Panchromatic Imagery Research Articles

Pansharpening through orthogonal projection of data

With the increase in the amount of satellite data particularly in the form of satellite images, the need to fuse heterogeneous imagery has become an important research area. Pansharpening is an image fusion method that involves fusing a high spatial resolution panchromatic imagery and a high spectral resolution multispectral imagery to obtain an image that possesses spatial and spectral data both in high resolution. In this paper, a pansharpening method based on a classical information-theoretic result of orthogonal projection between two sets of correlated data is proposed. The originality of the study lies in the application of the information-theoretic approach to pansharpening which has not been reported to date. The proposed method which is illustrated using IKONOS data is also compared favorably with existing pansharpening methods such as IHS, Brovey, PCA, SFIM, HPF, and Multi methods using standard evaluation criteria, such as Chi-square test (X2), R2 test, RMSE, SNR, spectral discrepancy (SD) and ERGAS.

Joint Panchromatic and Multispectral Geometric Calibration Method for the DS-1 Satellite

The DS-1 satellite was launched successfully on 3 June 2021 from the Taiyuan Satellite Launch Center. The satellite is equipped with a 1 m panchromatic and a 4 m multispectral sensor, providing high-resolution and wide-field optical remote sensing imaging capabilities. For satellites equipped with panchromatic and multispectral sensors, conventional geometric processing methods in the past involved separate calibration for the panchromatic sensor and the multispectral sensor. This method produced distinct internal and external calibration parameters in the respective bands, and also resulted in nonlinear geometric misalignments between the panchromatic and multispectral images due to satellite chattering and other factors. To better capitalize on the high spatial resolution of panchromatic imagery and the superior spectral resolution of multispectral imagery, it is necessary to perform registration on the calibrated panchromatic and multispectral images. When registering separately calibrated panchromatic and multispectral images, poor consistency between panchromatic and multispectral images leads to a small number of corresponding points, resulting in poor accuracy and registration effects. To address this issue, we propose a joint panchromatic and multispectral calibration method to register the panchromatic and multispectral images. Before geometric calibration, it is necessary to perform corresponding points matching. When matching, the small interval between the panchromatic and multispectral Charge-Coupled Devices (CCDs) results in a small intersection angle of the corresponding points between the panchromatic and multispectral images. As a result of this, the consistency between the spectral bands significantly improves, and the corresponding points match to have a more uniform distribution and a wider coverage. The technique enhances the consistent registration accuracy of both the panchromatic and multispectral bands. Experiments demonstrate that the joint calibration method yields a registration accuracy of panchromatic and multispectral bands exceeding 0.3 pixels.

A method to pan-sharpen SAR amplitude data via electromagnetic reflectance similarities

ABSTRACT For a long time, the idea of fusing synthetic radar imagery (SAR) and higher-resolution panchromatic imagery, with the aim of increasing the spatial resolution of radar imagery, has been conceived as impossible. The challenge is due to the different natures of microwave radiation and visible radiation, where the former is the spectrum of SAR imagery, and the latter is of panchromatic imagery. Average reflection amplitude, which constitutes pixel values on both panchromatic and SAR imagery, is taken as a common framework around which this study proposes a method that enables the translation of an array on a panchromatic spectrum into an array of C-band microwave spectrum in order to achieve a higher spatial resolution. In this regard, open-access Worldview-2 and Worldview-3 panchromatic and Sentinel-1 (amplitude-only) images are used. The outputs are qualified using spatial correlation coefficient (SCC), net histogram curve, and R2 difference, and Erreur relative globale adimensionnelle de synthese (ERGAS) as full-reference image quality assessment methods in various scenarios. The results are tenable enough to consider this method for ordinary earth observation and monitoring objectives.

The Effectiveness of Pan-Sharpening Algorithms on Different Land Cover Types in GeoEye-1 Satellite Images.

In recent years, the demand for very high geometric resolution satellite images has increased significantly. The pan-sharpening techniques, which are part of the data fusion techniques, enable the increase in the geometric resolution of multispectral images using panchromatic imagery of the same scene. However, it is not trivial to choose a suitable pan-sharpening algorithm: there are several, but none of these is universally recognized as the best for any type of sensor, in addition to the fact that they can provide different results with regard to the investigated scene. This article focuses on the latter aspect: analyzing pan-sharpening algorithms in relation to different land covers. A dataset of GeoEye-1 images is selected from which four study areas (frames) are extracted: one natural, one rural, one urban and one semi-urban. The type of study area is determined considering the quantity of vegetation included in it based on the normalized difference vegetation index (NDVI). Nine pan-sharpening methods are applied to each frame and the resulting pan-sharpened images are compared by means of spectral and spatial quality indicators. Multicriteria analysis permits to define the best performing method related to each specific area as well as the most suitable one, considering the co-presence of different land covers in the analyzed scene. Brovey transformation fast supplies the best results among the methods analyzed in this study.

Deep learning models for river classification at sub-meter resolutions from multispectral and panchromatic commercial satellite imagery

Remote sensing of the Earth’s surface water is critical in a wide range of environmental studies, from evaluating the societal impacts of seasonal droughts and floods to the large-scale implications of climate change. Consequently, a large literature exists on the classification of water from satellite imagery. Yet, previous methods have been limited by (1) the >10 m spatial resolution of public satellite imagery, (2) classification schemes that operate at the pixel level, and (3) the need for multiple spectral bands. We advance the state-of-the-art by (1) using commercial satellite imagery with panchromatic and multispectral resolutions of ∼ 30 cm and ∼ 1.2 m, respectively, (2) developing multiple fully convolutional neural networks (FCN) that can learn the morphological features of water bodies in addition to their spectral properties, and (3) FCN that can classify water even from panchromatic imagery. This study focuses on rivers in the Arctic, using images from the Quickbird-2, WorldView-1, WorldView-2, WorldView-3, and GeoEye satellites. Because no training data are available at such high resolutions, we construct those manually. First, we use the red, green, blue, and near-infrared bands of the 8-band multispectral sensors. Those trained models all achieve excellent precision and recall over 90% on validation data, aided by on-the-fly preprocessing of the training data specific to satellite imagery. In a novel approach, we then use results from the multispectral model to generate training data for FCN that only require panchromatic imagery, of which considerably more is available. Despite the smaller feature space, these models still achieve a precision and recall of over 85%. We provide our open-source codes and trained model parameters to the remote sensing community, which paves the way to a wide range of environmental hydrology applications at vastly superior accuracies and 1–2 orders of magnitude higher spatial resolution than previously possible.

A decision-level fusion approach to tree species classification from multi-source remotely sensed data

In this study, an object-oriented, decision-level fusion method is proposed for tree species classification based on spectral, textural, and structural features derived from multi-spectral and panchromatic imagery and Light Detection And Ranging (LiDAR) data. Murphy's average method based on the Dempster Shafer theory (DST) was used to calculate the combined mass function for decision making purposes. For individual feature groups, the mass functions were calculated using the support vector machine (SVM) classification method. The species examined included Norway maple, honey locust, Austrian pine, blue spruce, and white spruce. In addition to these species, a two- or three-species compound class was included in the decision process based on the normalized entropy in the presence of conflict that was itself determined according to whether individual groups of features were consistent. The developed method provided a mechanism to identify tree crowns, which could not be classified to one single species with a high confidence due to the conflict among feature groups. Data used in this study were obtained for the Keele Campus of York University, Toronto, Ontario. Among the 223 test crowns, 204 crowns were assigned to one single species, and the overall classification accuracy was 0.89. A decision could not be made for 19 crowns with confidence, and as a result, a two- or three-species compound class was assigned. The classification accuracy was higher than that obtained using SVM classification based on individual and combined spectral, structural, and textural features.

Fusion Methods and Multi-classifiers to Improve Land Cover Estimation Using Remote Sensing Analysis

Adopting a low spatial resolution remote sensing imagery to get an accurate estimation of Land Use Land Cover is a difficult task to perform. Image fusion plays a big role to map the Land Use Land Cover. Therefore, This study aims to find out a refining method for the Land Use Land Cover estimating using these steps; (1) applying a three pan-sharpening fusion approaches to combine panchromatic imagery that has high spatial resolution with multispectral imagery that has low spatial resolution, (2) employing five pixel-based classifier approaches on multispectral imagery and fused images; artificial neural net, support vector machine, parallelepiped, Mahalanobis distance and spectral angle mapper, (3) make a statistical comparison between image classification results. The Landsat-8 image was adopted for this research. There are twenty Land Use Land Cover thematic maps were generated in this study. A suitable and reliable Land Use Land Cover method was presented based on the most accurate results. The results validation was performed by adopting a confusion matrix method. A comparison made between the images classification results of multispectral imagery and all fused images levels. It proved the Land Use Land Cover map produced by Gram–Schmidt Pan-sharpening and classified by support vector machine method has the most accurate result among all other multispectral imagery and fused images that classified by the other classifiers, it has an overall accuracy about (99.85%) and a kappa coefficient of about (0.98). However, the spectral angle mapper algorithm has the lowest accuracy compared to all other adopted methods, with overall accuracy of 53.41% and the kappa coefficient of about 0.48. The proposed procedure is useful in the industry and academic side for estimating purposes. In addition, it is also a good tool for analysts and researchers, who could interest to extend the technique to employ different datasets and regions.

Historical land cover classification from CORONA imagery using convolutional neural networks and geometric moments

ABSTRACT Historical CORONA imagery, a very high-resolution panchromatic imagery captured during 1960-72, has a lot of unexplored potentials to represent the land cover of the pre-Landsat era. This pre-Landsat land cover can be a valuable input to many long-term environmental modelling problems. While automated binary change detection and multi-class classification by manual photo-interpretation have been the two most popular applications of CORONA imagery, automated multi-class classification of CORONA imagery has not been explored. In this research, we propose two supervised classification methods capable of extracting spatial features for the classification of CORONA imagery. The first method utilizes two-dimensional convolutional neural networks (2D-CNN) for classification, whereas the second method involves stacking the CORONA imagery with its texture features obtained through geometric moments (GM) and performing pixel-wise classification using the random forest (RF) classifier and is termed as GM-RF. The proposed methods are tested on two study sites – the critical zone observatory (CZO) managed by the Indian Institute of Technology Kanpur in Uttar Pradesh (India) and a site near Chhapra in Bihar (India). For estimating the capabilities of the proposed methods and to compare the proposed methods with other methods, overall accuracy and Cohen’s kappa coefficient (k) along with z-scores statistics are used. The effects of various hyperparameters (e.g. percentage of training data, patch size, order of geometric moments) on accuracy are also evaluated. The classification accuracies achieved through the 2D-CNN and GM-RF method in terms of ĸ are higher than 90% for both study sites indicating that the proposed methods can classify CORONA imagery with little domain knowledge about the study areas, and hence can be used in a variety of applications.

Validation of preliminary geometric positioning accuracy for China’s civil high-resolution surveying and mapping satellites: Ziyuan-3 01 and Gaofen-7 panchromatic imagery

ABSTRACT The positioning accuracy of Ziyuan-3 01 (ZY-3 01) and Gaofen-7 (GF-7) panchromatic imagery, obtained during the early deployment of the satellites, was verified. Particularly, this paper first validated the geometric positioning accuracy of GF-7 panchromatic imagery. Based on the rigorous geometric model of surveying and mapping satellites, this study applied a geometric calibration (GC) model using the Rodrigues rotation matrix. Also, we employed the self-calibrating bundle adjustment with an additional parameter model to compensate for the internal orientation distortion. The results show that the positioning accuracy can be largely improved with few ground control points. Especially, the horizontal accuracy (specified in x and y) and elevation accuracy of ZY-3 01 in the Anping area are around 4 m and 2 m, respectively, and that of GF-7 in the Beijing area can achieve 3.2 m, 2.8 m, and 1.2 m in the x, y, and z directions, respectively.

Learn to Optimize Panchromatic Imagery for Accurate Building Extraction

Due to the limited training data, current data-driven algorithms, including deep convolutional networks (DCNs), are susceptible to training data that cannot be applied to new data directly. Unlike existing methods that are trying to improve model generation capability using limited data, we introduce a learning-based image translation method to generate data that share the same characteristics of target data. The low-resolution panchromatic satellite images are converted into high-resolution color images through interpolation and colorization with the proposed symmetric colorization network (SCN). Experiments on a very-high-resolution (VHR) dataset show that images generated by our SCN are with both quantitatively and qualitatively high color fidelity. Furthermore, we also demonstrate that high extraction accuracy is retained during the model transferring from aerial to satellite images. For pre-trained feature pyramid network (FPN), compared to the performance on raw panchromatic images, the interpolated and colorized images increase 305.7% of recall (0.929 vs. 0.229), 78.2% of overall accuracy (0.768 vs. 0.431), 132.5% of f1-score (0.851 vs. 0.366), and 230.8% of Jaccard index (0.741 vs. 0.224), respectively.

Deep Learning-based Method for Multi-Class Classification of Oil Palm Planted Area on Plant Ages Using Ikonos Panchromatic Imagery

Oil palm has many advantages, such as biofuels, cosmetics, food ingredients, etc. The amount of oil contained in oil palm fruit is very dependent on the age of the plant, so automatic detection of oil palm plantation area based on plant ages is required to estimate the amount of oil. The use of high-resolution satellite images in oil palm detection has shown promising results for small dimensions, and previous studies have used more than one band of the satellite images data. This will be a burden in terms of cost and processing. Previous studies regarding oil palm area detection usually focused on detecting land cover to distinguish oil palm and non-oil palm areas. This study proposes a method based on deep-learning convolutional neural networks to classify oil palm plantations at a productive age. The images used in this study are the Ikonos satellite image with panchromatic bands only, which have a spatial ratio of 1m. The plantation area is classified into the non-oil palm, oil palm areas with young, mature, and old ages. This study proposes a multi-class classification method for oil palm plantations based on plant ages using convolutional neural networks (CNN). This study performs two fine-tune models on a pre-trained CNN and then classified using SVM and CNN. The performance of CNN architectures such as AlexNet, VGG16, and VGG19 was compared. The highest accuracy is 94.74% when using the CNN classifier and fine-tune model-2 of the VGG19 pre-trained network.

Optimizing the Achievable Information Content Extraction from WorldView-4 Stereo Imagery

In 2016, DigitalGlobe’s third-generation commercial Earth observation satellite WorldView-4 (WV-4), which offered 31 cm spatial resolution for panchromatic imagery, was launched and was active up to January 2019. Together with WorldView-3, this is the highest ground resolution of civilian optical space-borne missions. In this study, the information content of WV-4′s pan-sharpened stereo imagery is comprehensively investigated by manual and automatic object extraction approaches in a study area with rolling urban topography. The potential of automatic extraction was maximised by a group of refinement methods supported by the normalised digital surface model (NDSM) generated from WV-4 stereo pair. The NDSM was used as an additional band in the segmentation for revealing the object height heterogeneity. The results demonstrated that our method increased the precision, completeness and overall quality of WV-4 automatic extraction up to 8.4% for the number of buildings and up to 3.4% for the road length.

CALIBRATION OF THE SSOT MISSION USING A VICARIOUS APPROACH BASED ON OBSERVATIONS OVER THE ATACAMA DESERT AND THE GOBABEB RADCALNET STATION

Abstract. This work presents the results of the absolute radiometric calibration of the sensor on-board the “Sistema Satelital de Observación de la Tierra” (SSOT) using the vicarious approach based on in-situ measurements of surface reflectance and atmospheric retrievals. The SSOT mission, also known as FASat-Charlie, has been successfully operating for almost nine years ‒at the time of writing‒, exceeding its five-year nominal design life and providing multispectral and panchromatic imagery for different applications. The data acquired by SSOT has been used for emergency and disaster management and monitoring, cadastral mapping, urban planning, defense purposes, among other uses. In this paper, some results of the efforts conducting to the exploitation of the SSOT imagery for remote sensing quantitative applications are detailed. The results of the assessment of the radiometric calibration of the satellite sensor, performed in the Atacama Desert, Chile, using the data acquired and made available by the Gobabeb Station of Radiometric Calibration Network (RadCalNet), Namibia, are presented. Additionally, we describe the process for obtaining the absolute gains for the multispectral and panchromatic bands of the SSOT sensor by adapting the reflectance−based approach (Thome et al., 2001). The outputs achieved from the Atacama data collection have generated consistent results and average differences in the order of 3% with respect to the RadCalNet TOA reflectances. The presented results are an example of the benefits of having access to the RadCalNet data and how it increases the opportunity of conducting Cal/Val activities using endorsed calibration sites.

Investigating the performance of sentinel-2A and Landsat 8 imagery in mapping shoreline changes

Regular detection and timely monitoring of shoreline evolution are essentially needed in order to identify areas that require further investigation and protection. Possibilities for regular monitoring of shoreline changes are better facilitated now with high-frequency revisit times of Landsat-Sentinel-2 virtual constellation. Accordingly, this study aimed at investigating the performance of Landsat 8 and Sentinel-2A satellite imagery in mapping shoreline changes. The specific objectives were to 1) create reference data of shoreline erosion, accretion and seafilling using very high spatial resolution data, 2) compare the results of shoreline changes by using Landsat 8 and Sentinel-2A imagery respectively, and 3) complete the inventory of shoreline changes between 1962 and 2016. A Quickbird panchromatic imagery (0.6 m) of 2010 was employed in combination with aerial photography (0.5 m) from 1962 for the generation of reference data using a traditional photo-interpretation method. Then, geographic object-based image analysis (GEOBIA) was employed to map changes in the shoreline between 1962 (i.e., reference shoreline) and 2016 using Sentinel-2A (10 m) and Landsat 8 panchromatic band (15 m) imagery, consecutively. The use of Sentinel-2A image provided slightly better accuracy results when compared with those from the Landsat 8 image. Accordingly, an inventory of Lebanese shoreline evolution between 1962 and 2016 was completed using the Sentinel-2A classification results. The combined use of Landsat-Sentinel-2 imagery is expected to generate reliable data records for continuous monitoring of shoreline changes.

A Learning-Based Image Fusion for High-Resolution SAR and Panchromatic Imagery

Image fusion is an effective complementary method to obtain information from multi-source data. In particular, the fusion of synthetic aperture radar (SAR) and panchromatic images contributes to the better visual perception of objects and compensates for spatial information. However, conventional fusion methods fail to address the differences in imaging mechanism and, therefore, they cannot fully consider all information. Thus, this paper proposes a novel fusion method that both considers the differences in imaging mechanisms and sufficiently provides spatial information. The proposed method is learning-based; it first selects data to be used for learning. Then, to reduce the complexity, classification is performed on the stacked image, and the learning is performed independently for each class. Subsequently, to consider sufficient information, various features are extracted from the SAR image. Learning is performed based on the model’s ability to establish non-linear relationships, minimizing the differences in imaging mechanisms. It uses a representative non-linear regression model, random forest regression. Finally, the performance of the proposed method is evaluated by comparison with conventional methods. The experimental results show that the proposed method is superior in terms of visual and quantitative aspects, thus verifying its applicability.

Super-Resolution Based Deep Learning Techniques for Panchromatic Satellite Images in Application to Pansharpening

Pansharpening is a technique that fuses the coarser resolution of multispectral imagery (MS) with high spatial resolution panchromatic (PAN) imagery. Pansharpening is prone to spectral distortions based on the nature of the panchromatic band. If the spatial features are unclear in the panchromatic image, the pan-sharpened image will not be able to produce clear images. Super-Resolution (SR) is a technique that enhances minute details of the features in the image, thereby improving spatial information in the image. By fusing the Multispectral image with the super-resolved panchromatic image, there is a chance for producing high-quality multispectral imagery (pan-sharpened image). In this paper, ten state-of-the-art super-resolution based on deep learning techniques are tested and analyzed using ten different publicly available panchromatic datasets. On analysis, a feedback network for image super-resolution (SRFBN) technique outperforms the other algorithms in terms of sharp edges and pattern clarity, which are not visible in the input image. The proposed method is the fusion of SR applied PAN image with the MS image using a benchmarked Band Depended Spatial Detail (BDSD) pansharpening algorithm. The proposed method experiments with six datasets from different sensors. On analysis, the proposed technique outperforms the other counterpart pansharpening algorithms in terms of enhanced spatial information in addition to sharp edges and pattern clarity at reduced spectral distortion. Hence, the super-resolution based pansharpening algorithm is recommended for high spatial image applications.

Optimizing Sensor Relationship for Fusion of Multispectral and Panchromatic Imagery

The remote sensing satellite products: multispectral and panchromatic imagery are characterized by different levels of spatio-spectral resolutions. The fusion of these two images (provided, they are acquired for same geographic scenario) is also known as ‘Pansharpening’. This produces a composite image featuring simultaneous high levels of spatio-spectral resolutions to meet the demand of the most of remote sensing applications. Thus, different approaches for such fusion and further its quality assessment are continuously researched. The modulation transfer function is unique to the imaging sensors. In this paper, the sensor relationship of the input imagery is optimized to produce the efficient pansharpened/fused image. The performance measurement is carried out on two real datasets made available by WorldView-2 and WorldView-3 satellite sensors using two assessment techniques. The results of optimization approach are further compared to nine different most recent fusion algorithms.

WorldView-2 Data for Hierarchical Object-Based Urban Land Cover Classification in Kigali: Integrating Rule-Based Approach with Urban Density and Greenness Indices

The emergence of high-resolution satellite data, such as WorldView-2, has opened the opportunity for urban land cover mapping at fine resolution. However, it is not straightforward to map detailed urban land cover and to detect urban deprived areas, such as informal settlements, in complex urban environments based merely on high-resolution spectral features. Thus, approaches integrating hierarchical segmentation and rule-based classification strategies can play a crucial role in producing high quality urban land cover maps. This study aims to evaluate the potential of WorldView-2 high-resolution multispectral and panchromatic imagery for detailed urban land cover classification in Kigali, Rwanda, a complex urban area characterized by a subtropical highland climate. A multi-stage object-based classification was performed using support vector machines (SVM) and a rule-based approach to derive 12 land cover classes with the input of WorldView-2 spectral bands, spectral indices, gray level co-occurrence matrix (GLCM) texture measures and a digital terrain model (DTM). In the initial classification, confusion existed among the informal settlements, the high- and low-density built-up areas, as well as between the upland and lowland agriculture. To improve the classification accuracy, a framework based on a geometric ruleset and two newly defined indices (urban density and greenness density indices) were developed. The novel framework resulted in an overall classification accuracy at 85.36% with a kappa coefficient at 0.82. The confusion between high- and low-density built-up areas significantly decreased, while informal settlements were successfully extracted with the producer and user’s accuracies at 77% and 90% respectively. It was revealed that the integration of an object-based SVM classification of WorldView-2 feature sets and DTM with the geometric ruleset and urban density and greenness indices resulted in better class separability, thus higher classification accuracies in complex urban environments.

CDnet: CNN-Based Cloud Detection for Remote Sensing Imagery

Cloud detection is one of the important tasks for remote sensing image (RSI) preprocessing. In this paper, we utilize the thumbnail (i.e., preview image) of RSI, which contains the information of original multispectral or panchromatic imagery, to extract cloud mask efficiently. Compared with detection cloud mask from original RSI, it is more challenging to detect cloud mask using thumbnails due to the loss of resolution and spectrum information. To tackle this problem, we propose a cloud detection neural network (CDnet) with an encoder–decoder structure, a feature pyramid module (FPM), and a boundary refinement (BR) block. The FPM extracts the multiscale contextual information without the loss of resolution and coverage; the BR block refines object boundaries; and the encoder–decoder structure gradually recovers segmentation results with the same size as input image. Experimental results on the ZY-3 satellite thumbnails cloud cover validation data set and two other validation data sets (GF-1 WFV Cloud and Cloud Shadow Cover Validation Data and Landsat-8 Cloud Cover Assessment Validation Data) demonstrate that the proposed method achieves accurate detection accuracy and outperforms several state-of-the-art methods.

Measuring the spectral signature of polar bears from a drone to improve their detection from space

The increasing spatial resolution of earth observation satellites is creating new opportunities to survey wildlife. Satellites could be particularly valuable for surveying polar bears (Ursus maritimus) because of their remote circumpolar distribution and status of concern in the face of Arctic warming. However, the white coloration of bears does not contrast well with sea ice or snow in panchromatic imagery. We took advantage of the close-range observation capabilities of a drone to determine the spectral signature of polar bears as they would appear in multispectral satellite imagery, capturing low-altitude (≤100 m) multispectral images of bears in natural landscapes in Churchill, Manitoba, Canada. The bears' spectral curves were similar to those previously measured from pelts, with reflectance increasing with wavelength through the visible spectrum, although live bears had higher reflectance than pelts in the red-edge and near-infrared region. Bears had sufficiently consistent reflectance across the overhead surface of their body that ≥50% of pixels comprising each subject could be confidently matched to its core spectral signature, boding well for detection in coarser satellite imagery. Bears were clearly distinguishable from snow by their much lower reflectance in the blue and green region, but could potentially be confounded with large bright boulders. Currently available multispectral satellite imagery may still be too coarse (1.2 m/pixel) to reliably detect polar bears on sea ice, but resolution will likely continue to increase in future systems. Drones are a useful tool to resolve the spectral signature of wildlife species that could potentially be detected in satellite imagery.