Earth Observation Satellite Images Research Articles

Precise Relative Geometric Correction for Multi-Sensor Satellite Images

Abstract. Rapid progress in satellite technology has led to a noticeable surge in availability of Earth observation satellite images, which are being collected daily from satellites deployed worldwide. However, even with advanced satellite positioning equipment, there are still diverse level of remaining positional errors. This is a hindrance to satellite image utilization. Therefore, positional errors between satellite images must be corrected before utilization. Relative geometric correction of satellite images is a technique that adjusts geometric displacements based on their relative positional relationships in image or object space. In this study, we propose homography-based bundle adjustment for relative geometric correction of multi-sensor satellite images. Our method aims to estimate optimal ground plane on which images are projected and quickly generate result image. For experiments, orthorectified satellite images with various resolutions and georeferencing information were employed as input data. The experiment results showed that the average error, which was initially 4.96 pixels before relative geometric correction, was decreased to 1.73 pixels after applying the proposed method.

Detection of Moving Objects in Earth Observation Satellite Images: Verification

In multi-spectral images made by Earth observation satellites that use push-broom scanning, such as those operated by Planet Labs Corp., moving objects can be identified by the appearance of the object at different locations in each spectral band. The apparent velocity can be measured if the relative acquisition time between images in different spectral bands is known to millisecond accuracy. The images in the Planet Labs archive are mosaics of individual exposures acquired at different times. Thus, there is not a unique acquisition time for each spectral band. In an earlier paper, we proposed a method to determine the relative acquisition times from the information in the images themselves. High-altitude balloons provide excellent targets to test our proposed method because of their high apparent velocity due to the orbital velocity of the satellite and geometric parallax in images aligned to the level of the ground. We use images of the Chinese balloon that crossed the US in February 2024 as well as images of an identical balloon over Colombia to test our method. Our proposed method appears to be successful and allows the measurement of the apparent velocity of moving objects from the information available in the archive.

Cognitive Radio Strategy Combined with MODCOD Technique to Mitigate Interference on Low-Orbit Satellite Downlinks.

The concept of cognitive radio (CR) as a tool to optimize the obstacle of spectral coexistence has promoted the development of shared satellite-terrestrial wireless networks. Nevertheless, in some applications like Earth Exploration Satellite Services, which demand high spectral efficiency (bps/Hz) for downlink transmissions, spectral coexistence amidst interferences from cellular Base Stations is still challenging. Our research aims to mitigate these interferences on low-orbit satellite downlinks carrying imaging data received from a ground station. In order to fulfill this, we present cognitive radio approaches to enhance spectrum exploitation and introduce the adaptive modulation and coding (MODCOD) technique to increase RF power and spectral efficiencies. Therefore, we propose a combined methodology using CR and adaptive MODCOD (ACM) techniques. Afterwards, we applied the solution by monitoring the signal to interference plus noise ratio and the MODCOD strategy. Finally, we provide a real in situ case study at the Cuiabá ground station located in Brazil's central area, which receives images from an Earth observation satellite (EOS). In addition to demonstrating the strategy effectiveness in this scenario, we conducted a bench test emulating the interfering wireless communication system. In this sense, we demonstrated the proposed approach, successfully mitigating the harmful effects on the received EOS images.

Detection of surface temperature anomaly of the Sea of Marmara

Monitoring sea surface temperature (SST) over a long-term and detecting the anomalies highly contribute to understanding the prevailing water quality of the sea. Earth observation satellite images are the key data sources that offer the long-term SST detection in a cost and time effective way. Since the Sea of Marmara in Türkiye is surrounded by the highly populated provinces, the water quality of the sea has gained importance for scientific and public communities over the years. This article emphasizes on the significance of detecting SST trend and corresponding anomalies of the Sea of Marmara over the past 32 years. To address the SST variations of the Sea of Marmara in time, a comprehensive set of both field and satellite data regarding SSTs were obtained within the context of this study. The SST trend and its anomalies between the years 1990 and 2021 were detected by applying Seasonal-Trend decomposition procedure based on LOESS (STL) method to NOAA OISST V2 data. On the other hand, spatial SST distribution was detected with Landsat-8, Sentinel-3 and NOAA OISST V2 satellite data. SST results were verified with the in-situ data within the scope of accuracy assessment. The results showed that SST time-series data performed an increasing trend and had anomalies mostly during the spring months in the recent years.

Hyperspectral Remote Sensing for Mapping Foliar Pigment Concentration in Mudumalai Tiger Reserve, India

The present study focuses on mapping and identification of the pigment concentration and dominant invasive species in Mudumalai Tiger Reserve (MTR), Tamil Nadu, India using optical (Sentinel 2A) and hyperspectral (Hyperion) earth observation satellite images. The Hyperion satellite data-based study exhibited dominance of dense vegetation (40.64%) and moderate vegetation (27.81%), in contrast to low vegetation (15.71%) in MTR. While vegetation indices based analysis demonstrated more accurate results of forest cover through Modified Red Edge Normalised Difference Vegetation Index (MRENDVI) compared to other vegetation indices. The dominance of moderate Normalised Difference Vegetation Index (NDVI) values (0.55–0.6) were observed in MTR with moderate carotenoid concentration (<0.3) and low anthocyanin concentration (<0.2). The study highlights the prominence of Lantana camara, Parthenium hysterophorus , Prosopis juliflora, and Chromolaena odorata in the Mudumalai Tiger Reserve. The correlation of species and foliar pigment concentration provides a better understanding of the species distribution in tropical deciduous forests in southern India. The study highlights better results in Sentinel 2A images compared to Hyperion images in LULC classification due to higher spatial resolution, while Hyperion images provide more improved classification and diversity for species characterisation due to high spectral agility. The study necessitates concurrent monitoring of invasion in forest-rich regions through the adoption of potential forest conservation and management plans.

Classification of Remote Sensing Images With Parameterized Quantum Gates

This letter studies how to program and assess a parameterized quantum circuit (PQC) for classifying Earth observation (EO) satellite images. In this exploratory study, we assess a PQC for classifying a two-label EO image dataset and compare it with a classic deep learning classifier. We use the PQC with an input space of only 17 quantum bits (qubits) due to the current limitations of quantum technology. As a real-world image for EO, we selected the Eurosat dataset obtained from multispectral Sentinel-2 images as a training dataset and a Sentinel-2 image of Berlin, Germany, as a test image. However, the high dimensionality of our images is incompatible with the PQC input domain of 17 qubits. Hence, we had to reduce the dimensionality of the input images for this two-label case to a vector with 16 elements; the 17th qubit remains reserved for storing label information. We employed a very deep convolutional network with an autoencoder as a technique for the dimensionality reduction of the input image, and we mapped the dimensionally reduced image onto 16 qubits by means of parameter thresholding. Then, we used a PQC to classify the two-label content of the dimensionally reduced Eurosat image dataset. A PQC classifies the Eurosat images with high accuracy as a classic deep learning method (and with even better accuracy in some instances). From our experiment, we derived and enhanced deeper insight into programming future gate-based quantum computers for many practical problems in EO.

Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite Images

Scientists in the marine domain process satellite images in order to extract information that can be used for monitoring, understanding, and forecasting of marine phenomena, such as turbidity, algal blooms and oil spills. The growing need for effective retrieval of related information has motivated the adoption of semantically aware strategies on satellite images with different spatio-temporal and spectral characteristics. A big issue of these approaches is the lack of coincidence between the information that can be extracted from the visual data and the interpretation that the same data have for a user in a given situation. In this work, we bridge this semantic gap by connecting the quantitative elements of the Earth Observation satellite images with the qualitative information, modelling this knowledge in a marine phenomena ontology and developing a question answering mechanism based on natural language that enables the retrieval of the most appropriate data for each user’s needs. The main objective of the presented methodology is to realize the content-based search of Earth Observation images related to the marine application domain on an application-specific basis that can answer queries such as “Find oil spills that occurred this year in the Adriatic Sea”.

Quality control and class noise reduction of satellite image time series

The extensive amount of Earth observation satellite images available brings opportunities and challenges for land mapping in global and regional scales. These large datasets have motivated the use of satellite image time series analysis coupled with machine learning techniques to produce land use and cover class maps. To be successful, these methods need good quality training samples, which are the most important factor for determining the accuracy of the results. For this reason, training samples need methods for quality control of class noise. In this paper, we propose a method to assess and improve the quality of satellite image time series training data. The method uses self-organizing maps (SOM) to produce clusters of time series and Bayesian inference to assess intra-cluster and inter-cluster similarity. Consistent samples of a class will be part of a neighborhood of clusters in the SOM map. Noisy samples will appear as outliers in the SOM. Using Bayesian inference in the SOM neighborhoods, we can infer which samples are noisy. To illustrate the methods, we present a case study in a large training set of land use and cover classes in the Cerrado biome, Brazil. The results prove that the method is efficient to reduce class noise and to assess the spatio-temporal variation of satellite image time series training samples.

Change detection based flood mapping using multi-temporal Earth Observation satellite images: 2018 flood event of Kerala, India

ABSTRACT The future projections of climate change envisage a global increase in extreme precipitation events and subsequent flooding. The reliable and rapid flood maps are the critical parameters in preparing the disaster management plans. This study demonstrated an effective flood mapping framework using freely available multi-temporal Earth Observation (EO) images, including C-band Sentinel-1A & 1B Synthetic Aperture Radar (SAR) images and optical WorldView-3 images, for analyzing the 2018 flood event of Kerala, India. Two Change Detection (CD) techniques, i.e. Ratio Index (RI) and Normalized Change Index (NCI) combined with semi-automatic thresholding are implemented on temporal descending pass SAR images for flood identification. For ascending pass SAR images, the statistical-based thresholding method is implemented. The results indicate that combined use of ascending and descending pass SAR images contributed to a better understanding of flood conditions. It is also inferred that the use of a pre-flood image can enhance flood area estimation and helps in minimizing the overestimation errors. The results also found that NCI outperforms RI for Kerala flood event. Flood area extracted from these techniques is plotted against the Indian Meteorological Department (IMD) rainfall datasets, which showed a similar trend. Field photographs and optical images are used for validation purposes.

A Robust Ground‐to‐Image Transformation Algorithm and Its Applications in the Geometric Processing of Linear Pushbroom Images

AbstractLinear array cameras are widely used in the fields of aerial and space remote sensing. The ground‐to‐image transformation plays a significant role in the geometric processing procedures of linear pushbroom images, which is time‐consuming due to multiple iterative calculations. Moreover, the detectors of the linear array may exhibit a curved shape due to distortions, resulting in a more complex ground‐to‐image transformation. This paper presents a novel ground‐to‐image transformation algorithm for linear pushbroom images based on a rigorous sensor model. We regard the distance between the back‐projected image point and the curved shape linear array as the “generalized distance.” The idea of generalized distance prediction was developed to efficiently determine the best scan line corresponding to the ground point. The plane analytic geometry is employed to solve the practical problems of the ground‐to‐image transformation, including the iterative direction, convergence condition, and subpixel interpolation. The applications of the proposed method in orthorectification and image matching were investigated. The proposed method was comprehensively tested using airborne images, Earth observation satellite images, and planetary images. The experimental results demonstrate that the proposed method (1) delivers high accuracy (better than1 × 10−4pixels) and efficiency, (2) is better than the cutting‐edge method based on the geometric constraints of central perspective plane for the linear pushbroom images with distortions, and (3) provides promising applications for generating orthophotos and image matching. The proposed method is easy to implement and efficient, which can greatly enhance the geometric processing abilities for various types of linear pushbroom images.

Multitemporal Cloud Masking in the Google Earth Engine

The exploitation of Earth observation satellite images acquired by optical instruments requires an automatic and accurate cloud detection. Multitemporal approaches to cloud detection are usually more powerful than their single scene counterparts since the presence of clouds varies greatly from one acquisition to another whereas surface can be assumed stationary in a broad sense. However, two practical limitations usually hamper their operational use: the access to the complete satellite image archive and the required computational power. This work presents a cloud detection and removal methodology implemented in the Google Earth Engine (GEE) cloud computing platform in order to meet these requirements. The proposed methodology is tested for the Landsat-8 mission over a large collection of manually labeled cloud masks from the Biome dataset. The quantitative results show state-of-the-art performance compared with mono-temporal standard approaches, such as FMask and ACCA algorithms, yielding improvements between 4–5% in classification accuracy and 3–10% in commission errors. The algorithm implementation within the Google Earth Engine and the generated cloud masks for all test images are released for interested readers.

High‐frequency attitude jitter correction for the Gaofen‐9 satellite

AbstractWith the gradually improving resolution of earth observation satellite images, attitude determination accuracy has become a key factor influencing image geometric positioning accuracy. However, high‐frequency attitude jitters caused by the instability of the satellite platform lead to image distortions which cannot be corrected by the low‐frequency star sensor and gyro attitude measurement. Therefore, this paper proposes a high‐frequency attitude jitter correction method based on micro‐vibration accelerometer measurement. Three images from the GaoFen‐9 (GF‐9) satellite over Lijiang in China were selected to verify the method. The correction effect was assessed in terms of road straightness and image relative registration error. The results show that the proposed method can effectively eliminate image distortions caused by high‐frequency jitters, providing a feasible way to process images from small agile satellite platforms.

Modeling Dengue vector population using remotely sensed data and machine learning

Mosquitoes are vectors of many human diseases. In particular, Aedes ægypti (Linnaeus) is the main vector for Chikungunya, Dengue, and Zika viruses in Latin America and it represents a global threat. Public health policies that aim at combating this vector require dependable and timely information, which is usually expensive to obtain with field campaigns. For this reason, several efforts have been done to use remote sensing due to its reduced cost. The present work includes the temporal modeling of the oviposition activity (measured weekly on 50 ovitraps in a north Argentinean city) of Aedes ægypti (Linnaeus), based on time series of data extracted from operational earth observation satellite images. We use are NDVI, NDWI, LST night, LST day and TRMM-GPM rain from 2012 to 2016 as predictive variables. In contrast to previous works which use linear models, we employ Machine Learning techniques using completely accessible open source toolkits. These models have the advantages of being non-parametric and capable of describing nonlinear relationships between variables. Specifically, in addition to two linear approaches, we assess a support vector machine, an artificial neural networks, a K-nearest neighbors and a decision tree regressor. Considerations are made on parameter tuning and the validation and training approach. The results are compared to linear models used in previous works with similar data sets for generating temporal predictive models. These new tools perform better than linear approaches, in particular nearest neighbor regression (KNNR) performs the best. These results provide better alternatives to be implemented operatively on the Argentine geospatial risk system that is running since 2012.

Calibration of urban canopies albedo and 3D shortwave radiative budget using remote-sensing data and the DART model

ABSTRACTThe derivation of the radiative budget of urban environments has gained a high interest in the recent years as an essential part of the global energy budget of big cities. Urban 3D (three-dimensional) heterogeneity hampers its assessment with in-situ measurements, which stresses the interest of Earth Observation (EO) satellites. Improvements in remote-sensing technology and 3D urban databases open the way to new deterministic approaches. This paper presents a new method that derives maps of urban shortwave exitance, albedo and radiative budget * from EO satellite images (e.g. Sentinel 2, Landsat-8), using the discrete anisotropic radiative transfer model. In a preliminary step, it derives maps of urban material optical properties from an EO satellite image, at the spatial resolution of this EO image. The method is applied to the city of Basel, Switzerland, in the frame of the European Community H2020 URBANFLUXES (www.urbanfluxes.eu) project which aims at improving our knowledge on anthropogenic heat fluxes in European cities. Results are very encouraging. Indeed, urban * derived from 234 EO satellite images, ranging from 200 to 800 W/m2 through the year, is very close to in-situ measured *: ≈15 W/m2 temporal root mean square error (i.e. 2.7% mean relative difference) relative to measurements.

Cloud and Shadow Detection using Sequential Characteristics on Multi-Spectral Satellite Images

For land and sea surface monitoring applications that rely on optical Earth Observation satellite images, it is required that cloud and cloud shadow areas in the images are detected and removed. As a consequence, the frequency of obtaining new images can be increased and short-term changes can be studied more effectively. In this paper, an algorithm is designed that is able to automatically detect clouds and shadows in medium-resolution optical multi-spectral images. The developed system is a frame-based image processing technique utilizing multiple spectral bands for feeding a cloud and shadow detector, where possible cloud contamination is recursively removed from the input images. The cloud detector utilizes Brightness Temperature Differences in the spectral regions of Far IR (FIR) and Thermal IR (TIR). After careful considerations, the reflective band FIR was adopted for usage in this Difference Image. The shadow detector uses Background Subtraction, which iteratively constructs its Reference Image automatically. This iterative nature is exploited to utilize time-sequential characteristics among the input images. After experiments, 94.6% of the clouds are detected, with a precision of 86.5%, as determined using per-pixel ground-truth data. For shadows, these statistics are 77.1% and 75.8%, respectively and may be further improved in future work. Selected mid-resolution Landsat images have been used for the validation.

Pursuit Reinforcement Competitive Learning: PRCL based Online Clustering with Tracking Algorithm and its Application to Image Retrieval

Pursuit Reinforcement guided Competitive Learning: PRCL based on relatively fast online clustering that allows grouping the data in concern into several clusters when the number of data and distribution of data are varied of reinforcement guided competitive learning is proposed. One of applications of the proposed method is image portion retrievals from the relatively large scale of the images such as Earth observation satellite images. It is found that the proposed method shows relatively fast on the retrievals in comparison to the other existing conventional online clustering such as Vector Quatization: VQ. Moreover, the proposed method shows much faster than the others for the multi-stage retrievals of image portion as well as scale estimation. A new approach for online clustering based on reinforcement learning, called Pursuit Reinforcement Guided Competitive Learning. PRCL which is derived from pursuit method in reinforcement learning that maintain both action- value and action preferences, with the preferences continually pursuing the action that is greedy according to the current action-value estimates together with learning automata is proposed. PRCL can be used as online clustering method. One of the applications is, then introduced for evacuation simulation. The following section describes the proposed PRCL with learning automata together with the existing conventional online clustering methods of RGCL, SRGCL and VQ. Then preliminary experiments are described followed by its application of image retrievals. After all, conclusion is described with some discussions.

Band-to-band registration model for near-equatorial Earth observation satellite images with the use of automatic control point extraction

The near-equatorial orbit (NEqO) image bands have large differences in distortion, scaling, and skewing. Therefore band-to-band registration of a near-equatorial orbit image is challenging. This study thus proposes a technique to overcome the band-to-band registration of NEqO image bands. The proposed method involves the conversion of image bands into greyscale, followed by image compression and automatic control point (CP) generation through scale-invariant feature transform. CPs are used to perform registration with both first- and second-order polynomials and spline transformation to correct the misregistration between NEqO image bands. The images used in this study are from the Malaysian RazakSAT satellite. Results show accurate registration performance. Moreover, the root-mean-square errors of the first- and second-order polynomial transformations are 4 and 3 m, respectively. These values are lower than those obtained with the use of spline transformation. Verification is performed by comparing the results of both automatic and manual transformations to the use of polynomial transformation. The results confirm the effectiveness of the proposed algorithm.

Rotational Pixel Swapping Method for Detection of Circular Features in Binary Images

We propose a new automatic method called the rotational pixel swapping (RPSW) method to detect circular features in binary images of remote sensing images. The method is based on a multiplication operation between the original image and the rotated images. We show that the RPSW selectively enhances rotational symmetric patterns and weakens nonrotational symmetric patterns, including noise components, without any noise reduction processes. The method can detect not only simple circles but also more complex circular features such as incomplete ring structures or several concentric rings. Furthermore, we demonstrate that the RPSW provides the stable detection of circular features such as terrestrial impact structures, which are irregular imperfect circular shapes, in binary images based on Earth-observation satellite images. The RPSW would provide a potential method of future surveys or statistical studies using huge data sets of multiband or hyperspectral images obtained by Earth-observation satellites.

Building EOS capability for Malaysia – the options

Earth observation satellite (EOS) is currently a major tool to monitor earth dynamics and increase human understanding of earth surface process. Since the early 80s, Malaysia has been using EOS images for various applications, such as weather forecasting, land use mapping, agriculture, environment monitoring and others. Until now, all EOS images were obtained from foreign satellite systems. Realising on the strategic need of having its own capability, Malaysia embarked into EOS development programs in the early 90s. Starting with TiungSAT-1, a micro-satellite carrying small camera, then followed by RazakSAT, a small satellite carrying 2.5 m panchromatic (PAN) medium-aperture-camera, the current satellite program development, the RazakSAT-2, designed to carry a 1.0 m high resolution PAN and 4.0m multi-spectral camera, would become a strategic initiative of the government in developing and accelerating the nation's capability in the area of satellite technology and its application. Would this effort continue until all needs of the remote sensing community being fulfilled by its own EOS? This paper will analyze the intention of the Malaysian government through its National Space Policy and other related policy documents, and proposes some policy options on this. Key factors to be considered are specific data need of the EOS community, data availability and the more subjective political motivations such as national pride.

Glacier-Related Hazards and Their Assessment in the Tajik Pamir: A Short Review.

This paper reviews contemporary glacial features, characteristics of documented glacial hazards, and the status of hazard assessment studies in the Pamirs of Tajikistan (hereafter the Tajik Pamir). The review found detachment of a hanging glacier, glacier-surging, and outburst discharge of a glacial lake to be major potential causes of glacial hazards in the Tajik Pamir, especially in the western area (west from approx. 73° E). Guerrilla glacial lakes, which are characterised as glacial lakes on the ice-core moraine that “appear repeatedly,” “enlarge rapidly (within less than one year),” “disappear within less than two years,” “are superficially closed,” and “are of relatively small size (approx. < 10 6 m 2 )” should hereafter be paid special attentions. Preparation of appropriate hazard-mitigation activities should require early detection of not only the large crack in the hanging-glacier terminus, surge behaviour, and the glacial lake and guerrilla glacial lake, but also regular monitoring of these hazard factors. One of the most suitable monitoring techniques is to use earth observation satellite images with a relatively short repetitive cycle (ideally once every few days) and a high spatial resolution (ideally several meters). These criteria may be satisfied in the near future by using images captured by many sets of microsatel lites, such as the 50-kg class microsatellite SPRITE-SAT (RISING).