Search Results (712)

Hyperspectral cameras collect detailed spectral information at each image pixel, contributing to the identification of image features. The rich spectral content of hyperspectral imagery has led to its application in diverse fields of study. This study focused on cloud classification using a dataset of hyperspectral sky images captured by a Resonon PIKA XC2 camera. The camera records images using 462 spectral bands, ranging from 400 to 1000 nm, with a spectral resolution of 1.9 nm. Our preliminary/unlabeled dataset comprised 33 parent hyperspectral images (HSI), each a substantial unlabeled image measuring 4402-by-1600 pixels. With the meteorological expertise within our team, we manually labeled pixels by extracting 10 to 20 sample patches from each parent image, each patch consisting of a 50-by-50 pixel field. This process yielded a collection of 444 patches, each categorically labeled into one of seven cloud and sky condition categories. To embed the inherent data structure while classifying individual pixels, we introduced an innovative technique to boost classification accuracy by incorporating patch-specific information into each pixel’s feature vector. The posterior probabilities generated by these classifiers, which capture the unique attributes of each patch, were subsequently concatenated with the pixel’s original spectral data to form an augmented feature vector. We then applied a final classifier to map the augmented vectors to the seven cloud/sky categories. The results compared favorably to the baseline model devoid of patch-origin embedding, showing that incorporating the spatial context along with the spectral information inherent in hyperspectral images enhances the classification accuracy in hyperspectral cloud classification. The dataset is available on IEEE DataPort. Full article

Tree species classification using hyperspectral imagery shows incredible promise in developing a large-scale, high-resolution model for identifying tree species, providing unprecedented details on global tree species distribution. Many questions remain unanswered about the best practices for creating a global, general hyperspectral tree species classification model. This study aims to address three key issues in creating a hyperspectral species classification model. We assessed the effectiveness of three data-labeling methods to create training data, three data-splitting methods for training/validation/testing, and machine-learning and deep-learning (including semi-supervised deep-learning) models for tree species classification using hyperspectral imagery at National Ecological Observatory Network (NEON) Sites. Our analysis revealed that the existing data-labeling method using the field vegetation structure survey performed reasonably well. The random tree data-splitting technique was the most efficient method for both intra-site and inter-site classifications to overcome the impact of spatial autocorrelation to avoid the potential to create a locally overfit model. Deep learning consistently outperformed random forest classification; both semi-supervised and supervised deep-learning models displayed the most promising results in creating a general taxa-classification model. This work has demonstrated the possibility of developing tree-classification models that can identify tree species from outside their training area and that semi-supervised deep learning may potentially utilize the untapped terabytes of unlabeled forest imagery. Full article

The cotton bollworm (CBW) poses a significant risk to maize crops worldwide. This study investigated whether hyperspectral satellites offer an accurate evaluation method for monitoring maize ear damage caused by CBW larvae. The study analyzed the records of maize ear damage for four maize fields in Southeast Hungary, Csongrád-Csanád County, in 2021. The performance of Sentinel-2 bands, PRISMA bands, and synthesized Sentinel-2 bands was compared using linear regression, partial least squares regression (PLSR), and two-band vegetation index (TBVI) methods. The best newly developed indices derived from the TBVI method were compared with existing vegetation indices. In mid-early grain maize fields, narrow bands of PRISMA generally performed better than wide bands, unlike in sweet maize fields, where the Sentinel-2 bands performed better. In grain maize fields, the best index was the normalized difference of λ_A = 571 and λ_B = 2276 (R² = 0.33–0.54, RMSE 0.06–0.05), while in sweet maize fields, the best-performing index was the normalized difference of green (B03) and blue (B02) Sentinel-2 bands (R² = 0.54–0.72, RMSE 0.02). The findings demonstrate the advantages and constraints of remote sensing for plant protection and pest monitoring. Full article

This study focused on improving the clustering performance of hyperspectral imaging (HSI) by employing the Generalized Orthogonal Matching Pursuit (GOMP) algorithm for feature extraction. Hyperspectral remote sensing imaging technology, which is crucial in various fields like environmental monitoring and agriculture, faces challenges due to its high dimensionality and complexity. Supervised learning methods require extensive data and computational resources, while clustering, an unsupervised method, offers a more efficient alternative. This research presents a novel approach using GOMP to enhance clustering performance in HSI. The GOMP algorithm iteratively selects multiple dictionary elements for sparse representation, which makes it well-suited for handling complex HSI data. The proposed method was tested on two publicly available HSI datasets and evaluated in comparison with other methods to demonstrate its effectiveness in enhancing clustering performance. Full article

Mangrove ecosystems play an irreplaceable role in coastal environments by providing essential ecosystem services. Diverse mangrove species have different functions due to their morphological and physiological characteristics. A precise spatial distribution map of mangrove species is therefore crucial for biodiversity maintenance and environmental conservation of coastal ecosystems. Traditional satellite data are limited in fine-scale mangrove species classification due to low spatial resolution and less spectral information. This study employed unmanned aerial vehicle (UAV) technology to acquire high-resolution multispectral and hyperspectral mangrove forest imagery in Guangxi, China. We leveraged advanced algorithms, including RFE-RF for feature selection and machine learning models (Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM)), to achieve mangrove species mapping with high classification accuracy. The study assessed the classification performance of these four machine learning models for two types of image data (UAV multispectral and hyperspectral imagery), respectively. The results demonstrated that hyperspectral imagery had superiority over multispectral data by offering enhanced noise reduction and classification performance. Hyperspectral imagery produced mangrove species classification with overall accuracy (OA) higher than 91% across the four machine learning models. LightGBM achieved the highest OA of 97.15% and kappa coefficient (Kappa) of 0.97 based on hyperspectral imagery. Dimensionality reduction and feature extraction techniques were effectively applied to the UAV data, with vegetation indices proving to be particularly valuable for species classification. The present research underscored the effectiveness of UAV hyperspectral images using machine learning models for fine-scale mangrove species classification. This approach has the potential to significantly improve ecological management and conservation strategies, providing a robust framework for monitoring and safeguarding these essential coastal habitats. Full article

Vegetation canopy water content (CWC) crucially affects stomatal conductance and photosynthesis and, consequently, is a key state variable in advanced ecosystem models. Remote sensing has been shown to be an effective tool for retrieving CWCs. However, the retrieval of the CWC from satellite remote sensing data is affected by the vegetation canopy structure and soil background. This study proposes a methodology that combines a modified spectral down-scaling model with a high-universality leaf water content inversion model to retrieve the CWC through constraining the impacts of canopy structure and soil background on CWC retrieval. First, canopy spectra acquired by satellite sensors were down-scaled to leaf reflectance spectra according to the probabilities of viewing the sunlit foliage (PT) and background (PG) and the estimated spectral multiple scattering factor (M). Then, leaf water content, or equivalent water thickness (EWT), was obtained from the down-scaled leaf reflectance spectra via a leaf-scale EWT inversion model calibrated with PROSPECT simulation data. Finally, the CWC was calculated as the product of the estimated leaf EWT and canopy leaf area index. Validation of this coupled model was performed using satellite-ground synchronous observation data across various vegetation types within the study area, affirming the model’s broad applicability. Results indicate that the modified spectral down-scaling model accurately retrieves leaf reflectance spectra, aligning closely with site-level measured spectra. Compared to the direct inversion approach, which performs poorly with Hyperion satellite images, the down-scale strategy notably excels. Specifically, the Similarity Water Index (SWI)-based canopy EWT coupled model achieved the most precise estimation, with a normalized Root Mean Square Error (nRMSE) of 15.28% and an adjusted R² of 0.77, surpassing the performance of the best index Shortwave Angle Normalized Index (SANI)-based model (nRMSE = 15.61%, adjusted R² = 0.52). Given its calibration using simulated data, this coupled model proved to be a potent method for extracting canopy EWT from satellite imagery, suggesting its applicability to retrieve other vegetative biochemical components from satellite data. Full article

Band selection (BS) aims to reduce redundancy in hyperspectral imagery (HSI). Existing BS approaches typically model HSI only in a single dimension, either spectral or spatial, without exploring the interactions between different dimensions. To this end, we propose an unsupervised BS method based on a spectral–spatial cross-dimensional attention network, named SSANet-BS. This network is comprised of three stages: a band attention module (BAM) that employs an attention mechanism to adaptively identify and select highly significant bands; two parallel spectral–spatial attention modules (SSAMs), which fuse complex spectral–spatial structural information across dimensions in HSI; a multi-scale reconstruction network that learns spectral–spatial nonlinear dependencies in the SSAM-fusion image at various scales and guides the BAM weights to automatically converge to the target bands via backpropagation. The three-stage structure of SSANet-BS enables the BAM weights to fully represent the saliency of the bands, thereby valuable bands are obtained automatically. Experimental results on four real hyperspectral datasets demonstrate the effectiveness of SSANet-BS. Full article

Herein, the development of an estimation model to measure the chlorophyll (Ch) and macronutrients, such as the total nitrogen (T-N), phosphorus (P), potassium (K), carbon (C), calcium (Ca), and magnesium (Mg), in apples is detailed, using key band ratios selected from hyperspectral imagery acquired with an unmanned aerial vehicle, for the management of nutrients in an apple orchard. The k-nearest neighbors regression (KNR) model for Ch and all macronutrients was chosen as the best model through a comparison of calibration and validation R² values. As a result of model development, a total of 13 band ratios (425/429, 682/686, 710/714, 714/718, 718/722, 750/754, 754/758, 758/762, 762/766, 894/898, 898/902, 906/911, and 963/967) were selected for Ch and all macronutrients. The estimation potential for the T-N and Mg concentrations was low, with an R² ≤ 0.37. The estimation performance for the other macronutrients was as follows: R² ≥ 0.70 and RMSE ≤ 1.43 μg/cm² for Ch; R² ≥ 0.44 and RMSE ≤ 0.04% for P; R² ≥ 0.53 and RMSE ≤ 0.23% for K; R² ≥ 0.85 and RMSE ≤ 6.18% for C; and R² ≥ 0.42 and RMSE ≤ 0.25% for Ca. Through establishing a fertilization strategy using the macronutrients estimated through hyperspectral imagery and measured soil chemical properties, this study presents a nutrient management decision-making method for apple orchards. Full article

Transformers have shown remarkable success in modeling sequential data and capturing intricate patterns over long distances. Their self-attention mechanism allows for efficient parallel processing and scalability, making them well-suited for the high-dimensional data in hyperspectral and LiDAR imagery. However, further research is needed on how to more deeply integrate the features of two modalities in attention mechanisms. In this paper, we propose a novel Multi-Feature Cross Attention-Induced Transformer Network (MCAITN) designed to enhance the classification accuracy of hyperspectral and LiDAR data. The MCAITN integrates the strengths of both data modalities by leveraging a cross-attention mechanism that effectively captures the complementary information between hyperspectral and LiDAR features. By utilizing a transformer-based architecture, the network is capable of learning complex spatial-spectral relationships and long-range dependencies. The cross-attention module facilitates the fusion of multi-source data, improving the network’s ability to discriminate between different land cover types. Extensive experiments conducted on benchmark datasets demonstrate that the MCAITN outperforms state-of-the-art methods in terms of classification accuracy and robustness. Full article

Proper nitrogen management in crops is crucial to ensure optimal growth and yield maximization. While hyperspectral imagery is often used for nitrogen status estimation in crops, it is not feasible for real-time applications due to the complexity and high cost associated with it. Much of the research utilizing RGB data for detecting nitrogen stress in plants relies on datasets obtained under laboratory settings, which limits its usability in practical applications. This study focuses on identifying nitrogen deficiency in maize crops using RGB imaging data from a publicly available dataset obtained under field conditions. We have proposed a custom-built vision transformer model for the classification of maize into three stress classes. Additionally, we have analyzed the performance of convolutional neural network models, including ResNet50, EfficientNetB0, InceptionV3, and DenseNet121, for nitrogen stress estimation. Our approach involves transfer learning with fine-tuning, adding layers tailored to our specific application. Our detailed analysis shows that while vision transformer models generalize well, they converge prematurely with a higher loss value, indicating the need for further optimization. In contrast, the fine-tuned CNN models classify the crop into stressed, non-stressed, and semi-stressed classes with higher accuracy, achieving a maximum accuracy of 97% with EfficientNetB0 as the base model. This makes our fine-tuned EfficientNetB0 model a suitable candidate for practical applications in nitrogen stress detection. Full article

Grapevine rootstocks are gaining importance in viticulture as a strategy to combat abiotic challenges, as well as enhance scion physiology. Direct leaf-level physiological parameters like net assimilation rate, stomatal conductance to water vapor, quantum yield of PSII, and transpiration can illuminate the rootstock effect on scion physiology. However, these measures are time-consuming and limited to leaf-level analysis. This study used different rootstocks to investigate the potential application of aerial hyperspectral imagery in the estimation of canopy level measurements. A statistical framework was developed as an ensemble stacked regression (REGST) that aggregated five different individual machine learning algorithms: Least absolute shrinkage and selection operator (Lasso), Partial least squares regression (PLSR), Ridge regression (RR), Elastic net (ENET), and Principal component regression (PCR) to optimize high-throughput assessment of vine physiology. In addition, a Convolutional Neural Network (CNN) algorithm was integrated into an existing REGST, forming a hybrid CNN-REGST model with the aim of capturing patterns from the hyperspectral signal. Based on the findings, the performance of individual base models exhibited variable prediction accuracies. In most cases, Ridge Regression (RR) demonstrated the lowest test Root Mean Squared Error (RMSE). The ensemble stacked regression model (REGST) outperformed the individual machine learning algorithms with an increase in R² by (0.03 to 0.1). The performances of CNN-REGST and REGST were similar in estimating the four different traits. Overall, these models were able to explain approximately 55–67% of the variation in the actual ground-truth data. This study suggests that hyperspectral features integrated with powerful AI approaches show great potential in tracing functional traits in grapevines. Full article

Total nitrogen (TN) is a critical nutrient for plant growth, and its monitoring in agricultural soil is vital for farm managers. Traditional methods of estimating soil TN levels involve laborious and costly chemical analyses, especially when applied to large areas with multiple sampling points. Remote sensing offers a promising alternative for identifying, tracking, and mapping soil TN levels at various scales, including the field, landscape, and regional levels. Spaceborne hyperspectral sensing has shown effectiveness in reflecting soil TN levels. This study evaluates the efficiency of spectral reflectance at visible near-infrared (VNIR) and shortwave near-infrared (SWIR) regions to identify the most informative hyperspectral bands responding to the TN content in agricultural soil. In this context, we used PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral imagery with ensemble learning modeling to identify N-specific absorption features. This ensemble consisted of three multivariate regression techniques, partial least square (PLSR), support vector regression (SVR), and Gaussian process regression (GPR) learners. The soil TN data (n = 803) were analyzed against a hyperspectral PRISMA imagery to perform spectral band selection. The 803 sampled data points were derived from open-access soil property and nutrient maps for Africa at a 30 m resolution over a bare agricultural field in southern Morocco. The ensemble learning strategy identified several bands in the SWIR in the regions of 900–1300 nm and 1900–2200 nm. The models achieved coefficient-of-determination values ranging from 0.63 to 0.73 and root-mean-square error values of 0.14 g/kg for PLSR, 0.11 g/kg for SVR, and 0.12 g/kg for GPR, which had been boosted to an R² of 0.84, an RMSE of 0.08 g/kg, and an RPD of 2.53 by the ensemble, demonstrating the model’s accuracy in predicting the soil TN content. These results underscore the potential for using spaceborne hyperspectral imagery for soil TN estimation, enabling the development of decision-support tools for variable-rate fertilization and advancing our understanding of soil spectral responses for improved soil management. Full article

The VIIRS instrument on the JPSS-2 (renamed NOAA-21 upon reaching orbit) spacecraft was launched in November 2022, making it the third sensor in the VIIRS series, following those onboard the SNPP and NOAA-20 spacecrafts, which are operating nominally. As a multi-disciplinary instrument, the VIIRS provides the worldwide user community with high-quality imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans. This study provides an early assessment of the calibration stability and radiometric consistency of the NOAA-21 VIIRS RSBs using the latest NASA SIPS C2 L1B products. Vicarious approaches are employed, relying on reflectance data obtained from the Libya-4 desert and Dome C sites, deep convective clouds, and simultaneous nadir overpasses, as well as intercomparison with the first two VIIRS instruments using MODIS as a transfer radiometer. The impact of existing band spectral differences on sensor-to-sensor comparison is corrected using scene-specific a priori hyperspectral observations from the SCIAMACHY sensor onboard the ENVISAT platform. The results indicate that the overall radiometric performance of the newly launched NOAA-21 VIIRS is quantitatively comparable to the NOAA-20 for the VIS and NIR bands. For some SWIR bands, the measured reflectances are lower by more than 2%. An upward adjustment of 6.1% in the gain of band M11 (2.25 µm), based on lunar intercomparison results, generates more consistent results with the NOAA-20 VIIRS. Full article

The objective of cross-scene hyperspectral image (HSI) classification is to develop models capable of adapting to the “domain gap” that exists between different scenes, enabling accurate object classification in previously unseen scenes. Many researchers have devised various domain adaptation techniques aimed at aligning the statistical or spectral distributions of data from diverse scenes. However, many previous studies have overlooked the potential benefits of incorporating spatial topological information from hyperspectral imagery, which could provide a more accurate representation of the inherent data structure in HSIs. To overcome this issue, we introduce an innovative approach for cross-scene HSI classification, founded on hierarchical prototype graph alignment. Specifically, this method leverages prototypes as representative embedded representations of all samples within the same class. By employing multiple graph convolution and pooling operations, multi-scale domain alignment is attained. Beyond statistical distribution alignment, we integrate graph matching to effectively reconcile semantic and topological information. Experimental results on several datasets achieve significantly improved accuracy and generalization capabilities for cross-scene HSI classification tasks. Full article

Despite its high importance for crop yield prediction and monitoring, early-season crop mapping is severely hampered by the absence of timely ground truth. To cope with this issue, this study aims at evaluating the capability of PRISMA hyperspectral satellite images compared with Sentinel-2 multispectral imagery to produce early- and in-season crop maps using consolidated machine and deep learning algorithms. Results show that the accuracy of crop type classification using Sentinel-2 images is meaningfully poor compared with PRISMA (14% in overall accuracy (OA)). The 1D-CNN algorithm, with 89%, 91%, and 92% OA for winter, summer, and perennial cultivations, respectively, shows for the PRISMA images the highest accuracy in the in-season crop mapping and the fastest algorithm that achieves acceptable accuracy (OA 80%) for the winter, summer, and perennial cultivations early-season mapping using PRISMA images. Moreover, the 1D-CNN algorithm shows a limited reduction (6%) in performance, appearing to be the best algorithm for crop mapping within operational use in cross-farm applications. Machine/deep learning classification algorithms applied on the test fields cross-scene demonstrate that PRISMA hyperspectral time series images can provide good results for early- and in-season crop mapping. Full article