Panchromatic Satellite Imagery Research Articles

Mapping off-road tracks and animal paths in protected areas using high-resolution GeoEye-1 panchromatic satellite imagery

ABSTRACT Off-road driving activities are common in many protected areas. This study aimed at applying a curvelet-based approach for the automatic extraction, mapping and separation of off-road tracks and animal paths in Masai Mara National Reserve, Kenya, using high-resolution GeoEye-1 panchromatic satellite imagery (50 cm). A novel hybrid remote sensing-GIS method comprising three main blocks is proposed: (1) extracting the high-contrast curvilinear feature from curvelet magnitudes derived from the finer scale of curvelet coefficient; (2) extracting the low-contrast curvilinear feature from coarser scale curvelets and refine the shape by deformable active contour (Snake), and (3) categorizing the extracted curvilinear feature into vehicle tracks and animal paths by a fuzzy logic inference system. Results from quantification of extraction and categorization performance of the trails were 77.5% for completeness, 89.2% for correctness, and 4.5% for redundancy, with an overall accuracy of 79.5%. Using grid matching, we find a high correlation between off-road vehicle tracks and animal paths in the area (r = 0.75, p < 0.05) indicating co-occurrence of these two types of trails. The proposed approach provides a basis for large-scale mapping and monitoring of off-road tracks and animal paths in the African savanna from space.

Machine-learning model to delineate sub-surface agricultural drainage from satellite imagery.

Knowing sub-surface drainage (tile-drain) extent is integral to understanding how landscapes respond to precipitation events and subsequent days of drying, as well as how soil characteristics and land management influence stream response. Consequently, a time-series of tile-drain extent would inform one aspect of land management that complicates our ability to explain streamflow and water-quality as a function of climate variability or conservation management. We trained a UNet machine-learning model, a convolutional neural network designed to highlight objects of interest within an image, to delineate tile-drain networks in panchromatic satellite imagery without additional data on soils, topography, or historical tile-drain extent. This was done by training the model to match the accuracy of human experts manually tracing the surface representation of tile drains in satellite imagery. Our approach began with a library of images that were used to train and quantify the accuracy of the model, with model performance tested on imagery from two areas that were not used to train the model. Satellite imagery included acquisition dates from 2008-2020. Training imagery were from agricultural areas within the U.S. Great Lakes basin. Validation imagery were from the upper Maumee River, tributary to western Lake Erie, and an Indiana, Ohio-River headwater tributary. Our analysis of the satellite imagery paired with meteorological and soil data found that during spring, a combination of relatively high solar radiation, intermediate soil-water content and bare fields enabled the best model performance. Each area of interest was heavily tile-drained, where better understanding the movement of water, nutrients, and sediment from fields to downstream water bodies is key to managing harmful algal blooms and hypoxia. The trained UNet model successfully identified tile drains visible in the validation imagery with an accuracy of 93-96% and balanced accuracy of 52-54%, similar to performance for training data (95% and 63%, respectively). Model performance will benefit from ongoing contributions to the training library. This article is protected by copyright. All rights reserved.

Super-Resolution Integrated Building Semantic Segmentation for Multi-Source Remote Sensing Imagery

Multi-source remote sensing imagery has become widely accessible owing to the development of data acquisition systems. In this paper, we address the challenging task of the semantic segmentation of buildings via multi-source remote sensing imagery with different spatial resolutions. Unlike previous works that mainly focused on optimizing the segmentation model, which did not enable the severe problems caused by the unaligned resolution between the training and testing data to be fundamentally solved, we propose to integrate SR techniques with the existing framework to enhance the segmentation performance. The feasibility of the proposed method was evaluated by utilizing representative multi-source study materials: high-resolution (HR) aerial and low-resolution (LR) panchromatic satellite imagery as the training and testing data, respectively. Instead of directly conducting building segmentation from the LR imagery by using the model trained using the HR imagery, the deep learning-based super-resolution (SR) model was first adopted to super-resolved LR imagery into SR space, which could mitigate the influence of the difference in resolution between the training and testing data. The experimental results obtained from the test area in Tokyo, Japan, demonstrate that the proposed SR-integrated method significantly outperforms that without SR, improving the Jaccard index and kappa by approximately 19.01% and 19.10%, respectively. The results confirmed that the proposed method is a viable tool for building semantic segmentation, especially when the resolution is unaligned.

Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery

Recent deployments of CubeSat imagers by companies such as Planet may advance hydrological remote sensing by providing an unprecedented combination of high temporal and high spatial resolution imagery at the global scale. With approximately 170 CubeSats orbiting at full operational capacity, the Planet CubeSat constellation currently offers an average revisit time of &lt;1 day for the Arctic and near-daily revisit time globally at 3 m spatial resolution. Such data have numerous potential applications for water resource monitoring, hydrologic modeling and hydrologic research. Here we evaluate Planet CubeSat imaging capabilities and potential scientific utility for surface water studies in the Yukon Flats, a large sub-Arctic wetland in north central Alaska. We find that surface water areas delineated from Planet imagery have a normalized root mean square error (NRMSE) of &lt;11% and geolocation accuracy of &lt;10 m as compared with manual delineations from high resolution (0.3–0.5 m) WorldView-2/3 panchromatic satellite imagery. For a 625 km2 subarea of the Yukon Flats, our time series analysis reveals that roughly one quarter of 268 lakes analyzed responded to changes in Yukon River discharge over the period 23 June–1 October 2016, one half steadily contracted, and one quarter remained unchanged. The spatial pattern of observed lake changes is heterogeneous. While connections to Yukon River control the hydrologically connected lakes, the behavior of other lakes is complex, likely driven by a combination of intricate flow paths, underlying geology and permafrost. Limitations of Planet CubeSat imagery include a lack of an automated cloud mask, geolocation inaccuracies, and inconsistent radiometric calibration across multiple platforms. Although these challenges must be addressed before Planet CubeSat imagery can achieve its full potential for large-scale hydrologic research, we conclude that CubeSat imagery offers a powerful new tool for the study and monitoring of dynamic surface water bodies.

Automatic Counting of Large Mammals from Very High Resolution Panchromatic Satellite Imagery

Estimating animal populations by direct counting is an essential component of wildlife conservation and management. However, conventional approaches (i.e., ground survey and aerial survey) have intrinsic constraints. Advances in image data capture and processing provide new opportunities for using applied remote sensing to count animals. Previous studies have demonstrated the feasibility of using very high resolution multispectral satellite images for animal detection, but to date, the practicality of detecting animals from space using panchromatic imagery has not been proven. This study demonstrates that it is possible to detect and count large mammals (e.g., wildebeests and zebras) from a single, very high resolution GeoEye-1 panchromatic image in open savanna. A novel semi-supervised object-based method that combines a wavelet algorithm and a fuzzy neural network was developed. To discern large mammals from their surroundings and discriminate between animals and non-targets, we used the wavelet technique to highlight potential objects. To make full use of geometric attributes, we carefully trained the classifier, using the adaptive-network-based fuzzy inference system. Our proposed method (with an accuracy index of 0.79) significantly outperformed the traditional threshold-based method (with an accuracy index of 0.58) detecting large mammals in open savanna.

Hierarchical Sea-Land Segmentation for Panchromatic Remote Sensing Imagery

Automatic sea-land segmentation is an essential and challenging field for the practical use of panchromatic satellite imagery. Owing to the temporal variations as well as the complex and inconsistent intensity contrast in both land and sea areas, it is difficult to generate an accurate segmentation result by using the conventional thresholding methods. Additionally, the freely available digital elevation model (DEM) also difficultly meets the requirements of high-resolution data for practical usage, because of the low precision and high memory storage costs for the processing systems. In this case, we proposed a fully automatic sea-land segmentation approach for practical use with a hierarchical coarse-to-fine procedure. We compared our method with other state-of-the-art methods with real images under complex backgrounds and conducted quantitative comparisons. The experimental results show that our method outperforms all other methods and proved being computationally efficient.

Use of shadow for enhancing mapping of perennial desert plants from high-spatial resolution multispectral and panchromatic satellite imagery

Satellite remote-sensing techniques face challenges in extracting vegetation-cover information in desert environments. The limitations in detection are attributed to three major factors: (1) soil background effect, (2) distribution and structure of perennial desert vegetation, and (3) tradeoff between spatial and spectral resolutions of the satellite sensor. In this study, a modified vegetation shadow model (VSM-2) is proposed, which utilizes vegetation shadow as a contextual classifier to counter the limiting factors. Pleiades high spatial resolution, multispectral (2 m), and panchromatic (0.5 m) images were utilized to map small and scattered perennial arid shrubs and trees. We investigated the VSM-2 method in addition to conventional techniques, such as vegetation indices and prebuilt object-based image analysis. The success of each approach was evaluated using a root sum square error metric, which incorporated field data as control and three error metrics related to commission, omission, and percent cover. Results of the VSM-2 revealed significant improvements in perennial vegetation cover and distribution accuracy compared with the other techniques and its predecessor VSM-1. Findings demonstrated that the VSM-2 approach, using high-spatial resolution imagery, can be employed to provide a more accurate representation of perennial arid vegetation and, consequently, should be considered in assessments of desertification.

Seasonal evolution of melt ponds on Arctic sea ice

AbstractThe seasonal evolution of melt ponds has been well documented on multiyear and landfast first‐year sea ice, but is critically lacking on drifting, first‐year sea ice, which is becoming increasingly prevalent in the Arctic. Using 1 m resolution panchromatic satellite imagery paired with airborne and in situ data, we evaluated melt pond evolution for an entire melt season on drifting first‐year and multiyear sea ice near the 2011 Applied Physics Laboratory Ice Station (APLIS) site in the Beaufort and Chukchi seas. A new algorithm was developed to classify the imagery into sea ice, thin ice, melt pond, and open water classes on two contrasting ice types: first‐year and multiyear sea ice. Surprisingly, melt ponds formed ∼3 weeks earlier on multiyear ice. Both ice types had comparable mean snow depths, but multiyear ice had 0–5 cm deep snow covering ∼37% of its surveyed area, which may have facilitated earlier melt due to its low surface albedo compared to thicker snow. Maximum pond fractions were 53 ± 3% and 38 ± 3% on first‐year and multiyear ice, respectively. APLIS pond fractions were compared with those from the Surface Heat Budget of the Arctic Ocean (SHEBA) field campaign. APLIS exhibited earlier melt and double the maximum pond fraction, which was in part due to the greater presence of thin snow and first‐year ice at APLIS. These results reveal considerable differences in pond formation between ice types, and underscore the importance of snow depth distributions in the timing and progression of melt pond formation.

Calibrating remotely sensed river bathymetry in the absence of field measurements: Flow REsistance Equation‐Based Imaging of River Depths (FREEBIRD)

AbstractRemote sensing could enable high‐resolution mapping of long river segments, but realizing this potential will require new methods for inferring channel bathymetry from passive optical image data without using field measurements for calibration. As an alternative to regression‐based approaches, this study introduces a novel framework for Flow REsistance Equation‐Based Imaging of River Depths (FREEBIRD). This technique allows for depth retrieval in the absence of field data by linking a linear relation between an image‐derived quantity X and depth d to basic equations of open channel flow: continuity and flow resistance. One FREEBIRD algorithm takes as input an estimate of the channel aspect (width/depth) ratio A and a series of cross‐sections extracted from the image and returns the coefficients of the X versus d relation. A second algorithm calibrates this relation so as to match a known discharge Q. As an initial test of FREEBIRD, these procedures were applied to panchromatic satellite imagery and publicly available aerial photography of a clear‐flowing gravel‐bed river. Accuracy assessment based on independent field surveys indicated that depth retrieval performance was comparable to that achieved by direct, field‐based calibration methods. Sensitivity analyses suggested that FREEBIRD output was not heavily influenced by misspecification of A or Q, or by selection of other input parameters. By eliminating the need for simultaneous field data collection, these methods create new possibilities for large‐scale river monitoring and analysis of channel change, subject to the important caveat that the underlying relationship between X and d must be reasonably strong.

DETECTING AND DISCARDING OF SHADOWS IN IMAGE USING GEOMETRIC CONTOURS AND REGION BASED SEGMENTATION USING THRESHOLDING APPROACH

Shadow detection and removal is an important task when dealing with color outdoor images. Shadows are generated by a local and relative absence of light. Most shadow detection and segmentation methods are based on image analysis. However, some factors will affect the detection result due to the complexity of the circumstances.In this paper, a new algorithm for shadow detection and isolation of buildings in high-resolution panchromatic satellite imagery is proposed. This algorithm is based on tailoring the traditional model of the geometric active contours such that the new model of the contours is systematically biased toward segmenting the shadow and the dark regions in the image. Feature extraction a type of dimensionality reduction that efficiently represents interesting parts of an image as a compact feature vector. This approach is useful when image sizes are large and a reduced feature representation is required to quickly complete tasks such as image matching and retrieval.

Automatic urban debris zone extraction from post-hurricane very high-resolution satellite and aerial imagery

Automated remote sensing methods have not gained widespread usage for damage assessment after hurricane events, especially for low-rise buildings, such as individual houses and small businesses. Hurricane wind, storm surge with waves, and inland flooding have unique damage signatures, further complicating the development of robust automated assessment methodologies. As a step toward realizing automated damage assessment for multi-hazard hurricane events, this paper presents a mono-temporal image classification methodology that quickly and accurately differentiates urban debris from non-debris areas using post-event images. Three classification approaches are presented: spectral, textural, and combined spectral–textural. The methodology is demonstrated for Gulfport, Mississippi, using IKONOS panchromatic satellite and NOAA aerial colour imagery collected after 2005 Hurricane Katrina. The results show that multivariate texture information significantly improves debris class detection performance by decreasing the confusion between debris and other land cover types, and the extracted debris zone accurately captures debris distribution. Additionally, the extracted debris boundary is approximately equivalent regardless of imagery type, demonstrating the flexibility and robustness of the debris mapping methodology. While the test case presents results for hurricane hazards, the proposed methodology is generally developed and expected to be effective in delineating debris zones for other natural hazards, including tsunamis, tornadoes, and earthquakes.

GPU-based normalized cuts for road extraction using satellite imagery

This paper presents a GPU implementation of normalized cuts for road extraction problem using panchromatic satellite imagery. The roads have been extracted in three stages namely pre-processing, image segmentation and post-processing. Initially, the image is pre-processed to improve the tolerance by reducing the clutter (that mostly represents the buildings, vegetation, and fallow regions). The road regions are then extracted using the normalized cuts algorithm. Normalized cuts algorithm is a graph-based partitioning approach whose focus lies in extracting the global impression (perceptual grouping) of an image rather than local features. For the segmented image, post-processing is carried out using morphological operations – erosion and dilation. Finally, the road extracted image is overlaid on the original image. Here, a GPGPU (General Purpose Graphical Processing Unit) approach has been adopted to implement the same algorithm on the GPU for fast processing. A performance comparison of this proposed GPU implementation of normalized cuts algorithm with the earlier algorithm (CPU implementation) is presented. From the results, we conclude that the computational improvement in terms of time as the size of image increases for the proposed GPU implementation of normalized cuts. Also, a qualitative and quantitative assessment of the segmentation results has been projected.

Shadow Detection of Man-Made Buildings in High-Resolution Panchromatic Satellite Images

High-resolution satellite imagery is considered an excellent candidate for extracting information about the human activities on Earth. The information about residential development and suburban area mapping is of interest that can be obtained from these images. Shadow of structures such as man-made buildings is one of the main cues for structure detection in panchromatic high-resolution satellite imagery. However, to correctly exploit the information of the shadow in an image, the shadow needs to be detected and isolated first. In this paper, we propose a new algorithm for shadow detection and isolation of buildings in high-resolution panchromatic satellite imagery. The proposed algorithm is based on tailoring the traditional model of the geometric active contours such that the new model of the contours is systematically biased toward segmenting the shadow and the dark regions in the image. The systematic biasing in the proposed contour model is accomplished by novel encoding of the radiometric characteristics of the shadows regions. After detecting and segmenting the shadow and the dark regions in the image, further processing steps are introduced. The proposed postprocessing is based on selection of optimal threshold and a boundary complexity metric to distinguish the true shadows from the clutter. Experimental results are presented to validate the performance of the proposed algorithm on real high-resolution panchromatic satellite images.

Methodology for identifying urban growth potential using land use and population data: A case study of Islamabad Zone IV

Spatial growth is an inevitable phenomenon which depends on a number of factors like accessibility; availability of land for growth and favourable environment both at internal and external level are considered. In this study, a GIS based methodology has been proposed to identify future growth potential in the area Islamabad Zone IV of Islamabad, Pakistan. Village level population data (2007) is used to obtain population density and population centre; SPOT panchromatic satellite imagery (2007) shows that mostly GIS weighted overlay analysis has been used to extract relative future growth potential in the area. The study shows that nearly 63 percent of Zone IV carries a ‘High’ to ‘Very High’ future growth potential which is mainly located closer to Islamabad Highway. This methodology can be used to identify the varying future growth potential within an area. It provides useful information that which areas are more likely to become urban in future as compared to other areas. Such exercise can provide a cost effective solution to policy makers to aid future land use planning and building control.

Integration of Corona and Landsat Thematic Mapper data for bedrock geological studies in the high Arctic

The Severnaya Zemlya Archipelago near the continental edge in the Russian high Arctic is one of few land areas along the Eurasian Arctic margin. It is of particular interest for investigating the Arctic's tectonic history. This study focuses on the Palaeozoic bedrock of October Revolution Island. In the Russian high Arctic detailed topographic maps and aerial photography often are not available. The potential of low-cost satellite imagery as a substitute is shown in this study. High-resolution Corona KH-4A panchromatic satellite imagery and Landsat Thematic Mapper (TM) multispectral data have been integrated. In combination with field investigations in key areas, these data provide the basis for new interpretations of the geology. Corona images were digitized and georeferenced to provide a basis for conventional and digital geological mapping. Merging Corona and Landsat TM data resulted in a high-resolution multispectral image of enhanced interpretability. Lithological contacts have been traced, supported by a bedrock image extracted from the Landsat TM data. Stereoscopic coverage of the Corona KH-4A photographic sensor allowed a structural interpretation. All results were integrated into a geological interpretation of southern October Revolution Island which provides an encouraging platform for further work in the high Arctic.

Edge- and region-based segmentation technique for the extraction of large, man-made objects in high-resolution satellite imagery

A new object-oriented segmentation approach with special focus on shape analysis was developed for the extraction of large, man-made objects, especially agricultural fields, in high-resolution panchromatic satellite imagery. The approach, a combination of region- and edge-based techniques, includes new methods for the evaluation of straight edges, edge preserving degradation, and edge-guided region growing.

Detection of boreal forest decline with high-resolution panchromatic satellite imagery

Long-term operation of the powerful copper-nickel smelter Severonikel in central Kola Peninsula, Arctic Russia has caused decline of boreal forest. Using historical photographs from the American spy satellite CORONA from the 1960s and latest images from civil high-resolution satellites, it is possible to study changes in forest over a long perspective. The study aims were: (1) to detect expansion of the forest decline around the Severonikel smelter for the period 1964-1996 with panchromatic high-resolution image pair; (2) to test the applicability of CORONA and Landsat TM together with a high resolution panchromatic image in automatic change detection studies. High-resolution panchromatic summer images, registered by the CORONA in 1964 and by IRS 1C in 1996, as well as multi-spectral Landsat TM from 1996 were used in the analysis. The simple-difference and PCA methods were applied separately to the datasets CORONA/IRS and CORONA/Landsat TM for change-detection between 1964 and 1996. Both methods showed similar results for the same dataset and close results for the two datasets. Analysis showed several patches of change in forests due to the smelter emissions, fires and logging. Close to the smelter, a further transition from the forest decline to the industrial desert took place at the expanse of the low emission sources, whereas beyond 10 km from the smelter, expansion of the forest damage progressed due to the emissions from the high sources. Landsat TM, despite its coarser spatial resolution, can be a good alternative in change detection studies, if other high-resolution panchromatic satellite data are not available. Historical CORONA can be successfully used in automatic change detection, although, for mosaicking and co-registration, DEM is badly needed.

The value of paddock trees for regional conservation in an agricultural landscape

Summary Isolated trees and small patches of trees – paddock trees – are a prominent feature of agricultural landscapes in Australia, but are declining in many areas due to natural senescence, clearing, dieback and the general absence of recruitment. We assessed the importance of paddock trees for woodland conservation in a 30 000 ha sample of the New South Wales (NSW) South‐west Slopes using Satellite Pour l’Observation de la Terre (SPOT) panchromatic satellite imagery combined with models predicting the original distribution of vegetation communities. Tree‐cover occurred over 12% of the study area. The patch‐size distribution of vegetation in the study area varied between woodland types. For woodland communities that were confined to hills and ridges, most tree‐cover occurred as few, large remnants. For woodland communities of the foothills and plains (Blakely's Red Gum, Eucalyptus blakelyi and Yellow Box, Eucalyptus melliodora, or White Box, Eucalyptus albens and Red Stringybark), 54% of remnant tree‐cover occurred as patches &lt; 1 ha. The loss of paddock trees will cause substantial reductions to some woodland communities. For example, the loss of patches &lt; 1 ha in woodlands dominated by Blakely's Red Gum and Yellow Box would reduce this association from 7.4% to 3.4% of its predicted pre‐1750 distribution. Mean distance to tree‐cover across the study area increased almost fourfold if patches &lt; 1 ha were removed from the landscape, which may have consequences for movements of some flora and fauna. Failure to protect and perpetuate paddock trees will diminish the likelihood of achieving the conservation objectives of comprehensiveness, adequacy and representativeness in agricultural landscapes.

Detection of small objects from high-resolution panchromatic satellite imagery based on supervised image segmentation

A new concept for the detection of small objects from modular optoelectronic multispectral scanner (MOMS-02) high spatial resolution panchromatic satellite imagery is presented. The authors combine supervised shape classification with unsupervised image segmentation in an iterative procedure which allows a target-oriented search for specific object shapes.