Very High Resolution Satellite Images Research Articles

Estimating beluga whale abundance from space: using drones to ground‐validate VHR satellite imagery

AbstractRoutine monitoring of cetaceans is imperative for understanding their population trends and making informed management decisions. However, the inherent nature of cetaceans and the marine ecosystems they inhabit make annual population surveys logistically and economically challenging with current survey methods. One emerging solution is utilizing very high‐resolution (VHR) satellite imagery, which is a logistically efficient method for providing an instantaneous view of areas spanning hundreds of square kilometers. The objective of this study was to determine two factors required to reliably conduct beluga whale population abundance estimates with VHR satellite imagery: (1) depths that beluga whales are visible in VHR satellite images, which are used to define availability bias correction factors, and (2) a comparison of abundance estimates in VHR satellite imagery to current aerial methods. We submerged beluga whale models to different depths in two different water clarities and determined that beluga whales are distinguished only at the surface in turbid water (Secchi depth: 2.56 m) and at depths of 0–2 m in clear water (Secchi depth: 4.04 m). Based on the proportion of time beluga whales spend at these depths, an availability bias correction factor for Western Hudson Bay beluga whales was defined as 2.40 ± 0.16 for turbid water and 1.89 ± 0.05 for clear water. Synchronous ground‐validation surveys determined availability corrected beluga whale abundance estimates in 0.31 m VHR satellite imagery (n = 173 beluga whales) and imagery that was HD sharpened using a proprietary algorithm to approximate 0.15 m resolution (n = 170) to be comparable to drone imagery (n = 164). VHR satellite imagery has the potential to increase the frequency of beluga whale population surveys, which has become increasingly important as beluga whales face rapid ecosystem changes and increased anthropogenic disturbances.

Transformer-based semantic segmentation for large-scale building footprint extraction from very-high resolution satellite images

Extracting building footprints from extensive very-high spatial resolution (VHSR) remote sensing data is crucial for diverse applications, including surveying, urban studies, population estimation, identification of informal settlements, and disaster management. Although convolutional neural networks (CNNs) are commonly utilized for this purpose, their effectiveness is constrained by limitations in capturing long-range relationships and contextual details due to the localized nature of convolution operations. This study introduces the masked-attention mask transformer (Mask2Former), based on the Swin Transformer, for building footprint extraction from large-scale satellite imagery. To enhance the capture of large-scale semantic information and extract multiscale features, a hierarchical vision transformer with shifted windows (Swin Transformer) serves as the backbone network. An extensive analysis compares the efficiency and generalizability of Mask2Former with four CNN models (PSPNet, DeepLabV3+, UpperNet-ConvNext, and SegNeXt) and two transformer-based models (UpperNet-Swin and SegFormer) featuring different complexities. Results reveal superior performance of transformer-based models over CNN-based counterparts, showcasing exceptional generalization across diverse testing areas with varying building structures, heights, and sizes. Specifically, Mask2Former with the Swin transformer backbone achieves a mean intersection over union between 88% and 93%, along with a mean F-score (mF-score) ranging from 91% to 96.35% across various urban landscapes.

Classification of buildings from VHR satellite images using ensemble of U-Net and ResNet

The urbanization rate of India is 35.9 % by 2022 reports. In this 45.23 % of urbanization is happening in Maharashtra and it is the third most urbanized state of India after Tamil Nadu and Kerala. In metropolitan areas, the classification of land cover from satellite images has been the focus of remote sensing over the years. Due to complex architecture and a lack of labeled data, classifying buildings in metropolitan areas from very high resolution (VHR) satellite imagery is challenging. Traditional approaches for building classification include hand-crafted features and transfer learning methods. These methods often struggle with the variability in building shapes, orientation, and viewpoint, leading to low accuracy in densely populated urban areas and limited performance when dealing with high- resolution satellite images. A deep-learning based approach for semantic segmentation using U-Net with a backbone of ResNet-34 is proposed for building classification. Urban area Dataset with Images of 0.5 m resolution is prepared from SASPlanet. One hot Encoding is applied for classifying buildings. U-Net is trained with encoded data. The proposed model is evaluated on the Indian dataset, specifically, the urban areas of Nashik, Maharashtra state and the accuracy obtained for the classification dataset is 60 % and the accuracy of the building detection is about 85 %. Change detection is calculated from bi-temporal images. The GIS maps are updated to detect changes in buildings, represented by different colors to distinguish newly constructed buildings, existing structures and demolished ones.

Case instance segmentation of small farmland based on Mask R-CNN of feature pyramid network with double attention mechanism in high resolution satellite images

Accurate spatial information of farmland in small farms is very important to provide operable information to farmers, managers and decision makers. However, small farms have small area, irregular shape, and use a variety of planting crops, which makes their boundaries blurred, and the standard edge detection algorithm cannot accurately segment the farmland boundary. Therefore, the automatic delimitation of fields in small farms is a challenging task. Aiming at the above problems, this paper proposes an example segmentation method of Mask R-CNN based on dual attention mechanism feature pyramid network (DAFPN) to describe small farms. DAFPN is composed of two attention modules: spatial attention module (SPA) and channel attention module (CHA) to enhance its feature extraction ability. Spatial attention module (SPA) generates spatial attention map by using the spatial relationship of features, and generates information to be emphasized or suppressed in spatial location; The channel attention module (CHA) learns an adaptive channel merging method based on the attention mechanism. Our proposed DAFPN can be easily inserted into the existing FPN model. We have conducted extensive experimental analysis on very high resolution (VHR) satellite images based on the Mask R-CNN deep learning framework of DAFPN. The standard COCO dataset evaluation index and F1-score evaluation strategy are used to compare the algorithm. AP50, AP75 and F1-score reach 82.86%, 55.51% and 70.90% respectively, which is 8.7%, 8.31% and 6.87% higher than Mask R-CNN respectively. Our results highlight the ability of Mask R-CNN based on DAFPN to accurately depict small farms in VHR satellite images, which lays a foundation for the automatic segmentation of small farms.

Gray whale detection in satellite imagery using deep learning

AbstractThe combination of very high resolution (VHR) satellite remote sensing imagery and deep learning via convolutional neural networks provides opportunities to improve global whale population surveys through increasing efficiency and spatial coverage. Many whale species are recovering from commercial whaling and face multiple anthropogenic threats. Regular, accurate population surveys are therefore of high importance for conservation efforts. In this study, a state‐of‐the‐art object detection model (YOLOv5) was trained to detect gray whales (Eschrichtius robustus) in VHR satellite images, using training data derived from satellite images spanning different sea states in a key breeding habitat, as well as aerial imagery collected by unoccupied aircraft systems. Varying combinations of aerial and satellite imagery were incorporated into the training set. Mean average precision, whale precision, and recall ranged from 0.823 to 0.922, 0.800 to 0.939, and 0.843 to 0.889, respectively, across eight experiments. The results imply that including aerial imagery in the training data did not substantially impact model performance, and therefore, expansion of representative satellite datasets should be prioritized. The accuracy of the results on real‐world data, along with short training times, indicates the potential of using this method to automate whale detection for population surveys.

Three-Dimensional Modelling of Past and Present Shahjahanabad through Multi-Temporal Remotely Sensed Data

Cultural heritage is under tremendous pressure in the rapidly growing and transforming cities of the global south. Historic cities and towns are often faced with the dilemma of having to preserve old monuments while responding to the pressure of adapting itself to a modern lifestyle, which often results in the loss of cultural heritage. Indian cities such as Delhi possess a rich legacy of tangible heritage, traditions, and arts, which are reflected in their present urban form. The creation of temporal 3D models of such cities not only provides a platform with which one can experience the past, but also helps to understand, examine, and improve its present deteriorating state. However, gaining access to historical data to support the development of city-scale 3D models is a challenge. While data gaps can be bridged by combining multiple data sources, this process also presents considerable technical challenges. This paper provides a framework to generate LoD-2 (level-of-detail) 3D models of the present (the 2020s) and the past (the 1970s) of a heritage mosque surrounded by a dense and complex urban settlement in Shahjahanabad (Old Delhi) by combining multiple VHR (very high resolution) satellite images. The images used are those of Pleiades and Worldview-1 and -3 (for the present) and HEXAGON KH-9 declassified spy images (for the past). The chronological steps are used to extract the DSMs and DTMs that provide a base for the 3D models. The models are rendered, and the past and present are visualized using graphics and videos. The results reveal an average increase of 80% in the heights of the built structures around the main monument (mosque), leading to a loss in the visibility of this central mosque.

DECISION-BASED FUSION OF PANSHARPENED VHR SATELLITE IMAGES USING TWO-LEVEL ROLLING SELF-GUIDANCE FILTERING AND EDGE INFORMATION

Abstract. Pan-sharpening (PS) fuses low-resolution multispectral (LR MS) images with high-resolution panchromatic (HR PAN) bands to produce HR MS data. Current PS methods either better maintain the spectral information of MS images, or better transfer the PAN spatial details to the MS bands. In this study, we propose a decision-based fusion method that integrates two basic pan-sharpened very-high-resolution (VHR) satellite imageries taking advantage of both images simultaneously. It uses two-level rolling self-guidance filtering (RSGF) and Canny edge detection. The method is tested on Worldview (WV)-2 and WV-4 VHR satellite images on the San Fransisco and New York areas, using four PS algorithms. Results indicate that the proposed method increased the overall spectral-spatial quality of the base pan-sharpened images by 7.2% and 9.8% for the San Fransisco and New York areas, respectively. Our method therefore effectively addresses decision-level fusion of different base pan-sharpened images.

Federated Deep Learning With Prototype Matching for Object Extraction From Very-High-Resolution Remote Sensing Images

Deep convolutional neural networks (DCNNs) have become the leading tools for object extraction from very-high-resolution (VHR) remote sensing images. However, the label scarcity problem of local datasets hinders the prediction performances of DCNNs, and privacy concerns regarding remote sensing data often arise in the traditional deep learning schemes. To cope with these problems, we propose a novel federated learning scheme with prototype matching (FedPM) to collaboratively learn a richer DCNN model by leveraging remote sensing data distributed among multiple clients. This scheme conducts federated optimization of DCNNs by aggregating clients’ knowledge in the gradient space without compromising data privacy. Specifically, the prototype matching method is developed to regularize the local training using prototypical representations while reducing the distribution divergence across heterogeneous image data. Furthermore, the derived deviations across local and global prototypes are applied to quantify the effects of local models on the decision boundary and optimize the global model updating via the attention-weighted aggregation scheme. Finally, the sparse ternary compression (STC) method is used to alleviate communication costs. Extensive experimental results derived from VHR aerial and satellite image datasets verify that the FedPM can dramatically improve the prediction performance of DCNNs on object extraction with lower communication costs. To the best of our knowledge, this is the first time federated learning has been applied for remote sensing visual tasks.

An online continual object detector on VHR remote sensing images with class imbalance

It is a great challenge for traditional offline detectors to learn from continuous data streams, remember previous tasks and adapt to new-coming tasks in dynamic environments. To meet the challenge, online continual learning has recently attracted increasing attention, while the overwhelming majority of works focus only on classification with a balanced class distribution assumption. In this paper, we propose a replay-based approach called an online continual object detector (OCOD) for very-high-resolution (VHR) remote sensing images. First, we find that rehearsal imbalance is ubiquitous, and has more important impact on experimental results than class imbalance, which is contrary to the situation of offline learning (due to the limited memory). Here, rehearsal imbalance refers to significant difference among the number of images pertaining to various classes. Second, entropy is used to measure the degree of rehearsal imbalance in the memory, and an entropy reservoir sampling (ERS) strategy is proposed to maintain rehearsal balance in the online memory. Finally, a rehearsal-balancing priority assignment network (RBPAN) is proposed to adaptively select images from the memory for a rehearsal-balancing replay procedure. The experimental results obtained on three publicly available VHR satellite images from the NWPU VHR-10, DIOR and DOTA datasets, highlight the effectiveness and practicality of developed method.

Monitoring Dryland Trees With Remote Sensing. Part A: Beyond CORONA—Historical HEXAGON Satellite Imagery as a New Data Source for Mapping Open-Canopy Woodlands on the Tree Level

Monitoring woody cover by remote sensing is considered a key methodology towards sustainable management of trees in dryland forests. However, while modern very high resolution satellite (VHRS) sensors allow woodland mapping at the individual tree level, the historical perspective is often hindered by lack of appropriate image data. In this first study employing the newly accessible historical HEXAGON KH-9 stereo-panoramic camera images for environmental research, we propose their use for mapping trees in open-canopy conditions. The 2–4 feet resolution panchromatic HEXAGON satellite photographs were taken 1971–1986 within the American reconnaissance programs that are better known to the scientific community for their lower-resolution CORONA images. Our aim is to evaluate the potential of combining historical CORONA and HEXAGON with recent WorldView VHRS imagery for retrospective woodland change mapping on the tree level. We mapped all trees on 30 1-ha test sites in open-canopy argan woodlands in Morocco in the field and from the VHRS imagery for estimating changes of tree density and size between 1967/1972 and 2018. Prior to image interpretation, we used simulations based on unmanned aerial system (UAS) imagery for exemplarily examining the role of illumination, viewing geometry and image resolution on the appearance of trees and their shadows in the historical panchromatic images. We show that understanding these parameters is imperative for correct detection and size-estimation of tree crowns. Our results confirm that tree maps derived solely from VHRS image analysis generally underestimate the number of small trees and trees in clumped-canopy groups. Nevertheless, HEXAGON images compare remarkably well with WorldView images and have much higher tree-mapping potential than CORONA. By classifying the trees in three sizes, we were able to measure tree-cover changes on an ordinal scale. Although we found no clear trend of forest degradation or recovery, our argan forest sites show varying patterns of change, which are further analysed in Part B of our study. We conclude that the HEXAGON stereo-panoramic camera images, of which 670,000 worldwide will soon be available, open exciting opportunities for retrospective monitoring of trees in open-canopy conditions and other woody vegetation patterns back into the 1980s and 1970s.

POTENTIAL EXPLORATION OF SEGMENTATION DRIVEN STEREO MATCHING OF VERY HIGH-RESOLUTION SATELLITE IMAGERY

Abstract. The automatic establishment of image relationship between oblique images can be a challenging task. Feature based image matching (FBM) establishes this relationship by detecting and matching corresponding feature points. A robust matching is beneficial for many tasks including reconstruction, mapping and localization. The need of automatic processing of remotely sensed data, like very highresolution (VHR) satellite imagery, increased over the years. Furthermore, green vegetation and water are changing the physical properties with respect to the amount of light emitted, season and pollution. In addition, with human interaction, the change in appearance increases. The Normalized Differential Vegetation Index (NDVI) is a well-known and studied index in order to detect healthy green vegetation. The Normalized Differential Water Index (NDWI) can help identify water areas in an image. They can be used to preliminarily segment images into different categories for later process. This study proposes a novel framework to explore the potential of feature based stereo matching for very high-resolution satellite imagery with segmentation. The proposed framework will perform first, image segmentation with NDVI and NDWI on stereo VHR satellite image pairs. Then, classification by threshold to detect healthy green vegetation, water and image frame. Features within these three classes are masked out and kept from being processed during the feature matching step. The idea is that, features in these classes are easy to cause miss matching because they are more prone to be affected by different image and environmental conditions in the stereo image pairs. As a result, the amount of miss matches can be reduced on average by 7.5%. Furthermore, the segmentation decreases the total amount of detected features by 13.71%, so that the processing time decreases. This study has successfully proven that segmentation can lead to improved stereo matching. In future, segmentation driven can be utilized by AI matching processes as well as traditionally photogrammetric or computer vision tasks.

MULTIRESOLUTION SEGMENTATION FOR EXTRACTING PLASTIC GREENHOUSES FROM DEIMOS-2 IMAGERY

Abstract. Accurate greenhouse mapping can support environment monitoring and resource management. In an object-based image analysis (OBIA) approach focused on plastic covered greenhouses (PCG) classification, the segmentation is a crucial step for the goodness of the final results. Multiresolution segmentation (MRS) is one of the most used algorithms in OBIA approaches, being greatly enabled by the advent of the commercial software eCognition. Therefore, in addition to the segmentation algorithm used, it is very important to count on tools to assess the quality of segmentation results from digital images in order to obtain the most similar segments to the real PCG objects. In this work, several factors affecting MRS such as the type of input image and the best MRS parameters (i.e., scale, compactness and shape), have been analysed. In this regard, more than 2800 segmentations focused on PCG land cover were conducted from four pre-processed Deimos-2 very high-resolution (VHR) satellite orthoimages taken in the Southeast of Spain (Almería). Specifically, one multispectral and one pansharpened Deimos-2 orthoimages, both with and without atmospheric correction were tested in this work. The free access AssesSeg command line tool, based on a modified version of the supervised discrepancy measure named Euclidean Distance 2 (ED2), was used to determine the best MRS parameters for all the VHR satellite images. According to both the supervised discrepancy measure ED2 and visual perception, the best segmentation on PCG was obtained over the atmospherically corrected pansharpened Deimos-2 orthoimage, achieving very good results.

Improvement of VHR Satellite Image Geometry with High Resolution Elevation Models

The number of high and very high resolution (VHR) optical satellite sensors, as well as the number of medium resolution satellites is continuously growing. However, not all high-resolution optical satellite imaging cameras have a sufficient and stable calibration in time. Due to their high agility in rotation, a quick change in viewing direction can lead to satellite attitude oscillation, causing image distortions and thus affecting image geometry and geo-positioning accuracy. This paper presents an approach based on re-projection of regularly distributed 3D ground points from object in image space, to detect and estimate the periodic distortions of Pléiades tri-stereo imagery caused by satellite attitude oscillations. For this, a hilly region was selected as a test site. Consequently, we describe a complete processing pipeline for computing the systematic height errors (deformations, waves) of the satellite-based digital elevation model by using a Lidar high resolution terrain model. Ground points with fixed positions, but with two elevations (actual and corrected) are then re-projected to the satellite images with the aid of the Rational Polynomial Coefficients (RPCs) provided with the imagery. Therefore, image corrections (displacements) are determined by computing the differences between the distinct positions of corresponding points in image space. Our experimental results in Allentsteig (Lower Austria) show that the systematic height errors of satellite-based elevation models cannot be compensated with an usual or even high number of Ground Control Points (GCPs) for RPC bias correction, due to insufficiently known image orientations. In comparison to a reference Lidar Digital Terrain Model (DTM), the computed elevation models show undulation effects with a maximum height difference of 0.88 m in along-track direction. With the proposed method, image distortions in-track direction with amplitudes of less than 0.15 pixels were detected. After applying the periodic distortion compensation to all three images, the systematic elevation discrepancies from the derived elevation models were successfully removed and the overall accuracy in open areas improved by 33% in the RMSE. Additionally, we show that a coarser resolution reference elevation model (AW3D30) is not feasible for improving the geometry of the Pléiades tri-stereo satellite imagery.

Integrated Preprocessing of Multitemporal Very-High-Resolution Satellite Images via Conjugate Points-Based Pseudo-Invariant Feature Extraction

Multitemporal very-high-resolution (VHR) satellite images are used as core data in the field of remote sensing because they express the topography and features of the region of interest in detail. However, geometric misalignment and radiometric dissimilarity occur when acquiring multitemporal VHR satellite images owing to external environmental factors, and these errors cause various inaccuracies, thereby hindering the effective use of multitemporal VHR satellite images. Such errors can be minimized by applying preprocessing methods such as image registration and relative radiometric normalization (RRN). However, as the data used in image registration and RRN differ, data consistency and computational efficiency are impaired, particularly when processing large amounts of data, such as a large volume of multitemporal VHR satellite images. To resolve these issues, we proposed an integrated preprocessing method by extracting pseudo-invariant features (PIFs), used for RRN, based on the conjugate points (CPs) extracted for image registration. To this end, the image registration was performed using CPs extracted using the speeded-up robust feature algorithm. Then, PIFs were extracted based on the CPs by removing vegetation areas followed by application of the region growing algorithm. Experiments were conducted on two sites constructed under different acquisition conditions to confirm the robustness of the proposed method. Various analyses based on visual and quantitative evaluation of the experimental results were performed from geometric and radiometric perspectives. The results evidence the successful integration of the image registration and RRN preprocessing steps by achieving a reasonable and stable performance.

Remote Sensing for Short-Term Economic Forecasts

Economic forecasts are an important instrument to judge the nation-wide economic situation. Such forecasts are mainly based on data from statistical offices. However, there is a time lag between the end of the reporting period and the release of the statistical data that arises for instance from the time needed to collect and process the data. To improve the forecasts by reducing the delay, it is of interest to find alternative data sources that provide information on economic activity without significant delays. Among others, satellite images are thought to assist here. This paper addresses the potential of earth observation imagery for short-term economic forecasts. The study is focused on the estimation of investments in the construction sector based on high resolution (HR) (10–20 m) and very high resolution (VHR) (0.3–0.5 m) images as well as on the estimation of investments in agricultural machinery based on orthophotos (0.1 m) simulating VHR satellite imagery. By applying machine learning it is possible to extract the objects of interest to a certain extent. For the detection of construction areas, VHR satellite images are much better suited than HR satellite images. VHR satellite images with a ground resolution of 30–50 cm are able to identify agricultural machinery. These results are promising and provide new and unconventional input for economic forecasting models.

Use of satellite imagery to identify southern right whales (Eubalaena australis) on a Southwest Atlantic Ocean breeding ground

AbstractSatellite imagery has been used to improve scientific research worldwide. In this study, the southern right whale (Eubalaena australis) was chosen to test the use of medium, high, and very high resolution (VHR) satellite images, on the Brazilian breeding ground. These images were used to identify the whales and were compared to aerial survey data collected in the same area. The VHR satellite images from the Pleiades‐1A satellite, available on Google Earth, displayed the best results when compared to those from Sentinel 2, Landsat 8, Rapid Eye, and Planet Scope. No significant differences were observed (Mann‐Whitney U test) between the possible whales recorded in the satellite images and of real whales recorded in situ by aerial surveys, while considering either the number of groups (p = .841, n = 5) or the total number of animals (p = .222, n = 5). Further, when using VHR images, the geographical positions of the whales recorded in situ had a positive correlation with the positions generated from the satellite images (Mantel test: r = 0.52, p = .001, n = 13). This technique may represent an important tool for detecting right whales, especially in countries where research funding is scarce.

Self-calibration dense bundle adjustment of multi-view Worldview-3 basic images

Very high-resolution satellite images (VHRSIs) with improved spatial resolution provide unprecedented opportunities to explore the geometric and semantic information of the world. Accordingly, to compensate for the bias of exterior orientation parameters, bundle adjustment of VHRSIs is required. Owing to the sparse points, the bundle adjustment of a few VHRSIs omits high-frequency errors and attitude jitters; therefore, the rational function model (RFM) can achieve an accuracy comparable to that of a rigorous sensor model (RSM), despite the significant difference between the RSM and RFM. In this study, we provide insight into the role of RSM in modeling attitude jitters for the Worldview-3 basic imagery product. Penalized splines were proposed to model the attitude jitters. After correcting the photogrammetric refraction using Saastamoinen’s model and light aberration, the RSM was built for agile satellites. The difference between RSM and RFM is consistent with attitude jitters, which is calculated using the penalized splines model and third-degree polyniomials. To fully explore the attitude jitters, a dense bundle adjustment was proposed to process 47 scenes of the Worldview-3 basic product imagery, which was provided by Johns Hopkins University Applied Physics Laboratory for the “Multi-View Stereo 3D Challenge.” Pairwise feature matching and feature tracking were adopted to generate over 16 000 tie-points (TPs), which were detected in an average of 9.38 images. The bundle adjustment residuals with RFM exhibited distortions similar to those of the attitude jitter. The experiments verified that the bundle adjustment with RFM introduced significant errors triggered by attitude jitters, with a maximum of over 5.0 pixels. The bundle adjustment with RSM could eliminate significant errors caused by attitude jitters and reduce the root mean square errors (RMSEs) from 1.12 pixels to 0.61 pixels. However, the basic product imagery of Worldview-3 exhibited errors in the interior orientation parameters. After analyzing the physical meanings of bias compensation, a self-calibration model was proposed. After comparing the shift compensation, affine compensation, self-calibration, temporal self-calibration, second-degree self-calibration, and second-degree polynomial models, the dense bundle adjustment with self-calibration was suggested because it could compensate for errors in the interior orientation parameters (IOPs) and obtain an accuracy similar to that of high-degree models. Thus, a self-calibration dense bundle adjustment with RSM compensates for the attitude jitters and errors in the IOPs, and achieves a remarkable accuracy of 0.49 pixels in the image coordinates.

An approach for building rooftop border extraction from very high-resolution satellite images

The development of a new Very High-Resolution (VHR) satellite sensor provides opportunities for mapping more details. The building extraction process is a challenging problem due to the misclassification between buildings and similar spectral objects. The main aim of this study is to introduce an approach to building rooftops border extraction. In this approach, a new index to highlight building areas using the capabilities of the WorldView-2 eight-bands VHR satellite images is presented. The developed index is then integrated into a complete approach to extract borders between buildings and their corresponding shadows. The building’s rooftops boundaries are determined using a novel algorithm. The proposed approach is applied to five study areas and compared with three recent approaches for building extraction. The results of the comparison have illustrated that the proposed approach achieves the highest overall accuracy (average of 92.20%) and has the ability for detecting building rooftops separated from their façades.

Automatic Detection of Individual Trees from VHR Satellite Images Using Scale-Space Methods.

This research investigates the use of scale-space theory to detect individual trees in orchards from very-high resolution (VHR) satellite images. Trees are characterized by blobs, for example, bell-shaped surfaces. Their modeling requires the identification of local maxima in Gaussian scale space, whereas location of the maxima in the scale direction provides information about the tree size. A two-step procedure relates the detected blobs to tree objects in the field. First, a Gaussian blob model identifies tree crowns in Gaussian scale space. Second, an improved tree crown model modifies this model in the scale direction. The procedures are tested on the following three representative cases: an area with vitellaria trees in Mali, an orchard with walnut trees in Iran, and one case with oil palm trees in Indonesia. The results show that the refined Gaussian blob model improves upon the traditional Gaussian blob model by effectively discriminating between false and correct detections and accurately identifying size and position of trees. A comparison with existing methods shows an improvement of 10–20% in true positive detections. We conclude that the presented two-step modeling procedure of tree crowns using Gaussian scale space is useful to automatically detect individual trees from VHR satellite images for at least three representative cases.

Slum Extraction from High Resolution Satellite Data using Mathematical Morphology based approach

ABSTRACT This paper proposes different mathematical morphology-based approaches to detect the urban slums from urban buildings using Very High Resolution (VHR) satellite images. Due to the rapid growth of urbanization activities, slum regions are the most evolving regions in South India whose locations are improper in official maps and statistics. Thus, automatic location identification of urban slums provides vital information for urban planners to formulate pro-poor policies and allocate resources properly. To detect the slums automatically from VHR satellite images, Multi Shape-Multi Size-Morphological Profile (MSh-MSi-MP) technique is proposed in this work. The development of this algorithm is motivated by modelling the urban slums using the conventional Morphological Profile (MP) which fails to model different structures in an image. On the other hand, the proposed MSh-MSi-MP is built by the sequence of morphological opening and closing profiles with different shapes and different sizes of structuring elements. Though MSh-MSi-MP yields high accuracy, some open areas in informal settlements and roofline structures of formal settlements are still classified as slums. To optimize these misclassification results, Morphological Spatial Pattern Analysis (MSPA) is used as a post-processing operation. The four different very high-resolution satellite images of Madurai city, South India acquired by WorldView-2 Sensor (1.84 m) proved the ability of the proposed approach to identify urban slums from other features by generating higher classification accuracy than any other methods.