Cloudy Images Research Articles

CloudCatcher: citizen science can be used to help to crowd source a satellite cloud validation dataset

AbstractAccurately identifying clouds in satellite images is an important stage in processing such images for scientific use. Cloudy image pixels are labelled using a pixel mask, to allow them to be filtered out and ignored. The accuracy at which ‘cloud masking’ can be performed needs to be determined and this is often done by expert manual classification. Given the public enthusiasm for weather, we tested whether this classification could be performed reliably by ‘citizen scientists’. We found that the public could identify cloud with a 94% accuracy over ocean. Our analysis pointed to future improvements of the CloudCatcher project.

Removing thick clouds in landsat 8 OLI images with cloudy time-series auxiliary data

Abstract Thick cloud occlusion severely limits the subsequent application of optical remote-sensing images. Existing mainstream cloud removal methods often use cloud-free homologous temporal images to provide auxiliary information. However, due to the limitation of cloud occlusion and satellite revisit cycle, the time interval between the cloud-free auxiliary image and the target cloudy image is often too large, and the possible land cover changes during the period increase the uncertainty of effective information on the auxiliary images. In this case, more accurate reconstructions may be obtained by using temporally closer and partially cloud-contaminated images. In this paper, a deep network, convLSTM, is developed for cloud removal (i.e., CR-convLSTM), which makes full use of the complementary effective information on partially cloud-contaminated time-series images. Experiments based on simulated clouds indicated that CR-convLSTM can obtain more accurate predictions than the classical cloud removal method MNSPI. CR-convLSTM is an effective cloud removal method with high computational efficiency while ensuring predicting accuracy.

Efficient On-Board Compression for Arbitrary-Shaped Cloud-Covered Remote Sensing Images via Adaptive Filling and Controllable Quantization

Due to the fact that invalid cloud-covered regions in remote sensing images consume a considerable quantity of coding bit rates under the limited satellite-to-ground transmission rate, existing image compression methods suffer from low compression efficiency and poor reconstruction quality, especially in cloud-free regions which are generally regarded as regions of interest (ROIs). Therefore, we propose an efficient on-board compression method for remote sensing images with arbitrary-shaped clouds by leveraging the characteristics of cloudy images. Firstly, we introduce two novel spatial preprocessing strategies, namely, the optimized adaptive filling (OAF) strategy and the controllable quantization (CQ) strategy. Specifically, the OAF strategy fills each cloudy region using the contextual information at its inner and outer edge to completely remove the information of cloudy regions and minimize their coding consumption, which is suitable for images with only thick clouds. The CQ strategy implicitly identifies thin and thick clouds and rationally quantifies the data in cloudy regions to alleviate information loss in thin cloud-covered regions, which can achieve the balance between coding efficiency and reconstructed image quality and is more suitable for images containing thin clouds. Secondly, we develop an efficient coding method for a binary cloud mask to effectively save the bit rate of the side information. Our method provides the flexibility for users to choose the desired preprocessing strategy as needed and can be embedded into existing compression framework such as JPEG2000. Experimental results on the GF-1 dataset show that our method effectively reduces the coding consumption of invalid cloud-covered regions and significantly improve the compression efficiency as well as the quality of decoded images.

CloudSeg: A multi-modal learning framework for robust land cover mapping under cloudy conditions

Cloud coverage poses a significant challenge to optical image interpretation, degrading ground information on Earth’s surface. Synthetic aperture radar (SAR), with its ability to penetrate clouds, provides supplementary information to optical data. However, existing optical-SAR fusion methods predominantly focus on cloud-free scenarios, neglecting the practical challenge of semantic segmentation under cloudy conditions. To tackle this issue, we propose CloudSeg, a novel framework tailored for land cover mapping in the presence of clouds. It addresses the challenges posed by cloud cover from two aspects: reducing semantic ambiguity in areas of the cloudy image that are obscured by clouds and enhancing effective information in the unobstructed portions. Specifically, CloudSeg employs a multi-task learning strategy to simultaneously handle low-level visual task and high-level semantic understanding task, mitigating the semantic ambiguity caused by cloud cover by acquiring discriminative features through an auxiliary cloud removal task. Additionally, CloudSeg incorporates a knowledge distillation strategy, which utilizes the knowledge learned by the teacher network under cloud-free conditions to guide the student network to overcome the interference of cloud-covered areas, enhancing the valuable information from the unobstructed parts of cloud-covered images. Extensive experiments conducted on two datasets, M3M-CR and WHU-OPT-SAR, demonstrate the effectiveness and superiority of the proposed CloudSeg method for land cover mapping under cloudy conditions. Specifically, CloudSeg outperforms the state-of-the-art competitors by 3.16% in terms of mIoU on M3M-CR and by 5.56% on WHU-OPT-SAR, highlighting its substantial advantages for analyzing regions frequently obscured by clouds. Codes are available at https://github.com/xufangchn/CloudSeg.

A Methodological Approach for Gap Filling of WFV Gaofen-1 Images from Spatial Autocorrelation and Enhanced Weighting

Clouds and cloud shadow cover cause missing data in some images captured by the Gaofen-1 Wide Field of View (GF-1 WFV) cameras, limiting the extraction and analysis of the image information and further applications. Therefore, this study proposes a methodology to fill GF-1 WFV images using the spatial autocorrelation and improved weighting (SAIW) method. Specifically, the search window size is adaptively determined using Getis-Ord Gi* as a metric. The spatial and spectral weights of the pixels are computed using the Chebyshev distance and spectral angle mapper to better filter the suitable similar pixels. Each missing pixel is predicted using linear regression with similar pixels on the reference image and the corresponding similar pixel located in the non-missing region of the cloudy image. Simulation experiments showed that the average correlation coefficient of the proposed method in this study is 0.966 in heterogeneous areas, 0.983 in homogeneous farmland, and 0.948 in complex urban areas. It suggests that SAIW can reduce the spread of errors in the gap-filling process to significantly improve the accuracy of the filling results and can produce satisfactory qualitative and quantitative fill results in a wide range of typical land cover types and has extensive application potential.

Blind single-image-based thin cloud removal using a cloud perception integrated fast Fourier convolutional network

Remote sensing images are frequently contaminated by clouds that often degrade the performance of subsequent applications. Cloud removal, therefore, is a standard step in remote sensing image preprocessing, and single-image-based thin cloud removal is a well-established area of research. Existing single-image-based thin cloud removal methods however, lack the capacity for simultaneous executions of efficient long-range modeling and physical attribute consideration. To extend this work and fill this gap, a novel blind single-image-based thin cloud removal method, called cloud perception integrated fast Fourier convolutional network (CP-FFCN), was designed and implemented. The CP-FFCN consists of two modules: the cloud perception module (CPM) and a fast Fourier convolution (FFC)-conducted reconstruction module (FFCN). The CPM uses a frequency spatial attention mechanism to realize long-range modeling of clouds, globally detect them in the cloudy image. It helps the CP-FFCN remove the clouds without external prior knowledge of the cloud distribution. The reconstruction module was designed with an FFC-conducted U-Net architecture to recover the clean images from cloudy scenarios, guided by the locations of clouds as detected by the CPM. In addition, the FFC blocks deployed in the encoder and decoder components in the U-Net architecture selectively learn the attributes of clouds and fogs from the frequency spectrograms to remove the clouds and reconstruct the underlying ground objects. The CP-FFCN selectively learns the frequency features for adequate cloud separation and at the same time efficiently models the long-range information for comprehensive scenario reconstruction with the help of these two modules. We adopted the Google Earth data and Landsat-8 imagery to train the CP-FFCN model and evaluate it on simulated and naturally occurring cloudy scenarios. The visual outcomes illustrate that the proposed CP-FFCN successfully removes thin and small-scale thick clouds with complex ground object scenarios, without external cloud masks and additional reference data. The quantitative analyses further demonstrate the higher effectiveness of the CP-FFCN when compared with several other state-of-the-art thin cloud removal methods, yielding a PSNR value over 39.24 and a SSIM value over 0.98 on the Landsat 8 images.

ARMing the Edge: Designing Edge Computing–Capable Machine Learning Algorithms to Target ARM Doppler Lidar Processing

Abstract There is a need for long-term observations of cloud and precipitation fall speeds in validating and improving rainfall forecasts from climate models. To this end, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) site at Lamont, Oklahoma, hosts five ARM Doppler lidars that can measure cloud and aerosol properties. In particular, the ARM Doppler lidars record Doppler spectra that contain information about the fall speeds of cloud and precipitation particles. However, due to bandwidth and storage constraints, the Doppler spectra are not routinely stored. This calls for the automation of cloud and rain detection in ARM Doppler lidar data so that the spectral data in clouds can be selectively saved and further analyzed. During the ARMing the Edge field experiment, a Waggle node capable of performing machine learning applications in situ was deployed at the ARM SGP site for this purpose. In this paper, we develop and test four algorithms for the Waggle node to automatically classify ARM Doppler lidar data. We demonstrate that supervised learning using a ResNet50-based classifier will classify 97.6% of the clear-air images and 94.7% of cloudy images correctly, outperforming traditional peak detection methods. We also show that a convolutional autoencoder paired with k-means clustering identifies 10 clusters in the ARM Doppler lidar data. Three clusters correspond to mostly clear conditions with scattered high clouds, and seven others correspond to cloudy conditions with varying cloud-base heights.

The Magnitude of Refractive Error and Its Associated Factors Among Patients Visiting Ophthalmology Clinics in Southern Ethiopia, 2022.

Refractive error is a vision-impairing condition due to light rays not being able to focus on the retina, resulting in the visual outcome of a cloudy image. It is one of the main causes of central vision impairment globally and in Africa, including Ethiopia. This study was conducted to assess the magnitude of refractive error and its associated factors among patients attending ophthalmic clinics. An institutional-based cross-sectional study design was utilized. A systematic random sampling technique was applied to select 356 participants. The data were gathered using an interview-structured questionnaire and check list. Then, the data were entered into Epi-data version 4.6 and transferred to SPSS version 25 for further clean up and analysis. Descriptive and analytical statistics were conducted. A binary logistic regression analysis was done, and variables with a p-value of less than 0.25 in univariate analysis were taken for bivariate analysis. Statistically significant was declared at a p-value of less than 0.05 with an adjusted odds ratio and 95% confidence interval. Among 356 participants, 96 (27.5%), with 95% CI (22.8, 32.1) had a refractive error, of which nearsighted is the most common type (15.8%). Regular use of electronic devices, near working distance (<33cm), less or lack of outdoor activities, history of diabetes mellitus and family history of refractive error were factors significantly associated with refractive error. The magnitude of refractive error was 27.5%, which is relatively elevated than the previous studies. Clients need to get screened regularly so that refractive defects can be detected and corrected early. Eye care professionals shall make a big concern for patients with a history of diabetes and other medical illnesses since they are related to ocular refractive defects.

SAR2NDVI: a pix2pix generative adversarial network for reconstructing field-level normalized difference vegetation index time series using Sentinel-1 synthetic aperture radar data

The normalized difference vegetation index (NDVI) is essential for monitoring urban green space, forest cover, and crop growth from sowing to harvesting. High-resolution images from Sentinel-2 and Landsat-8 satellites are available nowadays to get field-level phenological information at short intervals. However, cloud cover is one of the biggest hindrances in vegetation monitoring using NDVI, resulting in data gaps. To address this issue, we propose an NDVI gap-filling methodology to generate cloud-free NDVI time series from Sentinel-1 SAR data using the pix2pix generative adversarial network (GAN) model. Pix2pix GANs with generators based on U-Net and ResNet were designed, and the performance of the models was compared for both the VV and VH polarizations. The generalization capability of the models was studied using synthetic aperture radar (SAR) and NDVI image pairs with different vegetation, field sizes, and shapes. Experimental results show that the generated synthetic NDVI images can effectively substitute the cloudy images for gap-filling. Compared to ResNet, the U-Net generator has given the best results with PSNR = 29.447 dB, RMSE = 0.055, and Pearson correlation coefficient ρ = + 0.909 for VH polarization.

Transfer learning approach based on satellite image time series for the crop classification problem

This paper presents a transfer learning approach to the crop classification problem based on time series of images from the Sentinel-2 dataset labeled for two regions: Brittany (France) and Vojvodina (Serbia). During preprocessing, cloudy images are removed from the input data, the time series are interpolated over the time dimension, and additional remote sensing indices are calculated. We chose TransformerEncoder as the base model for knowledge transfer from source to target domain with French and Serbian data, respectively. Even more, the accuracy of the base model with the preprocessing step is improved by 2% when trained and evaluated on the French dataset. The transfer learning approach with fine-tuning of the pre-trained weights on the French dataset outperformed all other methods in terms of overall accuracy 0.94 and mean class recall 0.907 on the Serbian dataset. Our partially fine-tuned model improved recall of crop types that were poorly classified by the base model. In the case of sugar beet, class recall is improved by 85.71%.

A New Strategy of Satellite Autonomy with Machine Learning for Efficient Resource Utilization of a Standard Performance CubeSat

A mega constellation of Nano/microsatellites is the contemporary solution for global-level Earth observation demands. However, as most of the images taken by Earth-observing satellites are covered by clouds, storing and downlinking these images results in inefficient utilization of scarce onboard resources and bandwidth. In addition, the trend of making satellite task execution plans by ground operators demands the efforts of experts or simulators to predict the real-time situation of satellites and to decide which tasks should be executed next. Granting controlled autonomy to satellites to perform onboard tasks will boost mission effectiveness. We experimented with granting controlled autonomy for satellites in performing onboard image classification and task scheduling. We designed a convolutional neural network-based binary image classification model with more than 99% accuracy in classifying clear and cloudy images. The model is configured to perform inference in low-performance computers of ordinary Cubesats. Moreover, we designed an autonomous satellite task scheduling mechanism based on reinforcement learning. It performs better than a custom heuristic-based method in scheduling onboard tasks. As a result, the proposed classification and scheduling techniques with machine learning ensured efficient utilization of onboard memory, power, and bandwidth in the highly resource-constrained CubeSat platforms and mission accomplishment of Nano/microsatellite constellations.

Cross-supervised learning for cloud detection

ABSTRACT We present a new learning paradigm, that is, cross-supervised learning, and explore its use for cloud detection. The cross-supervised learning paradigm is characterized by both supervised training and mutually supervised training, and is performed by two base networks. In addition to the individual supervised training for labeled data, the two base networks perform the mutually supervised training using prediction results provided by each other for unlabeled data. Specifically, we develop In-extensive Nets for implementing the base networks. The In-extensive Nets consist of two Intensive Nets and are trained using the cross-supervised learning paradigm. The Intensive Net leverages information from the labeled cloudy images using a focal attention guidance module (FAGM) and a regression block. The cross-supervised learning paradigm empowers the In-extensive Nets to learn from both labeled and unlabeled cloudy images, substantially reducing the number of labeled cloudy images (that tend to cost expensive manual effort) required for training. Experimental results verify that In-extensive Nets perform well and have an obvious advantage in the situations where there are only a few labeled cloudy images available for training. The implementation code for the proposed paradigm is available at https://gitee.com/kang_wu/in-extensive-nets.

Automated mucilage extraction index (AMEI): a novel spectral water index for identifying marine mucilage formations from Sentinel-2 imagery

ABSTRACT Marine mucilage that threatens marine habitats is one of the natural disasters, mainly resulting from global warming and marine pollution. Monitoring sea surface mucilage formations and mapping their spatial distributions provide valuable information to the local authorities and decision-makers in developing prevention and rehabilitation strategies. This study proposes a new spectral index called Automated Mucilage Extraction Index (AMEI) that allows effective and accurate detection of surface mucilage aggregates using Sentinel-2 satellite imagery. The index uses four bands of Sentinel-2 Level-2A imagery (Bands 3, 4, 8, and 12) covering visible, near-infrared, and shortwave infrared regions. The index was formulated considering the image acquired on 19 May 2021, when mucilage formations were most intensively observed in the Sea of Marmara. The performance of the developed index was then evaluated using the images acquired on 14 and 24 May and 13 June 2021. The Jenks Natural Breaks (JNB) algorithm was applied to estimate the threshold values for separating mucilage formations from the water surface background and classify index maps into two classes: ‘mucilage’ and ‘others’. To statistically analyse the effectiveness of the indices in distinguishing water background and mucilage formations, the proposed index was compared with 21 widely used water indices by applying Bhattacharyya Distance, Jeffries-Matusita Distance, and M-Statistic measures. Results confirm the robustness of the proposed spectral index, offering superior separation performance (above 1.5 in terms of M-Statistic) compared to other water indices on both cloud-free images and images with cumulus clouds. Visual interpretations also verified that boundaries of the mucilage formations in the cloudless and thin cloudy images were accurately identified by the proposed index, and different types of mucilage (i.e. yellow and white) can be identified when an appropriate histogram thresholding is applied.

FSSCat: The Federated Satellite Systems 3Cat Mission: Demonstrating the capabilities of CubeSats to monitor essential climate variables of the water cycle [Instruments and Missions

The Federated Satellite Systems/ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Cat-5 (FSSCat) mission was the winner of the European Space Agency (ESA) Sentinel Small Satellite (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) Challenge and overall winner of the 2017 Copernicus Masters competition. It consisted of two six-unit CubeSats. The Earth observation payloads were 1) the Flexible Microwave Payload 2 (FMPL-2) onboard <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Cat-5/A, an L-band microwave radiometer and GNSS reflectometer (GNSS-R) implemented using a software-defined radio (SDR), and 2) the HyperScout-2 onboard <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Cat-5/B, a hyperspectral camera, with the first experiment using artificial intelligence to discard cloudy images. FSSCat was launched on 3 September 2020 and injected into a 535-km synchronous orbit. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Cat-5/A was operated for three months until the payload was probably damaged by a solar flare and coronal mass ejection. During this time, all scientific requirements were met, including the generation of coarse-resolution and downscaled soil moisture (SM) maps, sea ice extent (SIE) maps, concentration and thickness maps, and even wind speed (WS) and sea surface salinity (SSS) maps, which were not originally foreseen. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Cat-5/B was operated a few more months until the number of images acquired met the requirements. This article briefly describes the FSSCat mission and the FMPL-2 payload and summarizes the main scientific results.

Study Model for Information Reconstruction on Cloud Contaminated Area for Single Multispectral Remote Sensing Sentinel-2 Imagery using Generative Adversarial Network

Cloud and cloud shadow cause information loss in optical remote sensing analysis. South East Asia, especially Vietnam, Sentinel-2 imagery has short re-visit cycle and observations tend to be contaminated with cloud and cloud shadow. Traditional cloud removal methods require close date multi-temporal data to avoid seasonal land cover changes. In this study, a method of integrating Deep Convolutional Neural Networks (DCNN) and Generative Adversarial Network (GAN) was proposed. This machine learning model estimates the information loss over cloud contaminated areas on a single Sentinel-2 image. The results show that for images with cloud cover rate under 25%, our model can reconstruct cloudless images with PSNR (25 – 40 dB) and SSIM (0.86 – 0.93) compared to real clear images. On the other hand, with cloud cover rate up to 40%, the model performance will be affected heavily by the distribution of cloud and cloud shadow areas. By investigating DCNN and GAN, our method has proven to be an effective tool to remove cloudy images with low and medium rates, which enriches the clear optical remote sensing data sources for environment monitoring.&#x0D;

Spatio-temporal subpixel mapping with cloudy images

Spatio-temporal subpixel mapping (STSPM) has shown great potential for monitoring land surfaces, by generating land cover maps with both fine spatial and temporal resolutions. Selecting cloud-free fine spatial resolution images as ancillary data for STSPM can ensure that the temporal dependence term is measured for all subpixels, as in all current STSPM methods. However, such images are generally limited by cloud contamination, thereby resulting in great land cover changes between the available clear image and the desired fine spatial resolution land cover map. This research proposes a cloud-independent STSPM (C-STSPM) method to reconstruct the fine spatial resolution land cover maps by using cloudy images directly, which are assumed to have fewer land cover changes than temporally distant clear images. Cloud-independent spatio-temporal dependence was proposed in the presence of cloudy pixels. Experiments were performed under various cloud conditions involving 21 × 21 pairs of simulated cloudy images. The results demonstrate that by utilizing land cover information of clear pixels in cloudy images, more accurate prediction can be produced by C-STSPM compared to directly discarding those cloudy images, even if the number of cloud pixels increases to 95%. The advantage of C-STSPM is more evident when the clouds are distributed sparsely, which benefits from the increased number of clear pixels at the edge of the cloudy areas. Furthermore, a negative linear correlation was detected between the prediction accuracy and the ratio of overlapping cloudy pixels in the cloudy images. Moreover, the C-STSPM method helps to deal with abrupt changes occurred in the temporally distant cloud-free images by utilizing the temporally adjacent cloudy images with gradual land cover changes. Overall, the C-STSPM method provides a completely new solution to make fuller use of the widely existing cloudy images in multi-scale time-series images.

Semi-supervised thin cloud removal with mutually beneficial guides

We present a semi-supervised thin cloud removal framework referred to as mutually beneficial guides. It is featured by two fundamental operations, i.e., (a) beneficial model guide and (b) beneficial objective guide. They are operated in terms of the iterative training of two benefactor networks, i.e., (a) a model benefactor network and (b) an objective benefactor network. The beneficial objective guide benefits the model benefactor network by guiding its training with the cloud removal results of cloudy images given by the objective benefactor network at each iteration. This operation explores the cloudy images whose cloud-free images are unavailable for training, and thus augments the normally insufficient training data for cloud removal in a semi-supervised fashion. The beneficial model guide benefits the objective benefactor network by transferring a historically optimal model benefactor network to it at each iteration. This operation enables the objective benefactor network to provide reliable cloud removal results for training the model benefactor network. Therefore, the two benefactor networks mutually benefit each other from different aspects during the iterative training such that their capabilities of removing clouds are gradually enhanced. Specifically, we construct the benefactor networks in terms of a cross-scale content fusion structure where multi-scale features are fused to the pixel-to-pixel residual processing blocks. The structure is in favor of both coherent and high-resolution cloud removal results. Experiments on both Landsat-8 and Sentinel-2A images validate that the framework of mutually beneficial guides outperforms other methods in thin cloud removal. The implementation code of the framework of mutually beneficial guides is available at: https://gitee.com/xuzunxiao/mutually-beneficial-guides.

Google Earth Engine for Monitoring Marine Mucilage: Izmit Bay in Spring 2021

Global warming together with environmental pollution threatens marine habitats and causes an increasing number of environmental disasters. Periodic monitoring of coastal water quality is of critical importance for the effective management of water resources and the sustainability of marine ecosystems. The use of remote sensing technologies provides significant benefits for detecting, monitoring, and analyzing rapidly occurring and displaced natural phenomena, including mucilage events. In this study, five water indices estimated from cloud-free and partly cloudy Sentinel-2 images acquired from May to July 2021 were employed to effectively map mucilage aggregates on the sea surface in the Izmit Bay using the cloud-based Google Earth Engine (GEE) platform. Results showed that mucilage aggregates started with the coverage of about 6 km² sea surface on 14 May, reached the highest level on 24 May and diminished at the end of July. Among the applied indices, the Adjusted Floating Algae Index (AFAI) was superior for producing the mucilage maps even for the partly cloudy image, followed by Normalized Difference Turbidity Index (NDTI) and Mucilage Index (MI). To be more specific, indices using green channel were found to be inferior for extracting mucilage information from the satellite images.

Deteksi Batimetri Perairan Dangkal di Pulau Menjangan, Provinsi Bali Menggunakan Citra Landsat

Remote sensing-based research in Indonesia using satellite imagery frequently faces the challenge of cloud coverage due to the tropical country. One spatial data that can be extracted from satellite imagery is bathymetry. However, cloud-covered water bathymetric extraction still needs to be examined. This study aims to understand the ability of Landsat 7 ETM+ acquired on 29 July 2013, and Landsat 8, acquired on 24 July 2020, as the representative of non-cloudy image compared to Landsat 8, acquired on 9 August 2020, as the cloudy image. Stumpf algorithm was applied, including a statistical approach of linear regression analysis with in-situ data measurement from Single Beam Echo-Sounder (SBES) to derive the absolute bathymetric map with several classes of depth ranging from 0 – 2 m up to 10 m. To assess the accuracy, RMSE and confusion matrix was used. The result shows that Landsat 7 ETM+ yields the highest R2 with 0,52, while the lowest total RMSE (8,167 m) and highest overall accuracy of about 69% from the confusion matrix was achieved by the cloudy image of Landsat 8. Nevertheless, the highest absolute depth value yield by Landsat 8 non-cloudy image with 16,1 m. This research confirms that the highest R2 value does not always produce the best model, but it is still promised to be used. Furthermore, the quality of the imagery based on its percentage of cloud coverage is affecting the resulted model.

Design of an FPGA-Based High-Quality Real-Time Autonomous Dehazing System

Image dehazing, as a common solution to weather-related degradation, holds great promise for photography, computer vision, and remote sensing applications. Diverse approaches have been proposed throughout decades of development, and deep-learning-based methods are currently predominant. Despite excellent performance, such computationally intensive methods as these recent advances amount to overkill, because image dehazing is solely a preprocessing step. In this paper, we utilize an autonomous image dehazing algorithm to analyze a non-deep dehazing approach. After that, we present a corresponding FPGA design for high-quality real-time vision systems. We also conduct extensive experiments to verify the efficacy of the proposed design across different facets. Finally, we introduce a method for synthesizing cloudy images (loosely referred to as hazy images) to facilitate future aerial surveillance research.