WorldView-2 Images Research Articles

AUTOMATED DETECTION OF COLLAPSED BUILDINGS WITH USE OF OPTICAL AND SAR IMAGES, CASE STUDY: IZMIR EARTHQUAKE ON OCTOBER 30TH, 2020

Abstract. In this study, we have analysed the optical and SAR images both to detect the collapsed building automatically with the use of the cloud-based programming environment Google Colab Cloud environment. We have used the existing digital map of buildings which were provided by Here Maps Company, for each building feature, the histograms were generated both for optical and SAR images, the unmatched histograms on the optical image were mainly the destroyed buildings and newly established tent areas for the people who lost their homes. In the method, the most recent (before and after) optical images of the earthquake zone are taken. Some pre-processing steps were performed including principal component analysis, K-Means clustering. Then, the statistical values of area overlap with the building vectors are calculated and the threshold values are determined. SAR images are used to refine the results. he used optical satellite images are Worldview images with 30 cm GSD, and for SAR images, Sentinel 1 C band and ICEYE X band SAR images are used. Sentinel 1 and ICEYE images are provided from ESA.

DELINEATION OF URBAN FLOOD RISK AREAS USING GEOSPATIAL TECHNIQUE

The threat posed by urban flooding in most cities of the world is becoming alarming especially within the recent decades. This makes it necessary to Identify and delineate flood risk areas within cities in order to curb it menace. This study employs geospatial technique to delineate flood risk areas within Kano metropolis with a view to mitigating its impact on lives and properties. Digital Elevation Model (ASTER DEM 30m) was used to derive excess surface run-off attributes including flow direction and accumulation. Based on these attributes, flood risk areas were determined and delineated using buffer distances of 500 meters. World View image (30 cm spatial resolution) was used to identify the landuses at risk. The result from the analysis delineated flood risk areas at varying exposure levels (i.e high, moderate and low).It was evident that flood risk level within the metropolis corresponds to the pattern of surface run-off flow accumulation areas. Settlements and farmlands found within high accumulation areas along the floodplains of River Jakara (in the North and North-eastern part) and Kano-Zaria road (southern part) are at higher risk than those found on low accumulation areas. The study concluded that excess surface run-off flow direction and accumulation are among the fundamental factors determining the risk to urban flooding. The study recommends that with the ongoing level of urban development and impervious surface expansion, urban planners and policy makers should make use of the flow direction and accumulation maps in determining safer places for future developments

Mapping Leaf Area Index of restored mangroves using WorldView-2 imagery in Perancak Estuary, Bali, Indonesia

There have been efforts to restore and rehabilitate mangrove forests to compensate for their loss or degradation. Approaches for monitoring these efforts are increasingly developed, including assessment of the dynamic changes in restored mangroves using the biophysical tree parameter called Leaf Area Index (LAI). This study aimed to (1) apply a semi-empirical approach to estimate LAI using WorldView-2 imagery, (2) compare the LAI distributions of three different mangrove forest types (natural, mixed, and planted) at the mangrove restoration site in Perancak Estuary, Bali, Indonesia, and (3) evaluate the applicability of LAI as an indicator for monitoring mangrove growth in restoration areas. To achieve these objectives, a high-spatial-resolution WorldView-2 image data (2 m pixel size) and Normalized Difference Vegetation Index (NDVI) were used. A semi-empirical approach was used to correlate field LAI and NDVI values at the corresponding location. The results showed that the best regression function between the two variables was the third-order polynomial (R2 = 0.98). Overall, the estimated LAI values ranged from 0 to 9.56 (mean = 1.87, σ = 0.95, max. Accuracy = 97.6%) and differed between the three mangrove forest types. The comparison revealed that high LAI was associated with planted mangrove forest type where Rhizophora sp. dominated. It indicates that LAI is a useful indicator for assessing mangrove biophysical conditions and, as such, can be used for monitoring mangrove restoration activities and outcomes.

DEEP BAYESIAN ACTIVE LEARNING IN HIGH-RESOLUTION SATELLITE IMAGES FOR CHANGE DETECTION IN URBAN AND SUBURBAN AREAS

Abstract. In this work the problem of change detection in high-resolution (HR) satellite images is addressed. The active learning (AL) algorithm Bayesian active learning disagreement (BALD) is applied on WorldView images of urban and suburban areas in the island of Crete, Greece. Comparisons with results from random sampling (RS) on AL are carried out. Several cases of selecting different amounts of images in the training set of a convolutional neural network (CNN) are experimented. The results show that the validation accuracy of classification as changed or unchanged of the BALD algorithm is superior to that of the RS algorithm. Indeed, the BALD algorithm achieves zero test error against the test errors 34.6% and 38.5% of the RS algorithm. Actually, as the amount of training images increases, the accuracy also increases. Interesting experiments could be executed in the future utilizing estimators from robust statistics inside the AL acquisition function framework. Up to now in the literature no other work has appeared to present deep AL on WorldView images for change detection.

A novel automatic method on pseudo-invariant features extraction for enhancing the relative radiometric normalization of high-resolution images

ABSTRACT Relative radiometric normalization (RRN) is a critical preprocessing step that is widely applied to remote sensing data. Essential to RRN are the pseudo-invariant features (PIFs) – ground objects with invariant reflectance across the acquisition dates of multiple images. To meet the requirements of RRN for change detection in multi-temporal images, this paper proposed a novel automatic method for PIF extraction: first, we calculated spectral, spatial, and visual features for the images. Next, a set of change vectors were generated to quantify the features’ differences. Then, the initial PIF was obtained by synthesizing similar pixels from each change vector. Finally, an iterative linear regression was performed to generate the final PIFs. Furthermore, we employed the random sample consensus (RANSAC) algorithm to establish the RRN model for normalizing the target image. The methods and workflow of PIF extraction and the RRN model establishment were validated with two TripleSat-2 multi-spectral images with a spatial resolution of 3.2 metres. The applicability of the proposed PIF extraction method was further validated by Landsat-8 OLI, GF-1 WFV, Sentinel-2 MSI, and WorldView-2 images. The results showed that: (1) The proposed PIF extraction method can automatically obtain the PIFs with high precision. Based on the extracted PIFs, the RANSAC algorithm is capable of building the RRN model with a significant linear relationship. (2) The PIF extraction method is applicable to multi-spectral images with different spatial resolutions. (3) Compared with the multivariate alteration detection (MAD) and the iteratively reweighted MAD (IR-MAD), the proposed PIF extraction method achieved a better performance in selecting the pixels with smaller radiometric differences, which is helpful to reach a higher accuracy of the RRN model. (4) Differences in image brightness, spectral domain, and spatial domain are considered in the PIF extraction method with fewer parameters and high portability. (5) Spectral features are dominant to obtain ideal PIFs, and feature combinations can get the advantages to enhance the effectiveness of the PIFs.

DELINEATION OF URBAN FLOOD RISK AREAS USING GEOSPATIAL TECHNIQUE

The threat posed by urban flooding in most cities of the world is becoming alarming especially within the recent decades. This makes it necessary to Identify and delineate flood risk areas within cities in order to curb it menace. This study employs geospatial technique to delineate flood risk areas within Kano metropolis with a view to mitigating its impact on lives and properties. Digital Elevation Model (ASTER DEM 30m) was used to derive excess surface run-off attributes including flow direction and accumulation. Based on these attributes, flood risk areas were determined and delineated using buffer distances of 500 meters. World View image (30 cm spatial resolution) was used to identify the landuses at risk. The result from the analysis delineated flood risk areas at varying exposure levels (i.e high, moderate and low).It was evident that flood risk level within the metropolis corresponds to the pattern of surface run-off flow accumulation areas. Settlements and farmlands found within high accumulation areas along the floodplains of River Jakara (in the North and North-eastern part) and Kano-Zaria road (southern part) are at higher risk than those found on low accumulation areas. The study concluded that excess surface run-off flow direction and accumulation are among the fundamental factors determining the risk to urban flooding. The study recommends that with the ongoing level of urban development and impervious surface expansion, urban planners and policy makers should make use of the flow direction and accumulation maps in determining safer places for future developments.

Combining WV-2 images and tree physiological factors to detect damage stages of Populus gansuensis by Asian longhorned beetle (Anoplophora glabripennis) at the tree level

BackgroundAnoplophora glabripennis (Motschulsky), commonly known as Asian longhorned beetle (ALB), is a wood-boring insect that can cause lethal infestation to multiple borer leaf trees. In Gansu Province, northwest China, ALB has caused a large number of deaths of a local tree species Populus gansuensis. The damaged area belongs to Gobi desert where every single tree is artificially planted and is extremely difficult to cultivate. Therefore, the monitoring of the ALB infestation at the individual tree level in the landscape is necessary. Moreover, the determination of an abnormal phenotype that can be obtained directly from remote-sensing images to predict the damage degree can greatly reduce the cost of field investigation and management.MethodsMultispectral WorldView-2 (WV-2) images and 5 tree physiological factors were collected as experimental materials. One-way ANOVA of the tree’s physiological factors helped in determining the phenotype to predict damage degrees. The original bands of WV-2 and derived vegetation indices were used as reference data to construct the dataset of a prediction model. Variance inflation factor and stepwise regression analyses were used to eliminate collinearity and redundancy. Finally, three machine learning algorithms, i.e., Random Forest (RF), Support Vector Machine (SVM), Classification And Regression Tree (CART), were applied and compared to find the best classifier for predicting the damage stage of individual P. gansuensis.ResultsThe confusion matrix of RF achieved the highest overall classification accuracy (86.2%) and the highest Kappa index value (0.804), indicating the potential of using WV-2 imaging to accurately detect damage stages of individual trees. In addition, the canopy color was found to be positively correlated with P. gansuensis’ damage stages.ConclusionsA novel method was developed by combining WV-2 and tree physiological index for semi-automatic classification of three damage stages of P. gansuensis infested with ALB. The canopy color was determined as an abnormal phenotype that could be directly assessed using remote-sensing images at the tree level to predict the damage degree. These tools are highly applicable for driving quick and effective measures to reduce damage to pure poplar forests in Gansu Province, China.

Potential of machine learning and WorldView-2 images for recognizing endangered and invasive species in the Atlantic Rainforest

We found high accuracy classification (Fmeasure = 95%, on cross-validation) of Araucaria angustifolia (Bertol.) Kuntze, an endangered native species, and Hovenia dulcis Thunb. an aggressive, invasive alien species in WorldView-2 multispectral images. In applying machine learning algorithms, the spectral attributes mainly related to the near-infrared band were the most important for the models. It is difficult to classify tree species in tropical rainforests due to the high spectral response’s diversity of existing species, as well as to adjust efficient machine learning techniques and orbital image resolution. To explore the spectral and textural response of an endangered species (A. angustifolia) and an invasive species (H. dulcis) in WorldView-2 multispectral images, testing its recognition capability by machine learning techniques. We used a WordView-2 (2016) image with 0.5-m spatial resolution. Then we manually clipped the canopy area of the two species in this image using two compositions: True color composition (R=660 nm, G=545 nm, B=480 nm) and near-infrared composition (NIR-2=950 nm, G=545 nm, B=480 nm). Thus, we applied spectral and textural descriptors (pyramid histogram of oriented gradients—PHOG and Edge Filter), which selects the most representative features of the dataset. Finally, we used artificial neural networks (ANN) and random forest (RF) for tree species classification. The species classification was performed with high accuracy (Fmeasure = 95%, on cross-validation), essentially for spectral attributes using the near-infrared composition. RF surpassed the ANN classification rates and also proved to be more stable and faster for training and testing. The WorldView-2 multispectral sensor showed the potential to provide sufficient information for classifying two species, proving its usefulness in this phytophysiognomy where hyperspectral sensors are generally used for this type of classification.

Improving Parcel-Level Mapping of Smallholder Crops from VHSR Imagery: An Ensemble Machine-Learning-Based Framework

Explicit spatial information about crop types on smallholder farms is important for the development of local precision agriculture. However, due to highly fragmented and heterogeneous cropland landscapes, fine-scale mapping of smallholder crops, based on low- and medium-resolution satellite images and relying on a single machine learning (ML) classifier, generally fails to achieve satisfactory performance. This paper develops an ensemble ML-based framework to improve the accuracy of parcel-level smallholder crop mapping from very high spatial resolution (VHSR) images. A typical smallholder agricultural area in central China covered by WorldView-2 images is selected to demonstrate our approach. This approach involves the task of distinguishing eight crop-level agricultural land use types. To this end, six widely used individual ML classifiers are evaluated. We further improved their performance by independently implementing bagging and stacking ensemble learning (EL) techniques. The results show that the bagging models improved the performance of unstable classifiers, but these improvements are limited. In contrast, the stacking models perform better, and the Stacking #2 model (overall accuracy = 83.91%, kappa = 0.812), which integrates the three best-performing individual classifiers, performs the best of all of the built models and improves the classwise accuracy of almost all of the land use types. Since classification performance can be significantly improved without adding costly data collection, stacking-ensemble mapping approaches are valuable for the spatial management of complex agricultural areas. We also demonstrate that using geometric and textural features extracted from VHSR images can improve the accuracy of parcel-level smallholder crop mapping. The proposed framework shows the great potential of combining EL technology with VHSR imagery for accurate mapping of smallholder crops, which could facilitate the development of parcel-level crop identification systems in countries dominated by smallholder agriculture.

Mapping the Urban Atmospheric Carbon Stock by LiDAR and WorldView-3 Data

Currently, the worsening impacts of urbanizations have been impelled to the importance of monitoring and management of existing urban trees, securing sustainable use of the available green spaces. Urban tree species identification and evaluation of their roles in atmospheric Carbon Stock (CS) are still among the prime concerns for city planners regarding initiating a convenient and easily adaptive urban green planning and management system. A detailed methodology on the urban tree carbon stock calibration and mapping was conducted in the urban area of Brussels, Belgium. A comparative analysis of the mapping outcomes was assessed to define the convenience and efficiency of two different remote sensing data sources, Light Detection and Ranging (LiDAR) and WorldView-3 (WV-3), in a unique urban area. The mapping results were validated against field estimated carbon stocks. At the initial stage, dominant tree species were identified and classified using the high-resolution WorldView3 image, leading to the final carbon stock mapping based on the dominant species. An object-based image analysis approach was employed to attain an overall accuracy (OA) of 71% during the classification of the dominant species. The field estimations of carbon stock for each plot were done utilizing an allometric model based on the field tree dendrometric data. Later based on the correlation among the field data and the variables (i.e., Normalized Difference Vegetation Index, NDVI and Crown Height Model, CHM) extracted from the available remote sensing data, the carbon stock mapping and validation had been done in a GIS environment. The calibrated NDVI and CHM had been used to compute possible carbon stock in either case of the WV-3 image and LiDAR data, respectively. A comparative discussion has been introduced to bring out the issues, especially for the developing countries, where WV-3 data could be a better solution over the hardly available LiDAR data. This study could assist city planners in understanding and deciding the applicability of remote sensing data sources based on their availability and the level of expediency, ensuring a sustainable urban green management system.

An automatic approach for detecting building fences from high-resolution images: the case study of Makkah, Saudi Arabia

Abstract In the light of the constantly expanding technological advancements concerning high-resolution satellites and imaging techniques for investigative and information uses it is important to look at how these algorithms progress to acquire more reliable data in a much efficient manner. This study aims to (1) investigate the ability to use remote sensing and geographic information system techniques to extract the detecting building edges in the City of Makkah and (2) investigate new machine learning techniques to derive the illegal building fences in the study area. Two WorldView-3 images will be the first obtained for the City of Makkah in 2016 and 2018. Convolutional neural networks algorithm will be investigated to detect all the fences within the two images. These traits have been utilized to create automated object detection techniques, which are a core requirement of information extraction and large frame analysis of images covering large expanses of land. In high-resolution images, object detection identifies objects belonging to a class, locating them using a bounding box. Based on satellite images time series, the outputs will detect the changes that occurred during 2016 and 2018. A web map application will be designed as the primary tool to make it easier, illustrating the differences between the main changes. Evaluation of binary classifiers approach will be used to evaluate the outcomes of building fences based on several performances that measure data interpretation. Preliminary findings will illustrate the precision and accuracy of the used machine learning algorithm. The research findings can contribute to the federal/municipal authorities and act as a generic indicator for targeting building fences for urban areas and/or suburban areas.

Detection of Actionable Oil using RADARSAT-2 and Simulated RADARSAT Constellation Mission Data

Abstract 688970 For oil spill response, one of the key parameters is detection of actionable oil. Actionable oil (AO), which tends to be thick, emulsified oil, refers to oil that can be cleaned-up versus non-actionable oil (sheen) which cannot be readily cleaned-up. Previous studies by MDA of a controlled oil spills in the North Sea have shown the capability of RADARSAT-2 quad polarized data to detect AO using the entropy parameter (H) derived from the Cloude-Pottier decomposition. H → 0 for oil-free water and H → 1 in the presence of an oil slick. To further test the detection of AO, RADARSAT-2, ASTER, WorldView imagery and in situ measurement were acquired April 25, 2017 at the MC20 site off Louisiana. Five oil thickness classes ranging from 1 micron to 100 microns were derived from a maximum likelihood classifier based on the ASTER and WorldView images and in situ samples. For oil-free water, the average H was 0.13 and 0.62 for the slick. There was good correlation between the variability of H and the oil thickness classes. Specifically, larger H was correlated with oil thickness in the 50 – 200 micron range and smaller H was correlated with oil in the 1 micro range. Not surprising there was overlap in H for area were the oil thickness was ~ 1 micron and the area deemed to be slick-free. The results indicate that actionable oil can be discriminated from non-actionable oil based on the relative difference of H. Although these results are encouraging, one of the operational limitations is based on the use of the relatively small swath-width (25 km – 50 km) of the RADARSAT-2 quad polarized mode. The impact of the swath-width can be mitigated with data from the RADARSAT Constellation Mission (RCM). The RCM has a compact polarimetry (CP) mode that provides more polarimetric information than dual polarized modes, less than quad polarized modes, but is available for swath widths up to 500 km. Analysis of simulated SC50 RCM data (50 m resolution, 350 km swath width) derived from the aforementioned RADARSAT-2 image shows similar oil-slick variability that was observed in the RADARSAT-2 image and hence the capability to detect AO.

Changes in seagrass carbon stock: implications of decreasing area and percentage cover of seagrass beds in Barranglompo Island, Spermonde archipelago, South Sulawesi, Indonesia

Seagrass ecosystems in shallow waters, including in highly dense small islands, are vulnerable to anthropogenic activities and may lead to a decreasing in seagrass area and percentage cover. As a result, it will negatively affect climate change mitigation, i.e. a decrease in seagrass carbon stock. This research was conducted to evaluate seagrass carbon stock changes in Barranglompo Island, Spermonde archipelago, in 2001 and 2017. Carbon stock estimation was carried out by collecting seagrass biomass based on the category of a sparse, medium, and dense seagrass coverage (each with 20 replications) and analysing their carbon concentrations. Changes in carbon sequestration were analysed using an estimated approach to the changes in the seagrass bed areas. Landsat and Worldview-2 image analysis were utilized to estimate the area of seagrass beds in 2001 and 2017, respectively. Results showed a decrease in seagrass beds by 9.9 ha, from 66.8 ha in 2001 to 56.9 ha in 2017, or an average of 0.62 ha per year. Based on that, it was estimated that seagrass carbon stocks had declined from 74.19 tons in 2001 to 65.21 tons in 2017 or decreased by an average of 0.75% per year. Anthropogenic activities, such as boat anchors as well as household waste disposal in the seagrass area, are considered having the potential in causing a decrease in seagrass area and the percentage coverage.

Spectral index to improve the extraction of built-up area from WorldView-2 imagery

Globally, the unprecedented increase in population in many cities has led to rapid changes in urban landscape, which requires timely assessments and monitoring. Accurate determination of built-up information is vital for urban planning and environmental management. Often, the determination of the built-up area information has been dependent on field surveys, which is laborious and time-consuming. Remote sensing data are the only option for deriving spatially explicit and timely built-up area information. There are few spectral indices for built-up areas and often not accurate as they are specific to impervious material, age, colour, and thickness, especially using higher resolution images. The objective of this study is to test the utility of a new built-up extraction index (NBEI) using WorldView-2 (WV-2) to improve built-up material mapping irrespective of material type, age, and color. The new index was derived from spectral bands such as green, red edge, NIR1, and NIR2 bands that profoundly explain the variation in built-up areas on WV-2 image. The result showed that NBEI improves the extraction of built-up areas with high accuracy [area under the receiver operating characteristic curve, ( AUROC ) = ∼ 0.82] compared to the existing indices such as built-up area index (AUROC = ∼ 0.73), built-up spectral index (AUROC = ∼ 0.78), red edge/green index (AUROC = ∼ 0.71) and WorldView-Built-up Index (WV-BI) (AUROC = ∼ 0.67). The study demonstrated that the new built-up index could extract built-up areas using high-resolution images. The performance of NBEI could be attributed to the fact that it is not material-specific, and would be necessary for urban area mapping.

Accurate mapping of Brazil nut trees (Bertholletia excelsa) in Amazonian forests using WorldView-3 satellite images and convolutional neural networks

The commercialization of Brazil nuts, seeds of Bertholletia excelsa Bonpl. (Lecythidaceae), represents one of the main income-generation activities for local and indigenous people from the Brazilian Amazon region. Because trees of B. excelsa grow and bear fruit almost exclusively in natural forests, information on their spatial distribution is crucial for nut harvest planning. However, this information is difficult to obtain with traditional approaches such as ground-based surveys. Here, we show the potential of convolutional neural networks (CNNs) and WorldView-3 satellite images (pixel size = 30 cm) to map individual tree crowns (ITCs) and groves of B. excelsa in Amazonian forests. First, we manually outlined B. excelsa ITCs in the WorldView-3 images using field-acquired geolocation information. Then, based on ITC boundaries, we sequentially extracted image patches and selected 80% of them for training and 20% for testing. We trained the DeepLabv3+ architecture with three backbones: ResNet-18, ResNet-50, and MobileNetV2. The average producer's accuracy was 93.87 ± 0.85%, 93.89 ± 1.6% and 93.47 ± 3.6% for ResNet-18, ResNet-50 and MobileNetV2, respectively. We then developed a new random patch extraction training strategy and assessed how a reduction in the percentage of training patches impacted the classification accuracy. To illustrate the robustness of the new training strategy, similar F1-scores were achieved whether 80% or 10% of the total number of patches were used to train the CNN model. By analyzing the feature maps derived from ResNet-18, we found that the shadow of emergent B. excelsa trees are important for their discrimination. Geometric distortions in the WorldView-3 images resulting from extreme off-nadir viewing angles compromise the presence of shadows, thus potentially hampering B. excelsa detection. Our results show that ITCs and groves of B. excelsa can be mapped by integrating CNNs and very-high-resolution (VHR) satellite images, paving the way for monitoring this important tree species in large tracts of Amazonian forests.

Monitoring Tamarix Changes Using WorldView-2 Satellite Imagery in Grand Canyon National Park, Arizona

Remote sensing methods are commonly used to monitor the invasive riparian shrub tamarisk (Tamarix spp.) and its response to the northern tamarisk beetle (D. carinulata), a specialized herbivore introduced as a biocontrol agent to control tamarisk in the Southwest USA in 2001. We use a Spectral Angle Mapper (SAM) supervised classification method with WorldView-2 (2 m spatial resolution) multispectral images from May and August of 2019 to map healthy tamarisk, canopy dieback, and defoliated tamarisk over a 48 km segment of the Colorado River in the topographically complex Grand Canyon National Park, where coarse-resolution satellite images are of limited use. The classifications in May and August produced overall accuracies of 80.0% and 83.1%, respectively. Seasonal change detection between May and August 2019 indicated that 47.5% of the healthy tamarisk detected in May 2019 had been defoliated by August 2019 within the WorldView-2 image extent. When compared to a previously published tamarisk map from 2009, derived from multispectral aerial imagery, we found that 29.5% of healthy tamarisk canopy declined between 2009 and 2019. This implies that tamarisk beetle impacts are continuing to accumulate even though land managers have noted the presence of the beetles in this reach of the river for 7 years since 2012.

A New Individual Tree Species Recognition Method Based on a Convolutional Neural Network and High-Spatial Resolution Remote Sensing Imagery

Tree species surveys are crucial to forest resource management and can provide references for forest protection policy making. The traditional tree species survey in the field is labor-intensive and time-consuming, supporting the practical significance of remote sensing. The availability of high-resolution satellite remote sensing data enable individual tree species (ITS) recognition at low cost. In this study, the potential of the combination of such images and a convolutional neural network (CNN) to recognize ITS was explored. Firstly, individual tree crowns were delineated from a high-spatial resolution WorldView-3 (WV3) image and manually labeled as different tree species. Next, a dataset of the image subsets of the labeled individual tree crowns was built, and several CNN models were trained based on the dataset for ITS recognition. The models were then applied to the WV3 image. The results show that the distribution maps of six ITS offered an overall accuracy of 82.7% and a kappa coefficient of 0.79 based on the modified GoogLeNet, which used the multi-scale convolution kernel to extract features of the tree crown samples and was modified for small-scale samples. The ITS recognition method proposed in this study, with multi-scale individual tree crown delineation, avoids artificial tree crown delineation. Compared with the random forest (RF) and support vector machine (SVM) approaches, this method can automatically extract features and outperform RF and SVM in the classification of six tree species.

Discussion of different Remote sensing satellite possibilities for scientifical Earth observations

More than 1,000 satellites are launched into space, and they differ in their functions, rotation orbits, resolution, and other properties. Scientists divide the satellites into low-resolution, medium-resolution, high-resolution, and very high-resolution satellites by their properties. Now, the biggest challenge facing scientists is to use some of these different resolution images in their field. To get the expected result, it is very important to analyze the image that needs an which gives more accurate results. Therefore, the main attention of this article is aimed to find the answer to these problems. In this article 3 satellite images which have different resolution are analyzed. The possibility of middle-resolution images of MODIS, high-resolution images of Landsat, and very high-resolution images of WorldView-2 (WV-2) satellites using GIS are analyzed. A research area was the Syrdarya region, and downloaded different images of satellites of this area and compared with using e Cognition. According to the results, a more accurate satellite image for irrigation sets information is WorldView-2 images. In comparison analysis, it shows more accurate properties than other satellite images. As irrigation sets are small objects for the analysis, very high spatial resolution satellite images are important. Water discharge and surface change happen very fast; thus, it requires daily monitoring of the condition. And in this case, the temporal resolution of the MODIS and Landsat is 16 day, and it is a too long period.

Research on Making Orthophotos of Mountainous Areas Based on WorldView-2 Images

Combining the RPC rational polynomial coefficient model, using WorldView-2 remote sensing images, combined with field surveyed control points, survey area digital elevation model DEM and other data to produce the mountain orthophoto map, combined with the detection points to analyze the accuracy of the results, solve the problem Topographic maps are difficult to measure and are subject to flight restrictions.

Irrigation network extraction in arid regions with using worldview-2 satellite data

After the 2000s, the launch of very high-resolution satellites provided great water and irrigation network management personnel opportunities. Now, the water management staff have the opportunity to study and monitor water supply systems and exploitation conditions of irrigation systems remotely via satellite imagery. By using those satellite images, specialists can search for water bodies, detect defected place of irrigation systems, and monitor their technical condition. Another advantage of satellite imagery is that they capture large areas of the Earth, keeping water systems under control in large areas. Therefore, the use of very high-resolution images has greatly developed in the water branch since the 2000s. The creation of different water extraction methods, models, indexes, and using different layers in the analysis for different regions using different satellites with very high resolution is developed. These indexes and layers are so numerous that they are now over 100. The user has difficulty getting any of them in the analyzes. Therefore, in this article, we have studied more than 50 water extraction methods, which gave positive and accurate results in an arid region. From those 50 methods, separated 10 the most effective methods and tested with WorldView-2 image analysis in the arid region and the water-rich region of Syrdarya region. According to the results of the analysis recommend the highest accuracy method for arid areas. Results show that water extraction using NIR2 layer of the WorldView-2 satellite images is the most accurate method than other methods. The accuracy of the results was 94 %. The analysis found the irrigation systems filled with sand and vegetation.