Cloud Shadows Research Articles

Detecting Offshore Drilling Rigs with Multitemporal NDWI: A Case Study in the Caspian Sea

Offshore drilling rigs are the foundation of oil and gas exploitation in water areas. Their spatial and temporal distribution, state attributes and other information directly reflect the exploitation level of regional oil and gas resources. Therefore, it is very important to build an automatic detecting method for offshore drilling rigs with good performance to accurately capture the temporal and spatial distribution and state of oil and gas exploitation activities. At present, there are two main groups of methods for offshore drilling rigs: invariant feature-based methods and nighttime firelight-based methods. Methods based on invariant location are more subjective in terms of their parameter settings and require intensive computation. Nighttime light-based methods are largely unable to identify offshore drilling rigs without associated waste gas ignition. Furthermore, multiple offshore drilling rigs in close proximity to one another cannot be effectively distinguished with low spatial resolution imagery. To address these shortcomings, we propose a new method for the automatic identification of offshore drilling rigs based on Landsat-7 ETM+ images from 2018 to 2019, taking the Caspian Sea as the research area. We build a nominal annual cloud and cloud shadow-free Normalized Difference Water Index (NDWI) composite by designing an optimal NDWI compositing method based of the influence of cloud and cloud shadow on the NDWI values of water, bare land (island) and offshore drilling rigs. The classification of these objects is simultaneously done during the compositing process, with the following rules: water body (Max_NDWI > 0.55), bare land (island) (Min_NDWI < −0.05) and offshore drilling rig (0 < Mean_NDWI < 0.4). A threshold segmentation and postprocessing were carried out to further refine the results. Using this method, 497 offshore platforms were automatically identified using a nominal annual cloud and cloud shadow-free NDWI composite image and Google Earth Engine. Validation using Sentinel-2 Multispectral Imager (MSI) and Google Earth images demonstrated that the correct rate of offshore drilling rig detection in the Caspian Sea is 90.2%, the missing judgment rate is 5.3% and the wrong judgment rate is 4.5%, proving the performance of the proposed method. This method can be used to identify offshore drilling rigs within a large water surface area relatively quickly, which is of great significance for exploring the exploitation status of offshore oil and gas resources. It can also be extended to finer spatial resolution optical remote sensing images; thus small-size drilling rigs can be effectively detected.

Reply to Comment on “Comparison of Cloud Cover Detection Algorithms on Sentinel–2 Images of the Amazon Tropical Forest”

In their comments about our paper, the authors remark on two issues regarding our results relating to the MACCS-ATCOR Joint Algorithm (MAJA). The first relates to the sub-optimal performance of this algorithm under the conditions of our tests, while the second corresponds to an error in our interpretation of MAJA’s bit mask. To answer the first issue, we acknowledge MAJA’s capacity to improve its performance as the number of images increases with time. However, in our paper, we used the images we had available at the time we wrote our paper. Regarding the second issue, we misread the MAJA’s bit mask and mistakenly labelled shadows as clouds. We regret our error and here we present the updated tables and images. We corrected our estimation and, consequently, there is an increment in MAJA’s accuracy in the detection of clouds and cloud shadows. However, these increments are not enough to change the conclusion of our original paper.

A method for predicting large-area missing observations in Landsat time series using spectral-temporal metrics

Combined with increasing computing ability, the free and open access to Landsat archive has enabled the changes on the Earth’s surface to be monitored for almost 50 years. However, due to missing observations that result from clouds, cloud shadows, and scan line corrector failure, the Landsat data record is neither a continuous nor consistent time series. We present a new gap-filling method, Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM), which uses spectral-temporal metrics computed from Landsat one-year time series and the k-Nearest Neighbor (k-NN) regression. Herein, we demonstrate the performance of MOPSTM by using five, nearly cloud-free, full scene Landsat images from Kenya, Finland, Germany, the USA, and China. Cloud masks from the images with extensive cloud cover were used to simulate large-area gaps, with the highest value we tested being 92% of missing data. The gap-filling accuracy was assessed quantitatively considering all five sites and different land use/land cover types, and the MOPSTM algorithm performed better than the spectral-angle-mapper based spatiotemporal similarity (SAMSTS) gap-filling algorithm. The mean RMSE values of MOPSTM were 0.010, 0.012, 0.025, 0.012, and 0.018 for the five sites, while those of SAMSTS were 0.011, 0.017, 0.038, 0.014, and 0.023, respectively. Furthermore, MOPSTM had mean coefficient of determination (R2) values of 0.90, 0.86, 0.78, 0.92, and 0.89, which were higher than those for SAMSTS (0.84, 0.75, 0.55, 0.89, and 0.83). The performance of MOPSTM was not considerably affected by image gap sizes as images ranging from gap sizes of 51% of the image all the way to 92% of the image yielded similar gap-filling accuracy. Also, MOPSTM does not require local parametertuning except for the k values in the k-NN regression, and it can make a gap-free image from any acquisition date. MOPSTM provides a new spectral-temporal approach to generate the gap-free imagery for typical Landsat applications, such as land use, land cover, and forest monitoring.

Automatic Shadow Compensation Based on Improved Wallis Model for High Resolution Remote Sensing Images

Objectives Shadows in high-resolution remote sensing images will cause objects information loss and image quality decline, which is not beneficial for relative applications. Current shadow compensation methods often take advantage of non-shadow information around the shadow area to increase the brightness, whereas there is an unsolved problem that the contrast cannot be enhanced well enough and self-adaptively. Wallis filter principle has been used in image dodging. However, when it is used in shadow compensation, contrast improvement is not as good as other methods, leading to poor compensation results. Therefore, an improved Wallis model compensation method is proposed in this paper to enhance the brightness and contrast better to restore the shaded information. Methods First, by adding compensation strength and stretch parameters, an improved Wallis model is designed. The strength parameter is positive to the brightness and contrast, and the stretch parameter is sensitive to the contrast. Therefore, the improved Wallis model is more efficient to adjust brightness and contrast. Moreover, an automatic parameters calculation strategy is further explored to customize a suitable compensation model for each shadow area. On one hand, the brightness average and deviation of the adjacent non-shadow region are calculated and used as the compensation target values. On the other hand, based on searching the same kinds of points around shadow boundaries, a series of non-shadow and shadow feature points are matched. Assumed the feature value of the non-shadow point is the approximate value of its responding shadow point, they can be used to calculate strength and stretch parameters automatically. Lastly, the brightness of each pixel in different shadow regions will be compensated by customized models to cover the shaded information self-adaptively. Results In this paper, three sets of comparative experiments are set up to analyze and compare the part compensation methods algorithm, original Wallis method in three images with ordinary object shadows and cloud shadows, respectively. The differences in brightness average and gradient average between the compensated value and the non-shadow target value are used to evaluate the compensation quality. The experimental results show that: (1) Original Wallis model is useful to enhance brightness to some extent, while it is insufficient to improve contrast. As a consequence, the visual compensated results are not acceptable as the other two methods. (2) Part compensation method can improve brightness and contrast effectively. However, it cannot adjust self-adaptively according to each shadow regions condition, leading to some over-compensated and some insufficient compensation results in the same image. (3) The proposed method results indicate the best compensation quality in different shadow regions because the improved Wallis model is more pointed to improve contrast and the automatically calculated parameters values are suitable to customize the compensation model for each shadow region. Conclusions Aiming at the contrast improvement of the current algorithm of automatic shadow compensation, an automatic compensation method based on an improved Wallis model is proposed in this paper. The experimental results show that the newly designed model is more effective to enhance brightness and contrast, which is useful for finding suitable parameters values. Combining with the strategy of automatic parameters calculation, its customized model can adaptively compensate each shadow. However, it should be pointed out that there are still some limitations about the shadow border compensation and the internal difference in one shadow area, which need to be further studied.

A multi-scale convolutional neural network for automatic cloud and cloud shadow detection from Gaofen-1 images

A multi-scale convolutional neural network for automatic cloud and cloud shadow detection from Gaofen-1 images

Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest

Urban areas represent the primary source region of greenhouse gas emissions. Mapping urban areas is essential for understanding land cover change, carbon cycles, and climate change (urban areas also refer to impervious surfaces, i.e., artificial cover and structures). Remote sensing has greatly advanced urban areas mapping over the last several decades. At present, we have entered the era of big data. Long time series of satellite data such as Landsat and high-performance computing platforms such as Google Earth Engine (GEE) offer new opportunities to map urban areas. The objective of this research was to determine how annual time series images from Landsat 8 Operational Land Imager (OLI) can effectively be composed to map urban areas in three cities in China in support of GEE. Three reducer functions, ee.Reducer.min(), ee.Reducer.median(), and ee.Reducer.max() provided by GEE, were selected to construct four schemes to synthesize the annual intensive time series Landsat 8 OLI data for three cities in China. Then, urban areas were mapped based on the random forest algorithm and the accuracy was evaluated in detail. The results show that (1) the quality of annual composite images was improved significantly, particularly in reducing the impact of cloud and cloud shadows, and (2) the annual composite images obtained by the combination of multiple reducer functions had better performance than that obtained by a single reducer function. Further, the overall accuracy of urban areas mapping with the combination of multiple reducer functions exceeded 90% in all three cities in China. In summary, a suitable combination of reducer functions for synthesizing annual time series images can enhance data quality and ensure differences between characteristics and higher precision for urban areas mapping.

Cloud and Cloud-Shadow Detection for Applications in Mapping Small-Scale Mining in Colombia Using Sentinel-2 Imagery

Small-scale placer mining in Colombia takes place in rural areas and involves excavations resulting in large footprints of bare soil and water ponds. Such excavated areas comprise a mosaic of challenging terrains for cloud and cloud-shadow detection of Sentinel-2 (S2A and S2B) data used to identify, map, and monitor these highly dynamic activities. This paper uses an efficient two-step machine-learning approach using freely available tools to detect clouds and shadows in the context of mapping small-scale mining areas, one which places an emphasis on the reduction of misclassification of mining sites as clouds or shadows. The first step is comprised of a supervised support-vector-machine classification identifying clouds, cloud shadows, and clear pixels. The second step is a geometry-based improvement of cloud-shadow detection where solar-cloud-shadow-sensor geometry is used to exclude commission errors in cloud shadows. The geometry-based approach makes use of sun angles and sensor view angles available in Sentinel-2 metadata to identify potential directions of cloud shadow for each cloud projection. The approach does not require supplementary data on cloud-top or bottom heights nor cloud-top ruggedness. It assumes that the location of dense clouds is mainly impacted by meteorological conditions and that cloud-top and cloud-base heights vary in a predefined manner. The methodology has been tested over an intensively excavated and well-studied pilot site and shows 50% more detection of clouds and shadows than Sen2Cor. Furthermore, it has reached a Specificity of 1 in the correct detection of mining sites and water ponds, proving itself to be a reliable approach for further related studies on the mapping of small-scale mining in the area. Although the methodology was tailored to the context of small-scale mining in the region of Antioquia, it is a scalable approach and can be adapted to other areas and conditions.

The Third Dimention in a Landscape

The present study examines the principles of conveying the third dimension in landscape painting. The author analyzes the recommendations provided in J. Littlejohns’ manual entitled “The Composition of a Landscape” [London, 1931]. J. Littlejohns describes four methods of showing depth in a landscape painting, each illustrated with pictorial composition schemes: 1) portrayal of long roads, which allows one to unveil the plasticity of the land surface; 2) creation of a “route” for the viewer by means of a well-thought-out arrangement of natural landforms; 3) introduction of vertically and horizontally flowing streams of water on different picture planes; 4) depiction of cloud shadows on a distinctly hilly landscape. The author of the article compares the schemes contained in the manual of J. Littlejohns with the works of G. G. Nissky, which enables readers to comprehend and reflect on the compositions of the masterpieces created by a prominent figure in Soviet art; on the other hand, Nissky’s landscape paintings open for a deeper understanding of the meaning and effectiveness of the methods proposed by J. Littlejohns. The outlined composition techniques are certainly relevant for contemporary artists (painters, graphic artists, animators, designers, etc.) as they make it possible to achieve the plastic expressiveness of a three-dimensional space in a twodimensional image.

Deep Learning Based Thin Cloud Removal Fusing Vegetation Red Edge and Short Wave Infrared Spectral Information for Sentinel-2A Imagery

Thin clouds seriously affect the availability of optical remote sensing images, especially in visible bands. Short-wave infrared (SWIR) bands are less influenced by thin clouds, but usually have lower spatial resolution than visible (Vis) bands in high spatial resolution remote sensing images (e.g., in Sentinel-2A/B, CBERS04, ZY-1 02D and HJ-1B satellites). Most cloud removal methods do not take advantage of the spectral information available in SWIR bands, which are less affected by clouds, to restore the background information tainted by thin clouds in Vis bands. In this paper, we propose CR-MSS, a novel deep learning-based thin cloud removal method that takes the SWIR and vegetation red edge (VRE) bands as inputs in addition to visible/near infrared (Vis/NIR) bands, in order to improve cloud removal in Sentinel-2 visible bands. Contrary to some traditional and deep learning-based cloud removal methods, which use manually designed rescaling algorithm to handle bands at different resolutions, CR-MSS uses convolutional layers to automatically process bands at different resolution. CR-MSS has two input/output branches that are designed to process Vis/NIR and VRE/SWIR, respectively. Firstly, Vis/NIR cloudy bands are down-sampled by a convolutional layer to low spatial resolution features, which are then concatenated with the corresponding features extracted from VRE/SWIR bands. Secondly, the concatenated features are put into a fusion tunnel to down-sample and fuse the spectral information from Vis/NIR and VRE/SWIR bands. Third, a decomposition tunnel is designed to up-sample and decompose the fused features. Finally, a transpose convolutional layer is used to up-sample the feature maps to the resolution of input Vis/NIR bands. CR-MSS was trained on 28 real Sentinel-2A image pairs over the globe, and tested separately on eight real cloud image pairs and eight simulated cloud image pairs. The average SSIM values (Structural Similarity Index Measurement) for CR-MSS results on Vis/NIR bands over all testing images were 0.69, 0.71, 0.77, and 0.81, respectively, which was on average 1.74% higher than the best baseline method. The visual results on real Sentinel-2 images demonstrate that CR-MSS can produce more realistic cloud and cloud shadow removal results than baseline methods.

Comparison of Masking Algorithms for Sentinel-2 Imagery

Masking of clouds, cloud shadow, water and snow/ice in optical satellite imagery is an important step in automated processing chains. We compare the performance of the masking provided by Fmask (“Function of mask” implemented in FORCE), ATCOR (“Atmospheric Correction”) and Sen2Cor (“Sentinel-2 Correction”) on a set of 20 Sentinel-2 scenes distributed over the globe covering a wide variety of environments and climates. All three methods use rules based on physical properties (Top of Atmosphere Reflectance, TOA) to separate clear pixels from potential cloud pixels, but they use different rules and class-specific thresholds. The methods can yield different results because of different definitions of the dilation buffer size for the classes cloud, cloud shadow and snow. Classification results are compared to the assessment of an expert human interpreter using at least 50 polygons per class randomly selected for each image. The class assignment of the human interpreter is considered as reference or “truth”. The interpreter carefully assigned a class label based on the visual assessment of the true color and infrared false color images and additionally on the bottom of atmosphere (BOA) reflectance spectra. The most important part of the comparison is done for the difference area of the three classifications considered. This is the part of the classification images where the results of Fmask, ATCOR and Sen2Cor disagree. Results on difference area have the advantage to show more clearly the strengths and weaknesses of a classification than results on the complete image. The overall accuracy of Fmask, ATCOR, and Sen2Cor for difference areas of the selected scenes is 45%, 56%, and 62%, respectively. User and producer accuracies are strongly class- and scene-dependent, typically varying between 30% and 90%. Comparison of the difference area is complemented by looking for the results in the area where all three classifications give the same result. Overall accuracy for that “same area” is 97% resulting in the complete classification in overall accuracy of 89%, 91% and 92% for Fmask, ATCOR and Sen2Cor respectively.

Cloud shadow detection removal for satellite supportive health care systems: research solution towards Australian Bushfire

Shadows limit numerous remote detecting applications, for example, characterization, target identification, and change discovery. Shadow recognition in high spatial goals remote detecting image is basic for finding land targets. Most current shadow location techniques use the histogram edge of unearthly qualities to recognize the shadows and nonshadows legitimately called hard parallel shadow. In this paper, we proposed another shadow identification calculation utilizing the HSI shading model and Daubechies complex wavelet change (DCWT). Since the pixel grid is a largescale framework, in the event that we apply calculation legitimately on the crude pixel space, it will be calculation escalated to compute the likeness network. Clearly, the exhibition of edge put together techniques vigorously depend with respect to the chose limit. All the while, these limit based strategies do not consider any spatial data. To beat these weaknesses, a delicate shadow portrayal strategy is created by bringing the idea of darkness into shadow identification, and technique is proposed so as to utilize neighborhood data. To take care of this issue, we isolate the lattice into a few squares and afterward applying calculation to distinguish shadows in H, S and I segments individually. At that point, three identified images are melded to get a last shadow recognition result. Near tests are performed for Kmeans and edge division techniques. The trial results show that higher discovery precision of the proposed approach is acquired, and it can take care of the issues of bogus excusals of K-means and limit division technique. Tests on remote detecting images have demonstrated that the proposed technique can acquire progressively precise identification results.

Cloud and Cloud Shadow Segmentation for Remote Sensing Imagery Via Filtered Jaccard Loss Function and Parametric Augmentation

Cloud and cloud shadow segmentation are fundamental processes in optical remote sensing image analysis. Current methods for cloud/shadow identification in geospatial imagery are not as accurate as they should, especially in the presence of snow and haze. This paper presents a deep learning-based framework for the detection of cloud/shadow in Landsat 8 images. Our method benefits from a convolutional neural network, Cloud-Net+ (a modification of our previously proposed Cloud-Net \cite{myigarss}) that is trained with a novel loss function (Filtered Jaccard Loss). The proposed loss function is more sensitive to the absence of foreground objects in an image and penalizes/rewards the predicted mask more accurately than other common loss functions. In addition, a sunlight direction-aware data augmentation technique is developed for the task of cloud shadow detection to extend the generalization ability of the proposed model by expanding existing training sets. The combination of Cloud-Net+, Filtered Jaccard Loss function, and the proposed augmentation algorithm delivers superior results on four public cloud/shadow detection datasets. Our experiments on Pascal VOC dataset exemplifies the applicability and quality of our proposed network and loss function in other computer vision applications.

Dealing With Clouds and Seasonal Changes for Center Pivot Irrigation Systems Detection Using Instance Segmentation in Sentinel-2 Time Series

The automatic detection of Center Pivot Irrigation Systems (CPIS) is fundamental for establishing public policies, especially in countries with a growth perspective in this technology, like Brazil. Previous studies to detect CPIS using deep learning used single-date optical images, containing limitations due to seasonal changes and cloud cover. Therefore, this research aimed to detect CPIS using Sentinel-2 multitemporal images (containing six dates) and instance segmentation, considering seasonal variations and different proportions of cloudy images, generalizing the models to detect CPIS in diverse situations. We used a novel augmentation strategy, in which, for each iteration, six images were randomly selected from the time series (from a total of 11 dates) in random order. We evaluated the Mask-RCNN model with the ResNext-101 backbone considering the COCO metrics on six testing sets with different ratios of cloudless ( $ ) and cloudy images ( $>75\%$ ), from six cloudless images and zero cloudy images (6:0) up to one cloudless image and five cloudy images (1:5). We found that using six cloudless images provided the best metrics [80% average precision (AP), 93% AP with a 0.5 intersection over union threshold (AP50)]. However, results were similar (74% AP, 88% AP50) even in extreme scenarios with abundant cloud presence (1:5 ratio). Our method provides a more adaptive and automatic way to map CPIS from time series, significantly reducing interference such as cloud cover, atmospheric effects, shadow, missing data, and lack of contrast with the surrounding vegetation.

AI Training Dataset for Cloud Detection of KOMPSAT Images

Clouds that appear inevitably when acquiring optical satellite images hinder the interpretation of surface information, so removing them is a crucial procedure to increase the utilization of satellite images. Currently, for KOMPSAT (Korea Multi-purpose Satellite) images, only the cloud amount by visual measurement is proved for the entire scene and detailed cloud masks are not provided. Since cloud detection is a time-consuming task, we built a cloud dataset for KOMPSAT images so as to develop an algorithm that expedites the task with state-of-the-art artificial intelligent techniques. In the dataset, satellite images were selected from various regions considering that clouds have different characteristics depending on the region, and masks were classified into thin clouds, thick clouds, cloud shadows, and clear sky. The size of dataset is over 4,000 image/mask pairs by an image size of 1000x1000 and one of the largest among publicly available cloud datasets, as of this writing. The dataset is built by a government AI (artificial intelligent) training dataset building program and will be available through the website, aihub.or.kr.

Cloud/shadow segmentation based on global attention feature fusion residual network for remote sensing imagery

ABSTRACT Cloud and cloud shadow segmentation of satellite imageries is a prerequisite for many remote sensing applications. Due to the limited number of available spectral bands and the complexity of background information, the traditional detection methods have some problems such as false detection, missing detection and inaccurate boundary information in segmentation. To solve these problems, a global attention fusion residual network method is proposed to segment cloud and cloud shadow of satellite imageries. The proposed model adopts Residual Network (ResNet) as backbone to extract semantic information at different feature levels. In order to improve the ability of the network to deal with the boundary information, an improved atrous spatial pyramid pooling method is introduced to extract the multi-scale deep semantic information. Then, the deep semantic information is fused with the shallow spatial information through the Global Attention up-sample mechanism in different scales, which improves the network’s ability to utilize the global and local features. Finally, a boundary refinement module is utilized to predict the boundary of cloud and shadow, consequently the boundary information is refined. The experimental results on Sentinel-2 satellite and Land Remote-Sensing Satellite (Landsat) imageries show that the segmentation accuracy and speed of proposed method are superior to the existing methods, it is of great significance for realizing practical cloud and shadow segmentation.

Detection of shadows in high spatial resolution ocean satellite data using DINEOF

Cloud shadows present in high spatial resolution remote sensing datasets can affect the quality of the data if they are not properly detected and removed. When working with ocean data, cloud shadows are often difficult to differentiate from non-shadow values, since they show similar spectral characteristics than water pixels. A methodology to detect cloud shadows over the ocean is proposed. The present approach combines a series of tests applied directly to the physical variables derived from the satellite measured radiances, and it therefore does not depend on the wavebands measured by a specific satellite sensor. The tests include a departure from an EOF basis calculated using DINEOF, a threshold test, a proximity to cloud test and a ray tracing test. The weighing of the different tests can be adapted to each case or domain of study. The results are compared to manually detected shadows and to another shadow detection method. The approach works with cloud shadows of all sizes, and also with very small objects shadows, like the shadows projected by offshore windmills.

Segmentation of Satellite Imagery of Amedi Site Using Chan–Vese Model with Saliency Estimation

The images that have been taken from space satellites are described by satellite imagery. The presence of the earth's surface is detected by remote sensing. Normally the source of the satellite image is barely seen, because many points in the sky are obscured with cloud shadows. Therefore, one of the most important and ubiquitous tasks in image analysis is segmentation. Segmentation is the method of dividing a image into a collection of specific regions that vary in some essential qualitative or quantitative manner. In this paper we will focus on a method for segmenting images that was developed Three different methods to detect the location of the satellite images have been studied, implemented, and tested; these are based on Chan-Vese and saliency map segmentation, and multi-resolution segmentation to obtain a proper object segmentation. In this study, the combination of the proposed segmentation automatic detection and image enhancement technique has been performed to reduce the noise of the original image. In addition, the Bilateral filter, and histogram equalization are used in these proposed techniques. Experimental results demonstrate that the suggested method can precisely extract the objective of Amedi site from the satellite images with difficult backgrounds and overlapping regions.

Pengamatan dan Pemetaan Penyakit Busuk Pangkal Batang di Perkebunan Kelapa Sawit Menggunakan Unmanned Aerial Vehicle (UAV) dan Kamera Multispektral

Surveillance and Mapping of Basal Stem Rot Disease in Oil Palm Plantation Using Unmanned Aerial Vehicle (UAV) and Multispectral Camera
 Basal stem rot (BSR) disease caused by Ganoderma boninensis is still a major disease in oil palm plantations both in Indonesia and Malaysia. In some countries, remote sensing approach has been used for monitoring BSR in oil palm plantation. However, the utilization of satellite imagery in remote sensing especially in vegetation study on the tropical region was often limited by cloud cover. A drone or unmanned aerial vehicle (UAV) utilization is the best way to deal with cloud cover in the tropic region. Machine learning of random forest (RF) and satellite imagery used in the BSR study produced good accuracy. This research was aimed to identify and monitor the BSR infection on individual oil palm trees using an UAV and multispectral camera and RF classification. The results showed that the data acquired from UAV was affected by cloud shadows. The RF classification of healthy and infected oil palm trees by BSR disease and the spreading map of BSR infection was affected by cloud shadows. The highest accuracy of healthy and infected oil palm by BSR was 79.49%. Reflectance calibrator, digital to reflectance conversion, and model implications to build spreading map of BSR infection need to be conducted both on the clear area and the cloud shadow-covered area. Moreover, the UAV-based data should be considering the cloud view on the coverage area.

Harvesting solar energy onboard LTA systems: Modeling and design

In the present scenario, solar-powered Lighter-Than-Air-Platforms (LTAPs) find wide applications in the field of surveillance, air monitoring, line of sight communication, mapping of the earth’s topography. Accordingly, there are specified power requirements for the sensors integrated on such systems. A helium-gas filled LTA system may be an airship or tactical aerostat with variable size depending on type of sensor array and payload units deployed onboard. Interestingly, power generated through lightweight and flexible photovoltaic panels show degradation tendency in the presence of impact factors such as dynamic cloud footprints, wind disturbances, and inherent platform contour leading to Non-Uniform Illumination. Thus, destabilization effects of these degradation factors, towards onboard harvested solar energy. Novelty lies in design for environmental factors such as clouds and winds for solar power computations onboard LTA systems. PV efficiency considering impact of wind velocity disturbances on contoured LTA platform is observed as 89.44 %. An efficiency drop of 22.06 % is reported under the prevailing cloud shadows on the Lighter Than Air system. This paper presents a novel systematic methodology to accurately compute the deficit energy under the prevailing conditions for Lighter Than Air systems.

A Deep Learning Method for Near-Real-Time Cloud and Cloud Shadow Segmentation from Gaofen-1 Images.

In this study, an essential application of remote sensing using deep learning functionality is presented. Gaofen-1 satellite mission, developed by the China National Space Administration (CNSA) for the civilian high-definition Earth observation satellite program, provides near-real-time observations for geographical mapping, environment surveying, and climate change monitoring. Cloud and cloud shadow segmentation are a crucial element to enable automatic near-real-time processing of Gaofen-1 images, and therefore, their performances must be accurately validated. In this paper, a robust multiscale segmentation method based on deep learning is proposed to improve the efficiency and effectiveness of cloud and cloud shadow segmentation from Gaofen-1 images. The proposed method first implements feature map based on the spectral-spatial features from residual convolutional layers and the cloud/cloud shadow footprints extraction based on a novel loss function to generate the final footprints. The experimental results using Gaofen-1 images demonstrate the more reasonable accuracy and efficient computational cost achievement of the proposed method compared to the cloud and cloud shadow segmentation performance of two existing state-of-the-art methods.