poorly despite spectral separability in the image-derived endmember pixels. In many cases, the supervised classifications accentuated noise or features in the mosaic that were artifacts of color balancing and feathering in areas of flightline overlap. Future UAS flight missions that optimize flight planning; minimize illumination differences between flightlines; and leverage ground reference data and time series analysis should be able to effectively distinguish native grasses such as Sandberg bluegrass from burr buttercup. </span></p>

scholarly journals Deep Spectral Spatial Inverted Residual Network for Hyperspectral Image Classification

2021 ◽  
Vol 13 (21) ◽  
pp. 4472
Author(s):  
Tianyu Zhang ◽  
Cuiping Shi ◽  
Diling Liao ◽  
Liguo Wang
Keyword(s):  
Image Classification ◽  
Data Augmentation ◽  
Hyperspectral Image ◽  
Classification Performance ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
Feature Maps ◽  
Spatial Feature ◽  
Global Context

Convolutional neural networks (CNNs) have been widely used in hyperspectral image classification in recent years. The training of CNNs relies on a large amount of labeled sample data. However, the number of labeled samples of hyperspectral data is relatively small. Moreover, for hyperspectral images, fully extracting spectral and spatial feature information is the key to achieve high classification performance. To solve the above issues, a deep spectral spatial inverted residuals network (DSSIRNet) is proposed. In this network, a data block random erasing strategy is introduced to alleviate the problem of limited labeled samples by data augmentation of small spatial blocks. In addition, a deep inverted residuals (DIR) module for spectral spatial feature extraction is proposed, which locks the effective features of each layer while avoiding network degradation. Furthermore, a global 3D attention module is proposed, which can realize the fine extraction of spectral and spatial global context information under the condition of the same number of input and output feature maps. Experiments are carried out on four commonly used hyperspectral datasets. A large number of experimental results show that compared with some state-of-the-art classification methods, the proposed method can provide higher classification accuracy for hyperspectral images.

scholarly journals DIMENSIONALITY REDUCTION VIA AN ORTHOGONAL AUTOENCODER APPROACH FOR HYPERSPECTRAL IMAGE CLASSIFICATION

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 357-362
Author(s):  
V. H. Ayma ◽  
V. A. Ayma ◽  
J. Gutierrez
Keyword(s):  
Dimensionality Reduction ◽  
Hyperspectral Image ◽  
Original Data ◽  
Data Representation ◽  
Hyperspectral Data ◽  
Attractive Alternative ◽  
Kennedy Space Center ◽  
Classification Tasks ◽  
Multiple Levels ◽  
Kennedy Space

Abstract. Nowadays, the increasing amount of information provided by hyperspectral sensors requires optimal solutions to ease the subsequent analysis of the produced data. A common issue in this matter relates to the hyperspectral data representation for classification tasks. Existing approaches address the data representation problem by performing a dimensionality reduction over the original data. However, mining complementary features that reduce the redundancy from the multiple levels of hyperspectral images remains challenging. Thus, exploiting the representation power of neural networks based techniques becomes an attractive alternative in this matter. In this work, we propose a novel dimensionality reduction implementation for hyperspectral imaging based on autoencoders, ensuring the orthogonality among features to reduce the redundancy in hyperspectral data. The experiments conducted on the Pavia University, the Kennedy Space Center, and Botswana hyperspectral datasets evidence such representation power of our approach, leading to better classification performances compared to traditional hyperspectral dimensionality reduction algorithms.

scholarly journals Spectral-Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest

2021 ◽  
Author(s):  
Xiangyu Song ◽  
Sunil Aryal ◽  
Kai Ming Ting ◽  
zhen Liu ◽  
Bin He
Keyword(s):  
Anomaly Detection ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Gabor Filter ◽  
Poor Performance ◽  
Hyperspectral Data ◽  
Relative Mass ◽  
Anomaly Score ◽  
Spectral Anomaly ◽  
Isolation Forest

Anomaly detection in hyperspectral image is affected by redundant bands and the limited utilization capacity of spectral-spatial information. In this article, we propose a novel Improved Isolation Forest (IIF) algorithm based on the assumption that anomaly pixels are more susceptible to isolation than the background pixels. The proposed IIF is a modified version of the Isolation Forest (iForest) algorithm, which addresses the poor performance of iForest in detecting local anomalies and anomaly detection in high-dimensional data. Further, we propose a spectral-spatial anomaly detector based on IIF (SSIIFD) to make full use of global and local information, as well as spectral and spatial information. To be specific, first, we apply the Gabor filter to extract spatial features, which are then employed as input to the Relative Mass Isolation Forest (ReMass-iForest) detector to obtain the spatial anomaly score. Next, original images are divided into several homogeneous regions via the Entropy Rate Segmentation (ERS) algorithm, and the preprocessed images are then employed as input to the proposed IIF detector to obtain the spectral anomaly score. Finally, we fuse the spatial and spectral anomaly scores by combining them linearly to predict anomaly pixels. The experimental results on four real hyperspectral data sets demonstrate that the proposed detector outperforms other state-of-the-art methods.

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