Airborne Hyperspectral Imaging Research Articles

Inferring arsenic anomalies indirectly using airborne hyperspectral imaging – Implication for gold prospecting along the Rise and Shine Shear Zone in New Zealand

Well-exposed mineral deposits are scarce at a global level and presently potential mineral-rich sites are underlying vegetation cover and topsoil, which are suboptimal for direct remote sensing based exploration techniques. This study aims to implement an indirect approach to arsenic (As) distribution mapping using the surface manifestations of the subsurface geology and link it to the known gold mineralisation in the study area. Rise and Shine Shear Zone (RSSZ) in New Zealand is broadly a part of the Otago schist hosting lower to upper green-schist facies rocks manifesting mesothermal gold mineralisation. The area has several surficial geological imprints separating mineralised and non-mineralised zones, but these are dominated by topographic ruggedness, soil moisture and vegetation (mainly grass/tussock) spectra in the hyperspectral data. Initially, a band selection using Recursive Feature Elimination (RFE) was executed. The bands generated were tallied with the field and geological understanding of the area. The resultant 85 bands were then further put through Orthogonal Total Variation Component Analysis (OTVCA) to concise the information in 10 bands. OTVCA output was then classified using Random Forest classifier to map three levels of As concentration (<20 ppm, between 20 and 100 ppm and >100 ppm). The potentially high As concentration zones are likely to be related to the gold mineralisation. The geology of the area correlates with soil exposure which is captured by the classification in some parts, this increases the accuracy but also makes the classification analysis challenging.

Bundle Adjustment of Aerial Linear Pushbroom Hyperspectral Images with Sub-Pixel Accuracy

AbstractLinear pushbroom (LP) cameras are often used in airborne hyperspectral imaging (HSI). Orthoimages are generated for HSI analyses but these require accurate camera orientations from bundle adjustment. However, the limited image overlap leads to an over-parameterization in the bundle adjustment with six degrees of freedom per image exposure, i.e., LP image line. Feature-based matching based on salient key points in small image neighborhoods cannot be readily applied to LP image lines, since each LP image line is only a single pixel wide in one of the image dimensions. A naive mosaicing of consecutive LP image lines leads to unacceptably large errors owing to the relative camera motion within these mosaics, even for a stabilized camera. Thus, a new method that allows the use of established methods for feature-based matching from aerial LP image lines is presented, and observations are retrieved and used in the bundle adjustment. The examination of the spatial misregistration between spectral bands in HSI cameras, i.e., the chromatic aberration, is also examined. The method assumes a stabilized camera system with a processed global navigation satellite system aided inertial navigation system solution. The bundle adjustment is done by estimating trajectory corrections in time intervals and retrieving discrete trajectory estimates from cubic spline interpolation. An experiment was conducted to demonstrate the method. The chromatic aberration is shown to be of sub-pixel level in the LP HSI camera, and the resulting planimetric accuracy (normalized median absolute deviation) from the bundle adjustment is $$\sim 1/4$$ ∼ 1 / 4 of the ground sampling distance in each of the north and east components. The accurate estimates from the bundle adjustment are shown to be suitable for high-quality orthoimage generation.

Relative truth value acquisition method of heterogeneous surface based on the Improved Point Spread Function

Abstract. The acquisition of pixel-scale relative truth value is important for remote sensing validation.The Point Spread Function (PSF) has been widely used in the field of the acquisition of relative truth value of heterogeneous surface. In this study, we propose an Improved Point Spread Function (IPSF) based on the Median Pixel Variability Weighted (MPVW) method and PSF to acquire relative truth value of heterogeneous surface. Firstly, the size of variance and clustering window are confirmed based on the pixel scale information of the satellite product to be validated. Secondly, the PSF suitable for heterogeneous surface is selected from 5 PSFs. Thirdly, the IPSF is constructed according to the MPVW and the PSF suitable for heterogeneous surface. Finally, the IPSF is used to acquire relative true value at pixel-scale of heterogeneous surface from airborne hyperspectral image. This study shows: (1) Good correlation between relative true value obtained using IPSF and reference value is indicated as R2 values among 256 channels reach 0.985, structural similarity (SSIM) ranges from 0.992 to 0.998, and peak signal noise ratio (PSNR) more than 35dB. (2) Better accuracy performance is observed in the acquisition of relative truth value of heterogeneous surface for IPSF than PSF. Compared with conventional PSF, the R2, PSNR and SSIM of the result of IPSF are increased by about 1.34%, 9% and 0.7% on the average. (3) Significant advantage of IPSF is also reported in reducing the deviation compared to PSF, as the average root mean square error (RMSEave) is reduced by 30.37% and the average mean absolute error (MAEave) is reduced by 35.98%, respectively. (4) Comparing the RMSE and MAE of PSF and IPSF result, the RMSE and MAE of the result of IPSF become smaller. The RMSE decreases from 3 ~ 195 to 2 ~ 131. The MAE changes from 2 ~ 138 to 1 ~ 89. Overall, the IPSF proposed in this paper can effectively calculate the relative true value of heterogeneous surface, which can provide reference for the validation of multi-spectral and hyper-spectral satellite products.

Exploring the spectral variation hypothesis for α- and β-diversity: a comparison of open vegetation and forests

Airborne hyperspectral imaging holds great promise for estimating plant diversity and composition, given its unprecedented combination of aerial coverage, spatial resolution, and spectral detail. Recently, there has been renewed attention toward the spectral variation hypothesis (SVH), which predicts that higher spectral variation is correlated with greater plant diversity. While several studies have highlighted methodological challenges involved with the SVH, there is little consensus about when it yields strong predictions of taxonomic, functional, and phylogenetic diversity. In part, this may be because prior studies have not explicitly considered how underlying environmental gradients drive changes in spectral and species composition. In this study, we tested the SVH separately in open vegetation (i.e. grasses and shrubs) and in forests at five sites across Canada. Generalized additive models revealed that spectral diversity was a better predictor of functional α-diversity than of taxonomic or phylogenetic α-diversity in both vegetation types. Mantel tests and Procrustes analyses revealed weak to moderate associations between spectral and plant β-diversity and composition in open vegetation, and moderate associations in forests. The better fit in forests appeared to be influenced by the presence of an elevational gradient and associated species turnover (from deciduous to coniferous trees); we observed weaker relationships when examining only a subset of this gradient. We suggest that the high variability in the strength of associations between plant and spectral diversity reported to date might be affected by the presence of environmental gradients. Finally, we found that different wavelength bands contributed to spectral α-diversity in open vegetation vs. forests, suggesting different spectral features are important for different vegetation types. In conclusion, spectral diversity is a potentially powerful tool for biodiversity assessment, but it requires a context-specific approach.

A semi-analytical approach for estimating inland water inherent optical properties and chlorophyll a using airborne hyperspectral imagery

The inversion of inherent optical properties (IOPs) and chlorophyll a (Chla) is one of the key objectives in water color remote sensing, and hyperspectral remote sensing with rich spectral information makes precise inversion possible. In this study, we developed a semi-analytical estimation method for inland water IOPs based on the quasi-analytical algorithm (QAA). Considering the complex optical characteristics of inland waters, empirical parameter regional optimization was conducted. Furthermore, a dual-band joint inversion strategy and Gaussian function fitting method were utilized to optimize the solution processes for the backscattering coefficient of particles (bbp) and the absorption coefficient of phytoplankton pigments (aph), respectively. This approach overcomes the limitations of single-band bbp inversion in inland waters. In addition, it directly decomposes the absorption coefficient to obtain aph using a Gaussian function, which can reduce the intermediate steps and errors caused by indirect inversion. For Chla, we constructed a binary inversion model using aph(677) and remote sensing reflectance (Rrs), where the coefficient of determination (R2) exceeded 0.8. We also constructed an airborne hyperspectral image correction process, including vicarious calibration, atmospheric correction, and bidirectional reflectance distribution function (BRDF) correction, obtaining high-precision Rrs images. The ground models were successfully applied to the airborne hyperspectral images, mapping the spatial distribution of IOPs and Chla concentration in the study area. The experiments demonstrated that the proposed semi-analytical method using airborne hyperspectral imagery exhibits a good performance in terms of modeling accuracy and mapping analysis, and successfully applied to long-term monitoring using satellite hyperspectral images, highlighting the significant potential of hyperspectral remote sensing for high-precision monitoring of regional water bodies.

Evaluating methods for measuring the leaf area index of encroaching shrubs in grasslands: From leaves to optical methods, 3-D scanning, and airborne observation

Leaf area index (LAI) is a key variable describing ecosystem structure and influencing the exchange of carbon, water, and energy. LAI is often evaluated with indirect methods. However, the accuracy of indirect measurements can vary with canopy structure and is not always generalizable across ecosystems. Previous research has characterized the accuracy of indirect methods for woody plants in forest ecosystems, but it is not well established for woody plants in open ecosystems—despite having large differences in canopy structure. In this study, we compared direct LAI measurements in clonal grassland shrub canopies to three indirect methods: a ceptometer, a handheld 3D scanner (processed using EXScanPro-3.6 software), and NEON's LAI product obtained from airborne hyperspectral imaging (derived from SAVI). To our knowledge, this is the first study to assess the accuracy of leaf area data in woody plants using a handheld 3D scanner and one of few studies assessing the accuracy of the National Ecological Observation Network's (NEON) Airborne Observation Platform—our source of airborne hyperspectral imaging. Data were collected in tallgrass prairie undergoing woody encroachment and three treatments: no herbivore disturbance, bison present, and simulated browsing. We found that direct LAI measurements of control and grazed C. drummondii canopies averaged ∼8.0. The ceptometer accurately estimated LAI in non-browsed canopies but overestimated LAI of browsed canopies by 38 %. One-sided leaf area of ramets measured with a handheld 3D scanner was strongly related to direct measurements (r2=0.86), but underestimated leaf area at greater values. LAI estimated from airborne spectral data underestimated LAI by 55 %. We conclude that a ceptometer was adequate for measuring LAI in dense shrub canopies when browsing was not present, the handheld 3D scanner was adequate for measuring leaf area of individual ramets, and the airborne spectral data was not suitable for estimating LAI of dense, grassland shrub canopies.

Prototype development and evaluation of a hyperspectral lidar optical receiving system.

As a new type of active Earth observation technology, airborne hyperspectral lidar combines the advantages of traditional lidar 3D information acquisition and passive hyperspectral imaging technology, and it can achieve integrated imaging detection with a high spatial and hyperspectral resolution. Thus, it has become an important future direction of Earth surface remote sensing technology. This article introduces the design and development of an airborne hyperspectral imaging lidar system. The hyperspectral lidar adopts a focal plane splitting method, combined with an array of 168 optical fibers, to couple wide-spectral-range laser echo signals one by one to the corresponding single tube detector, achieving efficient splitting and precise coupling of supercontinuum laser pulse echo signals. This article proposes a fast synchronous calibration method that is suitable for hyperspectral imaging lidar systems. Results show that the spectral range of the hyperspectral lidar system is 400-900 nm, and the spectral resolution of single-fiber detection is greater than 3 nm. Notably, this article focuses on analyzing the abnormal detection channels based on the calibration results. With the test results of adjacent channels combined, the reason for the abnormal spectral bandwidth of channel 17 is analyzed as an example. This research points out the direction for verifying the design parameters of the hyperspectral lidar prototype and lays an important foundation for airborne flight test of the hyperspectral lidar.

A novel framework combining band selection algorithm and improved 3D prototypical network for tree species classification using airborne hyperspectral images

Fine-grained identification of forest types and tree species represents a critical aspect of forest resource inventory and monitoring. The use of airborne hyperspectral remote sensing imagery stands out for its ability to finely differentiate among tree species, leveraging its exceptional spatial resolution and rich spectral details. However, this approach is limited by several challenges (e.g., high spectral correlation and information redundancy). In accordance, the adoption of a lightweight deep learning approach in the form of a few-shot learning model can effectively resolve the challenges of multi-forest tree species classification. Therefore, integrating a data dimensionality reduction algorithm with a few-shot classification model presents a promising avenue for resolving the fine-grained classification of forest tree species. In this study, we propose the innovative classification framework FAST 3D-CNN P-Net. This framework utilizes CNN for band selection, enhances the fine-grained identification process in hyperspectral data, and integrates an optimized FAST 3D-CNN into the P-Net classifier (a few-shot classifier). First, a CNN-based band selection method is employed to learn the nonlinear dependencies between spectral bands, assign weights to rank the bands, and reconstruct the global spectral information using the most informative bands. It then constructs a novel classification model, designated FAST 3D-CNN P-Net, through the integration of an optimal 3D-CNN with a prototypical network. To enhance classification performance, the FAST 3D-CNN P-Net utilizes reconstructed hyperspectral images derived from the band selection results as input. The effectiveness of the proposed framework was assessed with the airborne GFF dataset and the widely accessible medium-resolution hyperspectral datasets, Indian Pines (IP) and Kennedy Space Center (KSC). The overall classification accuracy reached 98.33 % for the GFF dataset and 97.21 % and 99.43 % for the IP and KSC, respectively, exhibiting performance superiority compared to the standalone 3D-CNN classification network. This classification framework demonstrates efficiency in selecting a subset of hyperspectral bands with minimal redundancy, empowering the rapid and accurate classification and mapping of tree species in complicated, multi-species forest stands, even with a limited quantity of labeled samples.

Mapping of soil organic carbon density at farmland scale based on airborne hyperspectral images

Mapping of soil organic carbon density at farmland scale based on airborne hyperspectral images

Tree Species Classification from Airborne Hyperspectral Images Using Spatial–Spectral Network

Tree species identification is a critical component of forest resource monitoring, and timely and accurate acquisition of tree species information is the basis for sustainable forest management and resource assessment. Airborne hyperspectral images have rich spectral and spatial information and can detect subtle differences among tree species. To fully utilize the advantages of hyperspectral images, we propose a double-branch spatial–spectral joint network based on the SimAM attention mechanism for tree species classification. This method achieved high classification accuracy on three tree species datasets (93.31% OA value obtained in the TEF dataset, 95.7% in the Tiegang Reservoir dataset, and 98.82% in the Xiongan New Area dataset). The network consists of three parts: spectral branch, spatial branch, and feature fusion, and both branches make full use of the spatial–spectral information of pixels to avoid the loss of information. In addition, the SimAM attention mechanism is added to the feature fusion part of the network to refine the features to extract more critical features for high-precision tree species classification. To validate the robustness of the proposed method, we compared this method with other advanced classification methods through a series of experiments. The results show that: (1) Compared with traditional machine learning methods (SVM, RF) and other state-of-the-art deep learning methods, the proposed method achieved the highest classification accuracy in all three tree datasets. (2) Combining spatial and spectral information and incorporating the SimAM attention mechanism into the network can improve the classification accuracy of tree species, and the classification performance of the double-branch network is better than that of the single-branch network. (3) The proposed method obtains the highest accuracy under different training sample proportions, and does not change significantly with different training sample proportions, which are stable. This study demonstrates that high-precision tree species classification can be achieved using airborne hyperspectral images and the methods proposed in this study, which have great potential in investigating and monitoring forest resources.

Combined Retrieval of Oil Film Thickness Using Hyperspectral and Thermal Infrared Remote Sensing

An outdoor experiment was conducted to measure the thickness of oil films (0~3000 μm) using an airborne hyperspectral imager and thermal infrared imager, and the spectral response and thermal response of oil films of different thicknesses were analyzed. The classic support vector regression (SVR) model was used to retrieve the oil film thickness. On this basis, the suitable range for retrieving oil film thickness using hyperspectral and thermal infrared remote sensing was explored, and the decision-level fusion algorithm was developed to fuse the retrieval capabilities of hyperspectral and thermal infrared remote sensing for oil film thickness. The following conclusions can be drawn: (1) Based on airborne hyperspectral data and thermal infrared data, the retrieval accuracy of oil films of different thicknesses reached 154.31 μm and 116.59 μm, respectively. (2) The S185 hyperspectral data were beneficial for retrieving thicknesses greater than or equal to 400 μm, and the H20T thermal infrared data were beneficial for retrieving thicknesses greater than 500 μm. (3) The result of the decision-level fusion model based on a fuzzy membership degree was superior to those obtained using a single sensor (hyperspectral or thermal infrared), indicating that it can better integrate the retrieval results of hyperspectral and thermal infrared remote sensing for oil film thickness. Furthermore, the feasibility of using hyperspectral and thermal infrared remote sensing to detect water-in-oil emulsions of different thicknesses was investigated through spectral response and thermal response analysis.

Study on Surface Reflectance Sampling Method and Uncertainty Based on Airborne Hyperspectral Images

The validation of satellite remote sensing surface reflectance products is aimed at comparing the reflectance pixel values of products with ground measurement values at the pixel scale. Due to the existence of surface heterogeneity, we cannot obtain the satellite pixel scale truth value through ground sampling, and only the satellite relative pixel scale truth value that closely approximates it can be acquired. The process of converting the point-scale spectrum of ground sampling into a pixel-scale spectrum will produce certain errors, known as point-to-pixel-scale conversion uncertainty, which is closely related to the type of sample area and the ground sampling method. In this study, we conducted research on the uncertainty of point-to-pixel-scale conversion generated via different ground sampling methods in the upscaling process. We utilized unmanned aerial vehicle (UAV) hyperspectral images to invert the surface reflectance spectral curves of wheat, corn, bare soil, and soybeans at the pixel scale, and simulate the ground measurement spectra and satellite pixel scale ground truth of different sampling methods, so as to realize the quantitative calculation of the uncertainty of the ground truth at the satellite pixel scale. On this basis, we analyzed in depth the effects of the sampling method, measurement height, and number of spectra on the scale conversion uncertainty. The research results show that airborne hyperspectral images can accurately simulate the spectra of ground measurements, and can be used as an effective means of ground spectral sampling and uncertainty analysis. When using the systematic sampling method, the more the sampling points, the smaller the uncertainty. However, the uncertainty of scale conversion tends to stabilize when the number of sampling points is increased to a certain quantity. As the height of ground measurement increases, the number of spectra within the elementary sampling unit (ESU) increases, leading to smaller scale conversion uncertainties. The research results of this study will provide support for the subsequent optimization of ground sampling methods and the improvement of measurement efficiency and measurement accuracy.

Airborne hyperspectral imaging for early diagnosis of kimchi cabbage downy mildew using 3D-ResNet and leaf segmentation

Kimchi cabbage (Brassica rapa pekinensis), one of the main agricultural products in Korea, is susceptible to downy mildew disease infections. Infected plants develop yellow spots (chlorosis) on the upper (adaxial) side of the infected leaf, undermining cabbage production and quality. An early detection method to recognize and treat the disease is crucial to prevent downy mildew and lessen its physical effects on plants. Hyperspectral imaging can capture data from a broad spectrum, which can be utilized to detect disease occurrence before any visible symptoms appear. Combining a hyperspectral camera with an unmanned aerial vehicle (UAV) can provide a non-destructive, field-scale disease detection system. In this study, three-dimensional (3D) convolutional neural network (CNN) models were used to simultaneously account for the spectral and spatial features of the disease to enable automatic disease detection. Using a 3D-residual network (ResNet) CNN with four residual blocks, each followed by a rectified linear unit activation function and a max-pooling layer, helped achieve an overall accuracy of 0.876 and a diseased class accuracy of 0.873. Disease severity was estimated by grouping nearby diseased leaves using the density-based spatial clustering of applications with noise clustering algorithm to achieve a 27.07 % relative error or a 1.08 level difference from the actual.

Design and Implementation of Airborne Hyperspectral Camera Control and Data Storage System

With the great progress of hyperspectral imaging technology, airborne hyperspectral cameras have been widely used in the field of earth detection and remote sensing. However, with the increasing resolution of airborne hyperspectral cameras, the amount of data acquired is also growing explosive, The speed and accuracy in the transmission process are easily affected, which poses challenges to the real-time control and data acquisition and storage of airborne hyperspectral cameras. In this paper, a general airborne hyperspectral image acquisition and storage system is designed, which converts the hyperspectral data from the Cameralink interface into a high-speed serial PCIe data stream and realizes high-speed storage of image data. The image width is 2048 pixels, the number of spectral segments is 120, the pixel size is 12bit, and the maximum frame rate is 300 fps. The maximum transmission rate is 2048×120×12bit×300fps, and the data rate is 844Mbps. Based on this, we perform thermal analysis and vibration analysis of the system platform. Experimental results show that the system can achieve stable data transmission and storage, and the data is intact and reliable.

Methane retrievals from airborne HySpex observations in the shortwave infrared

Abstract. Monitoring anthropogenic emissions is a crucial aspect in understanding the methane budget. Moreover, a reduction of methane emissions could help to mitigate global warming on a short timescale. This study compares various retrieval schemes for estimating localized methane enhancements around ventilation shafts in the Upper Silesian Coal Basin in Poland using nadir observations in the shortwave infrared acquired from the airborne imaging spectrometer HySpex. Linear and nonlinear solvers are examined and compared, with special emphasis put on strategies that tackle degeneracies between the surface reflectivity and broad-band molecular absorption features – a challenge arising from the instrument's low spectral resolution. Results reveal that the generalized nonlinear least squares fit, employed within the Beer InfraRed Retrieval Algorithm (BIRRA), can measure enhanced methane levels with notable accuracy and precision. This is accomplished by allowing the scene's background covariance structure to account for surface reflectivity statistics. Linear estimators such as matched filter (MF) and singular value decomposition (SVD) are able to detect and, under favorable conditions, quantify enhanced levels of methane quickly. Using k-means clustering as a preprocessing step can further enhance the performance of the two linear solvers. The linearized BIRRA fit (LLS) underestimates methane but agrees on the enhancement pattern. The non-quantitative spectral signature detection (SSD) method does not require any forward modeling and can be useful in the detection of relevant scenes. In conclusion, the BIRRA code, originally designed for the retrieval of atmospheric constituents from spaceborne high-resolution spectra, turned out to be applicable to hyperspectral airborne imaging data for the quantification of methane plumes from point-like sources. Moreover, it is able to outperform well-established linear schemes such as the MF or SVD at the expense of high(er) computing time.

Hyperspectral Marine Oil Spill Monitoring Using a Dual-Branch Spatial–Spectral Fusion Model

Marine oil spills pose a crucial concern in the monitoring of marine environments, and optical remote sensing serves as a vital means for marine oil spill detection. However, optical remote sensing imagery is susceptible to interference from sunglints and shadows, leading to diminished spectral differences between oil films and seawater. This makes it challenging to accurately extract the boundaries of oil–water interfaces. To address these aforementioned issues, this paper proposes a model based on the graph convolutional architecture and spatial–spectral information fusion for the oil spill detection of real oil spill incidents. The model is experimentally evaluated using both spaceborne and airborne hyperspectral oil spill images. Research findings demonstrate the superior oil spill detection accuracy of the developed model when compared to Graph Convolutional Network (GCN) and CNN-Enhanced Graph Convolutional Network (CEGCN), across two hyperspectral datasets collected from the Bohai Sea. Moreover, the performance of the developed model in oil spill detection remains optimal, even with only 1% of the training samples. Similar conclusions are drawn from the oil spill hyperspectral data collected from the Yellow Sea. These results validate the efficacy and robustness of the proposed model for marine oil spill detection.

Discriminative feature constraints via supervised contrastive learning for few-shot forest tree species classification using airborne hyperspectral images

In scenarios where sample collection is limited, studying few-shot learning algorithms such as prototypical networks (P-Net) is a keynote topic for supervised multiple tree species classification. In a previous study, we improved the P-Net by combining the feature enhancement algorithm based on the convolutional block attention module and several popular data augmentation methods in the computer vision domain, the classification accuracy can be significantly increased, and the degree of model overfitting can be reduced. However, there is a clear boundary between the data augmentations and the feature enhancement algorithm, which is manifested in that data augmentations are only used to enrich the diversity of the learned samples, but cannot directly affect the construction of the objective function, thus limiting the ability of data augmentations. In fact, in the supervised contrastive learning research, data augmentation methods are often used to generate positive samples of an anchor image to construct the objective function, i.e. supervised contrastive loss. The core idea for solving such a boundary problem is to use contrastive learning to make the anchor image close to its positive samples and the negative samples away from each other. Inspired by this, we introduced supervised contrastive learning in the P-Net, i.e., SCL-P-Net, which takes the discriminative feature representations as the constraints of the prototype clustering algorithm. In SCL-P-Net, data augmentation methods can not only extend the sample distribution, but also be used to construct the supervised contrastive loss directly. The study involves four airborne hyperspectral image datasets related to tree species classification, including the GFF-A and GFF-B datasets collected from Gaofeng Forest Farm in Nanning City, Guangxi Province, South China, the Xiongan dataset from Matiwan Village in Xiongan New Area, Hebei Province, North China, and the Tea Farm dataset from Fanglu Tea Farm in Changzhou City, Jiangsu Province, East China. The highest overall accuracy (OA) for the four datasets is 99.23% for GFF-A, 98.39% for GFF-B, 99.30% for Xiongan, and 99.54% for Tea Farm. It is concluded that the proposed SCL-P-Net classification framework can achieve multiple tree species classification with high-precision. Without changing the basic classification framework of P-Net, the introduction of supervised contrastive learning makes the combination of the data augmentations and the feature enhancement algorithm and plays a positive role in improving the distinguishability between samples.

A Gated Content-Oriented Residual Dense Network for Hyperspectral Image Super-Resolution

Limited by the existing imagery sensors, a hyperspectral image (HSI) is characterized by its high spectral resolution but low spatial resolution. HSI super-resolution (SR) aims to enhance the spatial resolution of the HSIs without modifying the equipment and has become a hot issue for HSI processing. In this paper, inspired by two important observations, a gated content-oriented residual dense network (GCoRDN) is designed for the HSI SR. To be specific, based on the observation that the structure and texture exhibit different sensitivities to the spatial degradation, a content-oriented network with two branches is designed. Meanwhile, a weight-sharing strategy is merged in the network to preserve the consistency in the structure and the texture. In addition, based on the observation of the super-resolved results, a gating mechanism is applied as a form of post-processing to further enhance the SR performance. Experimental results and data analysis on both ground-based HSIs and airborne HSIs have demonstrated the effectiveness of the proposed method.

Do beaver ponds increase methane emissions along Arctic tundra streams?

Beaver engineering in the Arctic tundra induces hydrologic and geomorphic changes that are favorable to methane (CH4) production. Beaver-mediated methane emissions are driven by inundation of existing vegetation, conversion from lotic to lentic systems, accumulation of organic rich sediments, elevated water tables, anaerobic conditions, and thawing permafrost. Ground-based measurements of CH4 emissions from beaver ponds in permafrost landscapes are scarce, but hyperspectral remote sensing data (AVIRIS-NG) permit mapping of ‘hotspots’ thought to represent locations of high CH4 emission. We surveyed a 429.5 km2 area in Northwestern Alaska using hyperspectral airborne imaging spectroscopy at ∼5 m pixel resolution (14.7 million observations) to examine spatial relationships between CH4 hotspots and 118 beaver ponds. AVIRIS-NG CH4 hotspots covered 0.539% (2.3 km2) of the study area, and were concentrated within 30 m of waterbodies. Comparing beaver ponds to all non-beaver waterbodies (including waterbodies >450 m from beaver-affected water), we found significantly greater CH4 hotspot occurrences around beaver ponds, extending to a distance of 60 m. We found a 51% greater CH4 hotspot occurrence ratio around beaver ponds relative to nearby non-beaver waterbodies. Dammed lake outlets showed no significant differences in CH4 hotspot ratios compared to non-beaver lakes, likely due to little change in inundation extent. The enhancement in AVIRIS-NG CH4 hotspots adjacent to beaver ponds is an example of a new disturbance regime, wrought by an ecosystem engineer, accelerating the effects of climate change in the Arctic. As beavers continue to expand into the Arctic and reshape lowland ecosystems, we expect continued wetland creation, permafrost thaw and alteration of the Arctic carbon cycle, as well as myriad physical and biological changes.

Endmember variability based abundance estimation of red and black soil over sparsely vegetated area using AVIRIS-NG hyperspectral image

Visible-near-and-short-wave-infrared hyperspectral images (HSI) have proven helpful for mapping the soil types over bare soils pixels. However, the accuracy of the traditional pixel-based classification methods decreases due to the spectral mixing between the significant agricultural features such as vegetation, soil and crop residue. In this context, spectral unmixing algorithms are handy for estimating the sub-pixel abundances of soil over sparsely vegetated areas. Most of the spectral unmixing methods focus on analyzing the HSI by considering the pixels as independent entities. However, in the HSIs of agricultural fields, the endmembers are spatially and temporally variable. It is also known that there exists a strong spatial correlation among neighbourhood pixels over agricultural fields. Therefore, both endmember variability and the neighbourhood spatial-spectral information are critical to estimate soil abundance accurately. Here we address the issue of estimating the abundances of two major soil types with spectral variability. The proposed approach combines the endmember bundle extraction with the spectral-spatial weighted unmixing approach (SSWU-SV). Experimental analysis has been carried out on the airborne HSI acquired by airborne-visible-and-infrared-imaging- spectrometer-next-generation (AVIRIS-NG) sensor. The quantitative analysis reveals that the proposed method consistently achieves a better unmixing performance than the traditional linear mixing model in terms of spectral angle mapper (SAM), and root-mean-square error (RMSE). Our results also indicate the saturation of normalized difference vegetation index (NDVI) at high vegetative fractions obtained from SSWU-SV.