Multispectral Datasets Research Articles

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

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.

Land Cover Classification of Multispectral LiDAR Data With an Efficient Self-Attention Capsule Network

Periodically conducting land cover mapping plays a vital role in monitoring the status and changes of the land use. The up-to-date and accurate land use database serves importantly for a wide range of applications. This letter constructs an efficient self-attention capsule network (ESA-CapsNet) for land cover classification of multispectral light detection and ranging (LiDAR) data. First, formulated with a novel capsule encoder–decoder architecture, the ESA-CapsNet performs promisingly in extracting high-level, informative, and strong feature semantics for pixel-wise land cover classification by using the five types of rasterized feature images. Furthermore, designed with a novel capsule-based attention module, the channel and spatial feature encodings are comprehensively exploited to boost the feature saliency and robustness. The ESA-CapsNet is evaluated on two multispectral LiDAR data sets and achieves an advantageous performance with the overall accuracy, average accuracy, and kappa coefficient of over 98.42%, 95.15%, and 0.9776, respectively. Comparative experiments with the existing methods also demonstrate the effectiveness and applicability of the ESA-CapsNet in land cover classification tasks.

Built-up area analysis using Sentinel data in metropolitan areas of Transylvania, Romania

The anthropic and natural elements have become more closely monitored and analysed through the use of remote sensing and GIS applications. In this regard, the study aims to feature a different approach to produce more and more thematic information, focusing on the development of built-up areas. In this paper, multispectral images and Synthetic Aperture Radar (SAR) images were the basis of a wide range of proximity analyses. These allow the extraction of data about the distribution of built-up space on the areas with potential for economic and social development. Application of interferometric coherence and supervised classifications have been accomplished on various territories, such as metropolitan areas of the most developed region of Romania, more specifically Transylvania. The results indicate accuracy values, which can reach 94 per cent for multispectral datasets and 93 per cent for SAR datasets. The accuracy of resulted data will reveal a variety of city patterns, depending mainly on local features regarding natural and administrative environments. In this way, a comparison will be made between the accuracy of both datasets to provide an analysis of the manner of built-up areas distribution to assess the expansion of the studied metropolitan areas. Therefore, this study aims to apply well-established methods from the remote sensing field to enhance the information and datasets in some areas lacking recent research.

A robust and fast multispectral pedestrian detection deep network

Multispectral pedestrian detection is a difficult task, especially with pedestrian images of different sizes. In most convolutional neural network (CNN) models, the shared receptive fields of each layer are of the same size, which constrains detection results of multiple scales pedestrians. In this paper, we propose a dynamic selection scheme to adaptive adjust receptive field size in multispectral pedestrian detection. Specifically, a network in network (NIN) is employed to combine visible and thermal information. Selective kernel networks (SKNets) which uses selective kernel unit with different kernel size are employed. To effectively fuse the feature representation in each layer, a build block is designed, in which different features are fused. Feature pyramid is employed to integrate feature information in each layer. We empirically show that our method outperforms existing 8 state-of-the-art methods on one multispectral dataset and 4 state-of-the-art methods on another multispectral dataset. Detailed analyses show that our proposed method can capture multispectral pedestrian detection of different scales, which confirms the effective of SKNets for adaptively resizing the receptive field sizes. In addition, our method can operate at 14 frames per second (fps) on a GPU.

A novel inequality-constrained weighted linear mixture model for endmember variability

Spectral unmixing has trigged considerable attention to both multispectral and hyperspectral data processing. Among different kinds of spectral unmixing models, the linear mixture model (LMM) has been widely applied with its simplicity in light scattering mechanisms and flexibility in different scenarios. Recently, a variety of optimized LMMs have been developed to enhance the decomposing ability. However, two major challenges still exist: endmember variability and global optimum solution with the inequality constraint. In this study, aiming to avoid the influence of endmember variability and to extend the application of LMM, a novel inequality-constrained weighted linear mixture model (IWLMM) is proposed. Based on the errors-in-variables (EIV) model and the theory of weighted total least squares (WTLS), the issue of endmember variability and stochastic errors of both endmembers and mixed pixels are eliminated. With the non-negativity constraint, a brief optimal algorithm to solve the IWLMM is generated as well. In order to indicate the advantages of proposed IWLMM, comparative experiments with the conventional fully constrained least squares (FCLS), three currently proposed LMM-based models (perturbed LMM (PLMM), extended LMM (ELMM) and augmented LMM (ALMM)) and three physics-based nonlinear mixture models (bilinear-Fan model (BFM), generalized bilinear model (GBM) and polynomial post-nonlinear model (PPNM)) on four synthetic and real scenarios with hyperspectral and multispectral datasets were performed. The experimental results suggest the IWLMM outperforms the other seven linear and nonlinear unmixing models after adding the correction for endmember variability by modeling the perturbation and scaling factors of spectral features simultaneously. It has an impressive ability for decomposing and reconstructing pixels.

Extraction of Spectral Information from Airborne 3D Data for Assessment of Tree Species Proportions

With the rapid development of photogrammetric software and accessible camera technology, land surveys and other mapping organizations now provide various point cloud and digital surface model products from aerial images, often including spectral information. In this study, methods for colouring the point cloud and the importance of different metrics were compared for tree species-specific estimates at a coniferous hemi-boreal test site in southern Sweden. A total of three different data sets of aerial image-based products and one multi-spectral lidar data set were used to estimate tree species-specific proportion and stem volume using an area-based approach. Metrics were calculated for 156 field plots (10 m radius) from point cloud data and used in a Random Forest analysis. Plot level accuracy was evaluated using leave-one-out cross-validation. The results showed small differences in estimation accuracy of species-specific variables between the colouring methods. Simple averages of the spectral metrics had the highest importance and using spectral data from two seasons improved species prediction, especially deciduous proportion. Best tree species-specific proportion was estimated using multi-spectral lidar with 0.22 root mean square error (RMSE) for pine, 0.22 for spruce and 0.16 for deciduous. Corresponding RMSE for aerial images was 0.24, 0.23 and 0.20 for pine, spruce and deciduous, respectively. For the species-specific stem volume at plot level using image data, the RMSE in percent of surveyed mean was 129% for pine, 60% for spruce and 118% for deciduous.

JU-VNT: a multi-spectral dataset of indoor object recognition using visible, near-infrared and thermal spectrum

Detecting objects in natural scenes can be a very challenging task. In several real-life scenarios it is often found that visible spectrum is not ideal for typical computer vision tasks. Going beyond the range of visible light spectrum, such as the near infrared spectrum or the thermal spectrum allows us to capture many unique properties of objects that normally not captured with a normal camera. In this work we propose two multi-spectral dataset with three different spectrum, namely, the visible, near infrared and thermal spectrum. The first dataset is a single object dataset where we have common desk objects of 25 different categories comprising of various materials. The second dataset comprises of all possible combination using these 25 objects taking a pair at a time. The objects are captured from 8 different angles using the three different cameras. The images are registered and cropped and provided along with classification and localization ground truths. Additionally classification benchmarks have been provided using the ResNet, InceptionNet and DenseNet architectures on both the datasets. The dataset would be publicly available from https://github.com/DVLP-CMATERJU/JU-VNT .

MSMatch: Semisupervised Multispectral Scene Classification With Few Labels

Supervised learning techniques are at the center of many tasks in remote sensing. Unfortunately, these methods, especially recent deep learning methods, often require large amounts of labeled data for training. Even though satellites acquire large amounts of data, labeling the data is often tedious, expensive and requires expert knowledge. Hence, improved methods that require fewer labeled samples are needed. We present MSMatch, the first semi-supervised learning approach competitive with supervised methods on scene classification on the EuroSAT benchmark dataset. We test both RGB and multispectral images and perform various ablation studies to identify the critical parts of the model. The trained neural network achieves state-of-the-art results on EuroSAT with an accuracy that is between 1.98% and 19.76% better than previous methods depending on the number of labeled training examples. With just five labeled examples per class we reach 94.53% and 95.86% accuracy on the EuroSAT RGB and multispectral datasets, respectively. With 50 labels per class we reach 97.62% and 98.23% accuracy. Our results show that MSMatch is capable of greatly reducing the requirements for labeled data. It translates well to multispectral data and should enable various applications that are currently infeasible due to a lack of labeled data. We provide the source code of MSMatch online to enable easy reproduction and quick adoption.

A Generalized Tool for Accurate and Efficient Image Registration of UAV Multi-lens Multispectral Cameras by N-SURF Matching

The original multispectral (MS) images obtained from multi-lens multispectral cameras (MSCs) have significant misregistration errors, which require image registration for precise spectral measurement. However, due to the nonlinearity intensity differences among MS images, performing image matching is difficult to find sufficient correct matches (CMs) for image registration, and results in a complex coarse-to-fine solution. Based on the modification of speed-up robust feature (SURF), we proposed a normalized SURF (N-SURF) that can significantly increase the amount of CMs among different pairs of MS images and make one-step image registration possible. In this study, we first introduce N-SURF and adopt different MS datasets acquired from three representative MSCs (MCA-12, Altum, and Sequoia) to evaluate its matching ability. Meanwhile, we utilized three image transformation models—affine transform (AT), projective transform (PT), and an extended projective transform (EPT) to correct the misregistration errors of MSCs and evaluate their co-registration correctness. The results show that N-SURF can obtain 6–20 times more CMs than SURF and can successfully match all pairs of MS images, while SURF failed in the cases of significant spectral differences. Moreover, visual comparison, accuracy assessment, and residual analysis show that EPT can more accurately correct the viewpoint and lens distortion differences of MSCs than AT and PT, and it can obtain co-registration accuracy of 0.2–0.4 pixels. Subsequently, using the successful N-SURF matching and EPT model, we developed an automatic MS image registration tool that is suitable for various multilens MSCs.

Deep Domain Adaptation Based Multi-Spectral Salient Object Detection

Salient Object Detection (SOD) plays an important role in many image-related multimedia applications. Although there are many existing research works about the salient object detection in traditional RGB (visible-light spectrum) images, there are still many complex situations that regular RGB images cannot provide enough cues for the accurate SOD, such as the shadow effect, similar appearance between background and foreground, strong or insufficient illumination, etc. Because of the success of near-infrared spectrum in many computer vision tasks, we explore the multi-spectral SOD in the synchronized RGB images and near-infrared (NIR) images for the both simple and complex situations. We assume that the RGB SOD in the existing RGB image datasets could provide references for the multi-spectral SOD problem. In this paper, we mainly model this research problem as a deep learning based domain adaptation from the traditional RGB image data (source domain) to the multi-spectral data (target domain), and an adversarial deep domain adaptation model is proposed. We first collect and will publicize a large multi-spectral dataset, RGBN-SOD dataset, including 780 synchronized RGB and NIR image pairs for the multi-spectral SOD problem in the simple and complex situations. Intensive experimental results show the effectiveness and accuracy of the proposed deep domain adaptation for the multi-spectral SOD. Besides, due to the absence of research on the field of multi-spectral co-saliency detection, we also collect 200 synchronized RGB and NIR image pairs in addition to explore the multi-spectral co-saliency detection.

Spectral Unmixing for Mapping a Hydrothermal Field in a Volcanic Environment Applied on ASTER, Landsat-8/OLI, and Sentinel-2 MSI Satellite Multispectral Data: The Nisyros (Greece) Case Study

The aim of this study was to propose a methodology that provides a detailed description of the argillic zone of a hydrothermal field, based on satellite multispectral data. More specifically, we developed a method based on spectral unmixing where hydroxyl-bearing alteration is represented by a single endmember (representing clays) and the three (nearly) non-altered primary volcanic lithologies, namely, two types of lava flows (basic and acidic compositions) and the loose materials (alluvial/beach deposits, scree, pyroclastic deposits, etc.), are represented by three endmembers. We also used one endmember representing elemental sulfur that is present in fumarolic vents hosted by active hydrothermal craters. The methodology was applied in the south part of Lakki plain inside the Nisyros volcano caldera (Greece), using Sentinel-2, Landsat-8/OLI, and ASTER satellite multispectral datasets. Specifically, it was applied separately to each one of the three datasets. The spectral unmixing results, combined with the relative geological map, provide quantitative estimations of the primary volcanic and loose material areas affected by alteration. In addition, pixels with high abundance values of hydroxyl-bearing alteration corresponded to mapped areas with strong hydrothermal alteration. The developed methodology is superior to conventional approaches (e.g., alteration spectral index) in terms of its ability to describe the overall pattern of the hydrothermal field. The most accurate results were taken when applied to ASTER or Sentinel-2 MSI data.

Radiometric mapping and spectral based classification of rocks using remote sensing data analysis: The Precambrian basement complex, NW Nigeria

In this study, radiometric datasets which are best interpreted in conjunction with multispectral remote sensing data were applied in discriminating and classifying the lithologies of the Kebbi–Sokoto basement terrain for effective lithological mapping of the region. K/Th/U radiometric and Landsat-8 remote sensing data were enhanced through several processing techniques to display the characteristics of surface responses needed as input parameters in the classification of the surface rocks. Potassium radioelement concentration, Th/K channel ratio, normalization and ternary maps were produced using radiometric data while band combination and band ratio images were obtained from the Landsat-8 data. Several band ratios highlighted the major rock–forming minerals in the area such as feldspar, quartz, hematite, goethite, illite, kaolinite, biotite, amphibolite and dolomite. The major lithological expressions deduced from the combined data analysis namely; sandstone, felsic gneiss, clay, migmatite, granitoid and carbonates were used as representative classes to train the classification algorithm for the lithological interpretation. Supervised classification algorithm employing maximum likelihood classifier was used to obtain the lithological classification for basement rock units in the region. The classification results which were tested on band ratio b6/b2, b6/b7, b6/b5*b4/b5 image, b4/b2, b5/b6, b6/b7 image and b4/b2, b6/b7, b6/b5 image using a contingency table produced overall classification accuracy values of 79%, 92%, and 88% respectively. The classified images displayed quite similar geological feature discrimination with the radiometric ternary map and geological map of the area. The study emphasized the benefit of using geophysical and multispectral datasets to produce a more complete classification of the surficial Precambrian rocks exposed in the region.

Abstract 15508: Random Forest Classifier-incoporated Intravascular Optical Coherence Tomography-fluorescence Lifetime Imaging (oct-flim) Provides Automated Characterization of Key Biochemical Components of Coronary Atherosclerotic Plaques

Introduction: Fluorescence lifetime imaging (FLIm) allows label-free biochemical characterization of atheroma, however, multispectral FLIm produces massive data throughput making image interpretation problematic. We investigated whether machine learning algorithm can be applied to intravascular OCT-FLIm for automated biochemical characterization of coronary plaques. Methods and Results: We built a fully-integrated, high-speed OCT-FLIm system and a low-profile, dual-modal imaging catheter that can provide high-resolution OCT images and correctly co-registered multispectral fluorescence lifetime (FL) readouts: ch.1 and ch.2 FL, and FL intensity ratio (ch.2/ch.1). Rapid intracoronary imaging (10-20 mm/s, 100 rps) was safely performed in atheromatous pigs. There were significant differences in FL measurements according to plaque types (high-risk vs. fibrotic: p<0.001). Multispectral FL measurements sampled selectively from histologically-proven plaque components (lipid, macrophage, lipid+macrophage, SMC) were analyzed. Each component was distinguishable from one another either by difference of FLs or intensity ratio. Random forest classifier (RFC), trained with component-labeled multispectral FL dataset, accurately classified key biochemical components of atherosclerotic plaques. RFC-determined biochemical characteristics of target plaque were consistent across two repeated imaging data (intraclass correlation, p<0.0001) and corroborated closely with those derived from quantitative immunohistochemistries. Conclusions: Our OCT-FLIm incorporating RFC-based systematic multispectral FL analysis could provide high-resolution plaque imaging with automated biochemical characterization in beating coronary environment. The present imaging strategy enabling comprehensive characterization of multiple components of atherosclerotic plaques will open a new avenue in the field of cardiovascular imaging.

Mangrove classification using airborne hyperspectral AVIRIS-NG and comparing with other spaceborne hyperspectral and multispectral data

Application of remote sensing makes the assessment and monitoring of mangroves both time and cost-effective. In this study, the capacity of AVIRIS-NG data in discriminating different mangrove species of Lothian Island of Indian Sundarbans has been evaluated and compared with hyperspectral (Hyperion) and multispectral dataset (Landsat 8 OLI and Sentinel-2). Spectral signatures of mangrove species were retrieved, and spectral libraries were created. With the corrected images and spectral libraries, mangroves were classified using appropriate classification techniques. For multispectral datasets (Landsat 8 OLI and Sentinel-2) and hyperspectral coarser-resolution Hyperion datasets, K-means classification followed by knowledge-based classification was adopted. For fine resolution hyperspectral AVIRIS-NG dataset, classification was accomplished using Support Vector Machine (SVM). The overall accuracy for the classification is significantly high in case of AVIRIS-NG data (87.61%) compared to the Landsat 8 OLI (76.42%), Sentinel-2 (79.81%), and Hyperion data (81.98%). The results showed that AVIRIS-NG hyperspectral dataset has the potential to classify not only the genus level but also species-level with satisfactory accuracy in a complex mangrove forest.

Estimation of land-use/land-cover changes associated with energy footprints and other disturbance agents in the Upper Peace Region of Alberta Canada from 1985 to 2015 using Landsat data

Quantification of Land‐Use and Land‐Cover (LULC) changes associated with energy extraction footprints plays an important role in reclamation performance assessment, compliance monitoring, and sustainable land management practices. For regulatory planning and decision making purposes, it is crucial to understand the types and representation of disturbance present on the landscape related to energy footprints (i.e., oil, gas, and coal mining activities), non-energy footprints (i.e., other infrastructure), cutblocks, and wildfire. In this study, Landsat multispectral datasets were analysed using advanced classification and unmixing algorithms to calculate LULC changes associated with these disturbance agents from 1985 to 2015 in the Upper Peace Region, one of the major oil and gas exploration sites in Alberta, Canada. Based on 5-year intervals throughout this 30-year study, seven epochal LULC maps and a cumulative land disturbance map were produced with 82 % and 89 % overall accuracies, respectively, based on extensive and multisource, independent validation. Assessment of multiple disturbance regimes and time-series trajectories with high accuracies in this complex environment was vital to document and quantify disturbance, vegetation re-establishment, succession, and reclamation. Results indicate that more than 60 % forest loss (coniferous and deciduous) occurred due to harvesting and less than 20 % forest loss occurred due to energy and non‐energy footprints. An assessment of the current state showed 40–60 % of cutblocks and burned areas returned as immature forest and wetland and 15–20 % recovered to forest, accounting for successional processes that take time for the re-establishment of trees following disturbance. Out of total energy footprints, 8 % recovered as shrubland and 16 % recovered as forest, with almost no sign of vegetation recovery evident in non‐energy footprints (e.g., urban and long term industrial land use). The highest area of land disturbance (3412 km2) was over coniferous forest with 5-year disturbance areas of 55 km2, 65 km2, 70 km2, and 295 km2, associated with, respectively, wildfire, energy development, non‐energy footprints, and harvesting. This type of LULC investigation informs land‐use threshold management for energy footprints by identifying and partitioning key disturbance agents into land use. Knowledge of change type and land use enables insights on long term implications of the changes present and serves to guide sustainable development, environmental protection, and informing forward looking land use future state simulations.

Suitability of resampled multispectral datasets for mapping flowering plants in the Kenyan savannah.

Pollination services and honeybee health in general are important in the African savannahs particularly to farmers who often rely on honeybee products as a supplementary source of income. Therefore, it is imperative to understand the floral cycle, abundance and spatial distribution of melliferous plants in the African savannah landscapes. Furthermore, placement of apiaries in the landscapes could benefit from information on spatiotemporal patterns of flowering plants, by optimising honeybees' foraging behaviours, which could improve apiary productivity. This study sought to assess the suitability of simulated multispectral data for mapping melliferous (flowering) plants in the African savannahs. Bi-temporal AISA Eagle hyperspectral images, resampled to four sensors (i.e. WorldView-2, RapidEye, Spot-6 and Sentinel-2) spatial and spectral resolutions, and a 10-cm ultra-high spatial resolution aerial imagery coinciding with onset and peak flowering periods were used in this study. Ground reference data was collected at the time of imagery capture. The advanced machine learning random forest (RF) classifier was used to map the flowering plants at a landscape scale and a classification accuracy validated using 30% independent test samples. The results showed that 93.33%, 69.43%, 67.52% and 82.18% accuracies could be achieved using WorldView-2, RapidEye, Spot-6 and Sentinel-2 data sets respectively, at the peak flowering period. Our study provides a basis for the development of operational and cost-effective approaches for mapping flowering plants in an African semiarid agroecological landscape. Specifically, such mapping approaches are valuable in providing timely and reliable advisory tools for guiding the implementation of beekeeping systems at a landscape scale.

TIRNet: Object detection in thermal infrared images for autonomous driving

In the present study, towards reliable and efficient object detection in thermal infrared (TIR) images, we put forward a novel object detection approach, termed TIRNet, which is built upon convolutional neural network (CNN). Instead of using the deep CNN backbone (ResNet, ResNeXt) which suffers low speed and high computational cost, the lightweight feature extractor (VGG) is adopted. To get the robust and discriminating features for accurate box regression and classification, the Residual Branch is introduced. More uniquely, it only exists in the training phase, so no any additional time is increased when inference. All the computation is encapsulated in a single network, so our TIRNet can be optimized and tested in the manner of end-to-end. Furthermore, the continuous information fusion strategy is proposed for improving detection performance, which can effectively solve the problems such as complex background, occlusion, and get more accurate and smoother detection results. To get the real-world dataset and effectively evaluate the effectiveness, a China Thermal Infrared (CTIR) dataset is collected. Besides, we also evaluate our proposed approach on the public KAIST Multispectral dataset. As demonstrated in the comparative experiments, our approach gets the state-of-the-art detection accuracy while maintains high detection efficiency.

Satellite imagery and spectral matching for improved estimation of calcium carbonate and iron oxide abundance in mine areas

This study attempts grade-wise mapping in a limestone mine in Ariyalur, Southern India, and in the iron ore mines of Noamundi, Eastern India. After noise removal in the Sentinel 2A (multispectral) and EO-1 Hyperion (hyperspectral) image datasets, spectral matching is performed using the Jeffries-Matusita (JM) distance, Spectral Correlation Mapper (SCM), and combined JM-SCM measure. Due to the specific absorption spectra for carbonates (1900 nm, 2000 nm, and 2160 nm) and iron oxide (865 nm), it is possible to identify and map such mineral deposits using the multispectral dataset (Sentinel 2A) and hyperspectral dataset (EO-1 Hyperion) respectively. The grade-wise mapping of carbonate in the Ariyalur mine using the Sentinel 2A dataset by the Jeffries-Matusita (JM) approach and Spectral Correlation Mapper (SCM) yielded R2 values of 0.44 and 0.77 respectively, whereas the combined JM-SCM approach resulted in a higher correlation with an R2 value of 0.87. The grade-wise mapping of iron oxide in Noamundi using the EO-1 Hyperion dataset by the Jeffries-Matusita (JM) approach and the Spectral Correlation Mapper (SCM) approach yielded R2 values of 0.15 and 0.76, respectively, whereas the combined JM-SCM approach resulted in a higher correlation with an R2 value of 0.90. Such an improved performance of the combined approach is primarily due to the simultaneous and effective utilization of band-wise information (by JM) and correlation aspects (by SCM) of the reference and target spectra considered in the matching algorithm. Thus, in this study, the proposed algorithm proved its compatibility and utility in extracting information on mineral abundance distribution for mine areas.

DRONE DATA ATMOSPHERIC CORRECTION CONCEPT FOR MULTI- AND HYPERSPECTRAL IMAGERY – THE DROACOR MODEL

Abstract. Remote sensing with unmanned aerial vehicles (UAVs) is a fast and cost-efficient tool for mapping and environmental monitoring. The sensors are operated at low flight altitudes, usually below 500 m above ground, leading to spatial resolutions up to the centimeter range. This type of data causes new challenges in atmospheric compensation and surface reflectance retrieval. Based on these specific boundary conditions, a new drone based atmospheric correction concept (DROACOR) is proposed, which is designed for currently available UAV based sensors. It is suited for multispectral visible/near infrared sensors as well as hyperspectral instruments covering the 400–1000 nm spectral region or the 400–2500 nm spectrum. The goal of the development is a fully automatic processor which dynamically adjusts to the given instrument and the atmospheric conditions. Optionally, irradiance measurements from simultaneously measured cosine receptors or from in-field reference panels can be taken into account to improve the processing quality by adjusting the irradiance parameter or by performing an in-flight vicarious calibration. Examples of DROACOR processing results are presented for a multispectral image data set and a hyperspectral data set, both acquired at variable flight altitudes. The resulting spectra show the applicability of the methods for both sensor types and an accuracy level below 2.5% reflectance units.

Assessment of Leaf Chlorophyll Content Models for Winter Wheat Using Landsat-8 Multispectral Remote Sensing Data

The leaf chlorophyll content (LCC) is a critical index to characterize crop growth conditions, photosynthetic capacity, and physiological status. Its dynamic change characteristics are of great significance for monitoring crop growth conditions and understanding the process of material and energy exchange between crops and the environment. Extensive research has focused on LCC retrieval with hyperspectral data onboard various sensor platforms. Nevertheless, limited attention has been paid to LCC inversion from multispectral data, such as the data from Landsat-8, and the potentials and capabilities of the data for crop LCC estimation have not been fully explored. The present study made use of Landsat-8 Operational Land Imager (OLI) imagery and the corresponding field experimental data to evaluate their capabilities and potentials for LCC modeling using four different retrieval methods: vegetation indices (VIs), machine learning regression algorithms (MLRAs), lookup-table (LUT)-based inversion, and hybrid regression approaches. The results showed that the modified triangular vegetation index (MTVI2) exhibited the best estimate accuracy for LCC retrieval with a root mean square error (RMSE) of 5.99 μg/cm2 and a relative RMSE (RRMSE) of 10.49%. Several other vegetation indices that were established from red and near-infrared (NIR) bands also exhibited good accuracy. Models established from Gaussian process regression (GPR) achieved the highest accuracy for LCC retrieval (RMSE = 5.50 μg/cm2, RRMSE = 9.62%) compared with other MLRAs. Moreover, red and NIR bands outweighed other bands in terms of GPR modelling. LUT-based inversion methods with the “K(x) = −log (x) + x” cost function that belongs to the “minimum contrast estimates” family showed the best estimation results (RMSE = 8.08 μg/cm2, RRMSE = 14.14%), and the addition of multiple solution regularization strategies effectively improved the inversion accuracy. For hybrid regression methods, the use of active learning (AL) techniques together with GPR for LCC modelling significantly increased the estimation accuracy, and the combination of entropy query by bagging (EQB) AL and GPR had the best accuracy for LCC estimation (RMSE = 12.43 μg/cm2, RRMSE = 21.77%). Overall, our study suggest that Landsat-8 OLI data are suitable for crop LCC retrieval and could provide a basis for LCC estimation with similar multispectral datasets.