GeoEye-1 Satellite Research Articles

Disparity Refinement for Stereo Matching of High-Resolution Remote Sensing Images Based on GIS Data

With the emergence of the Smart City concept, the rapid advancement of urban three-dimensional (3D) reconstruction becomes imperative. While current developments in the field of 3D reconstruction have enabled the generation of 3D products such as Digital Surface Models (DSM), challenges persist in accurately reconstructing shadows, handling occlusions, and addressing low-texture areas in very-high-resolution remote sensing images. These challenges often lead to difficulties in calculating satisfactory disparity maps using existing stereo matching methods, thereby reducing the accuracy of 3D reconstruction. This issue is particularly pronounced in urban scenes, which contain numerous super high-rise and densely distributed buildings, resulting in large disparity values and occluded regions in stereo image pairs, and further leading to a large number of mismatched points in the obtained disparity map. In response to these challenges, this paper proposes a method to refine the disparity in urban scenes based on open-source GIS data. First, we register the GIS data with the epipolar-rectified images since there always exists unignorable geolocation errors between them. Specifically, buildings with different heights present different offsets in GIS data registering; thus, we perform multi-modal matching for each building and merge them into the final building mask. Subsequently, a two-layer optimization process is applied to the initial disparity map based on the building mask, encompassing both global and local optimization. Finally, we perform a post-correction on the building facades to obtain the final refined disparity map that can be employed for high-precision 3D reconstruction. Experimental results on SuperView-1, GaoFen-7, and GeoEye satellite images show that the proposed method has the ability to correct the occluded and mismatched areas in the initial disparity map generated by both hand-crafted and deep-learning stereo matching methods. The DSM generated by the refined disparity reduces the average height error from 2.2 m to 1.6 m, which demonstrates superior performance compared with other disparity refinement methods. Furthermore, the proposed method is able to improve the integrity of the target structure and present steeper building facades and complete roofs, which are conducive to subsequent 3D model generation.

The Effectiveness of Pan-Sharpening Algorithms on Different Land Cover Types in GeoEye-1 Satellite Images.

In recent years, the demand for very high geometric resolution satellite images has increased significantly. The pan-sharpening techniques, which are part of the data fusion techniques, enable the increase in the geometric resolution of multispectral images using panchromatic imagery of the same scene. However, it is not trivial to choose a suitable pan-sharpening algorithm: there are several, but none of these is universally recognized as the best for any type of sensor, in addition to the fact that they can provide different results with regard to the investigated scene. This article focuses on the latter aspect: analyzing pan-sharpening algorithms in relation to different land covers. A dataset of GeoEye-1 images is selected from which four study areas (frames) are extracted: one natural, one rural, one urban and one semi-urban. The type of study area is determined considering the quantity of vegetation included in it based on the normalized difference vegetation index (NDVI). Nine pan-sharpening methods are applied to each frame and the resulting pan-sharpened images are compared by means of spectral and spatial quality indicators. Multicriteria analysis permits to define the best performing method related to each specific area as well as the most suitable one, considering the co-presence of different land covers in the analyzed scene. Brovey transformation fast supplies the best results among the methods analyzed in this study.

A spatiotemporal transferable image fusion technique for GeoEye-1 satellite imagery

This study proposed a novel technique to solve the problem of color distortion in the fusion of the GeoEye-1 satellite's panchromatic (PAN) and multispectral (MS) images. This technique suggested reducing the difference in radiometry between the PAN and MS images by using modification coefficients for the MS bands in the definition of the intensity (I) equation, which guarantees using only the overlapped wavelengths with the PAN band. These modification coefficients achieve spatiotemporal transferability for the proposed fusion technique. As the reflectance of vegetation is high in the NIR band and low in the RGB bands, this technique suggested using an additional coefficient for the NIR band in the definition of the I equation, which varies based on the ratio of the agricultural features within the image, to indicate the correct impact of vegetation. This vegetation coefficient provides stability for the proposed fusion technique across all land cover classes. This study used three datasets of GeoEye-1 satellite PAN and MS images in Tanta City, Egypt, with different land cover classes (agricultural, urban, and mixed areas), to evaluate the performance of this technique against five different standard image fusion techniques. In addition, it was validated using six additional datasets from different locations and acquired at different times to test its spatiotemporal transferability. The proposed fusion technique demonstrated spatiotemporal transferability as well as great efficiency in producing fused images of superior spatial and spectral quality for all types of land cover.

Land Use Classification of High-Resolution Multispectral Satellite Images With Fine-Grained Multiscale Networks and Superpixel Postprocessing

Land use recognition from multispectral satellite images is fundamentally critical for geological applications, but the results are not satisfied. The scale dimension of current multiscale learning is too coarse to account for rich scales in multispectral images, and pixel-wise classification tends to produce “salt-and-pepper” labels due to possible misclassification in heterogeneous regions. In this paper, these issues are addressed by proposing a new pixel-wise classification model with finer scales for convolutional neural networks. The model is designed to extract multiscale contextual information using multiscale networks at a fine-grained level, addressing the issue of insufficient multiscale learning for classification. Furthermore, a small-scale segmentation-combination method is introduced as a post-processing solution to smooth fragmented classification results. The proposed method is tested on GF-1, GF-2, DEIMOS-2, GeoEye-1, and Sentinel-2 satellite images, and compared with six neural-network-based algorithms. The results demonstrate the effectiveness of the proposed model in finding objects of large scale difference, improving classification accuracy, and reducing classified fragments. The discussion also illustrates that convolutional neural networks and pixel-wise inference are more practical than Transformer and patch-wise recognition.

Pansharpening scheme using spatial detail injection–based convolutional neural networks

Pansharpening produces a high spatial-spectral resolution pansharpened image by combining multispectral (MS) and panchromatic (PAN) images. In the traditional multi-resolution analysis (MRA) method, detailed PAN images are extracted by transformation methods that are injected into MS images. This gives spatial and spectral distortions in the pansharpened image. These distortions can be reduced in the pansharpened image by the correct matching of the PAN detail image component. This correct matching is possible by the convolutional neural network (CNN)–based models. This paper obtains the detailed image component using the CNN models. This CNN model extracts the PAN detail image that is suitable for the MRA-based pansharpening scheme which significantly reduces the spatial and spectral distortions. It is demonstrated by qualitative and quantitative analysis applied on GeoEye-1 and IKONOS satellite images and shows the effectiveness of the proposed scheme.

Методика визначення планового положення інфраструктури зруйнованого Звенигородського замку

Aim. The aim of the work is to determine the planned position on the modern space image of the destroyed castle in Zvenigorod, Lviv region and its infrastructure facilities according to archival maps of 18–19 centuries. Method. One of the possible technological schemes for research is offered. The analysis of the realized projects of similar character and in particular, concerning Zvenigorodsky castle is executed. The first stage of the work was to perform geometric correction of input graphics. Geometric correction included the transformation of input graphic data, namely: a space image of Zvenigorod, obtained from the GeoEye-1 satellite in 2017, an archival map vonMiga of the 18th century, a Polish archival map of the 18th century, an archeological research plan under the LODA project. When transforming archival maps, the maximum UPC was 6 pixels or 4.3 dead on the ground. The maximum root mean square error of transformation of modern graphic materials was 1 pixel or 0.5 m off-road. All materials were transformed into MapInfo in the internal orthogonal projection of the Plan-Scheme/Meters program. From archival maps of the 18th century. and from the topographic plan of archeological researches the infrastructure objects of Zvenigorod Castle and the adjacent territory were vectorized. A comparative analysis of vectorized objects based on different time materials was performed. It has been determined that the data of archeological researches differ somewhat from the objects marked on the archival maps, possibly due to the generalization of the maps. But in any case, these objects can not be discarded in further radiometric studies of the area. Results. As a result of the research, a thematic model of a 1:2000 scale photographic plan was created, based on a transformed space image obtained from the GeoEye-1 satellite in 2017. data from the modern topographic plan. Practical value. The obtained results can be used by the museum of the historical and cultural reserve “Ancient Zvenigorod”. These data will be used in the future to determine the vertical displacements of the castle on the basis of radar space images and georadar surveys.

Land-Use/Landscape Pattern Changes and Related Environmental Driving Forces in a Dong Ethnic Minority Village in Southwestern China

This paper outlines land-use/landscape pattern changes and relationships between land-use change trajectories and environmental variables in the Dong ethnic-minority village of Gaoyou in southwestern China. In the study, landscape metrics were applied to evaluate changes in land use and landscape patterns using GeoEye-1 satellite imagery for 2009/2020 and drone imagery for 2019, and redundancy analysis (RDA) was applied to clarify the relationship between land-use change trajectories and environmental variables. The 10–30% change in land use observed across each time sequence indicated rapid development in the area, resulting in increased fragmentation and reduced aggregation. The findings showed efficient usage of land resources in Gaoyou. Accessibility to land tended to govern the characteristics of land-use change, with natural variables influencing the type of development. The results also indicated that farmers responded quickly to government subsidies promoting tea and camellia plantations, unplanned road construction was causing fragmentation, and official land-use map content differed from the authors’ observations. Accordingly, the government should make integrated long-term plans for the development of ethnic-minority villages and engage in remote-sensor monitoring of local land-use change.

Ambientes de ocorrência do porífero Oncosclera jewelli no Parque Estadual Do Tainhas, Planalto Meridional do RS: uma análise geomorfométrica

O Parque Estadual do Tainhas abrange ambientes de ocorrência do porífero Oncosclera jewelli. Nosso objetivo é indicar as áreas de potencial ocorrência da espécie a partir de fatores relacionados à assinatura geomorfométrica dos pontos de ocorrência ao longo do Rio Tainhas no Parque e sua zona de amortecimento. Foram realizados cruzamentos e análises de dados a partir da construção e manipulação de um banco de dados geográficos, nos SIGs SPRING-5.4.3 e QGIS-2.18, contendo: a) MDEs oriundos das bases Topodata, Embrapa e Alos, obtidos por sensores remotos orbitais para toda a área de estudo, e MDE obtido por imagens aéreas geradas por Drone e as variáveis geomorfométricas produzidas: declividade, aspecto e índice de posição topográfica; b) mapa geológico do Rio Grande do Sul; c) mapa de uso e cobertura do solo, com base em imagens do satélite GeoEye. Os resultados demonstraram que a espécie ocorre exclusivamente sobre a unidade litológica da Formação Serra Geral. O relevo é marcado por terrenos de baixa declividade e orientação sul e leste e em relevo aplainado. Foi gerado o Mapa de Potencial Ocorrência da espécie, demonstrando que aproximadamente 4,5% do comprimento total dos trechos de cursos d’água na área de estudo reúnem as condicionantes geomorfométricas identificadas como de potencial ocorrência da espécie.

Pansharpening via Triplet Attention Network With Information Interaction

Pansharpening aims to obtain high spatial resolution multispectral (MS) images by fusing the spatial and spectral information in low spatial resolution (LR) MS and panchromatic (PAN) images. Recently, deep neural network (DNN) based pansharpening methods have been advanced extensively. Although most DNN-based methods show good performance, it is difficult for them to preserve the spatial details in the fused image. In this article, we propose a new pansharpening method based on a triplet attention network with information interaction to efficiently enhance the spatial and spectral information in the fused image. First, different attention mechanisms are designed to model the spatial and spectral feature properties in LR MS and PAN images. Then, the complementarity among different feature maps is enhanced by information interaction, which promotes the compatibility of features from subnetworks. Finally, we utilize a graph attention module to capture the similarity within feature maps. According to the graph, the informative feature maps are selected to provide more details for the reconstruction of the fused image. Extensive experiments on QuickBird and GeoEye-1 satellite datasets show that the proposed method can produce competitive fused images when compared with some state-of-the-art methods.

Spatial and Spectral Extraction Network With Adaptive Feature Fusion for Pansharpening

Pansharpening methods based on deep neural networks (DNNs) have been attracting great attention due to their powerful representation capabilities. In this article, to combine the feature maps from different subnetworks efficiently, we propose a novel pansharpening method based on a spatial and spectral extraction network (SSE-Net). Different from the other methods based on DNNs that directly concatenate the features from different subnetworks, we design adaptive feature fusion modules (AFFMs) to merge these features according to their information content. First, the spatial and spectral features are extracted by the subnetworks from low spatial resolution multispectral (LR MS) and panchromatic (PAN) images. Then, by fusing the features at different levels, the desired high spatial resolution MS (HR MS) images are generated by the fusion network consisting of AFFMs. In the fusion network, the features from different subnetworks are integrated adaptively, and the redundancy among them is reduced. Moreover, the spectral ratio loss and the gradient loss are defined to ensure the effective learning of spatial and spectral features. The spectral ratio loss captures the nonlinear relationships among the bands in the MS image to reduce the spectral distortions in the fusion result. Extensive experiments were conducted on QuickBird and GeoEye-1 satellite datasets. Visual and numerical results demonstrate that the proposed method produces better fusion results compared with literature techniques. The source code is available at <uri>https://github.com/RSMagneto/SSE-Net</uri>.

Developing a new IHS-based fusion methodology for GeoEye-1 satellite imagery with the ELSHORA fusion technique

In this research, a new image fusion technique was developed to merge the spatial details in the panchromatic (PAN) image and the spectral details in the multispectral (MS) image to produce fused images with the highest spatial and spectral qualities. This fusion technique is superior to the other existing fusion techniques in that it can produce sharp images with no color distortion for all types of land cover. This is because of using weighting parameters for all MS bands in the calculation of the I component according to the intersecting areas between the MS bands and the PAN band, and the I component will be an average of the four modified MS bands, which minimizes the gray level differences between PAN and I images. Moreover, using the variable vegetation coefficient β for the NIR band makes this technique produce the highest spectral and spatial qualities for each land cover class. To evaluate the performance of the ELSHORA fusion technique, it was compared to the traditional intensity-hue-saturation (IHS) fusion technique, the fast IHS (FIHS) fusion technique, and four other standard image fusion techniques; principal component analysis, hyper-spherical color space, multiplicative resolution merge and Brovey transform. These fusion techniques were applied to three sets of GeoEye-1 PAN and MS images covering different land covers in Tanta city, Egypt. The fused images are compared statistically and visually to the original PAN and MS images. The results showed that the ELSHORA fusion technique was chosen as the best method for all datasets. This is because it has produced the highest spectral quality in which no color distortion can be detected and has provided significant spatial quality, visually and analytically. To test the spatial and temporal transferability of ELSHORA fusion technique, it was compared to the original FIHS fusion technique to fuse three different datasets covering different classes of land cover (urban, agriculture, and mixed) located in different places and acquired at different times. The results indicate that ELSHORA fusion technique is spectrally and spatially in the lead for all datasets. This study proved the efficiency of ELSHORA fusion technique to produce images of high spectral and spatial resolution at different times and places.

Optimal segmentation parameters prediction using a orthogonal decomposition approach for geographical object based classification of urban areas

Good image segmentation produces objects that are internally homogeneous and distinct from their neighbors. Several approaches are used to assess the quality of segmentation parameters including visual observation of multiple segmentation results and techniques, which calculate measures of intra segment homogeneity and inter segment heterogeneity. Some of these techniques have been reported without considering under and over segmentation; occurrences and others exhibit some mathematical formulation instabilities when dealing with very heterogeneous areas. Moreover, the majority of segmentation assessment techniques focus on the evaluation of the scale parameter and give little attention to the influence of other parameters, such as compactness on the performance of the scale parameter. To the best of our knowledge, no existing approach can predict the optimal compactness thresholds that would enhance the performance of the scale parameters, or predict which scale parameter will perform best under certain compactness constraints. This paper proposes three spatial autocorrelation models, which identify the optimal parameters for single scale and multilevel segmentations of urban environments. Tested using a GeoEye satellite image of the City of Cape Town (South Africa), results reflect that the best combination of parameters to optimally segment the area and those that would poorly perform were successfully identified.

Mapping spatial distribution of invasive alien species through satellite remote sensing in Karachi, Pakistan: an urban ecological perspective

The exponential population growth of Karachi city has taken a very heavy toll on the indigenous environment of this South Asian megacity. In this context, the unlikely roadsides plantation of nonnative species by the city decision-makers was a short-term solution to cultivate green belts, ultimately, to improve city environment. However, this has backfired and become a very serious ecological crisis as the species become invasive alien species and destroyed the native ecological environment of the city significantly. This study provided an accurate spatial extent of invasive alien species and evaluated the intensity of the problem using remote sensing technology. Firstly, the importance value index was developed to highlight the intensity of invasive alien species in native plant communities. Afterwards, the hot spots were sampled to develop training areas for Object Based Image Analysis to map the extent of invasive alien species in the city using high-resolution GeoEye satellite imagery. The overall accuracy of mapping was 93.17% with Kappa coefficient value of 0.904. In addition, to explore hyperspectral remote sensing analysis, two techniques—Derivative Vegetation Index and Spectral Angle Mapper—were employed on EO-I hyperspectral imagery to map invasive alien species using spectral signatures. The analysis revealed 99.94% correlation between the two mapping techniques. Finally, to identify the impact of invasive alien species on the microclimate of the city, a spatiotemporal analysis (1992–2016) at selected sites was performed on Landsat thermal images. The study ruled out the concept of invasive alien species influence on the microclimate of the city as both vegetation growth and ambient air temperature change were not interdependent.

Методика картографування об’єктів історико-культурної спадщини засобами ГІС-технологій з використанням архівних картографічних та аероматеріалів

Aim. The purpose of the work is to process archival cartographic materials and remote sensing data for the interpretation of objects of historical and cultural heritage (OHCH) of Cherkasy, including those that have not been preserved. Method. One of the possible technological schemes for research is offered. According to her, the first step was to analyze the input data of the study, among which were: a map of Cherkasy in 1895 at a scale of 1:42000; German aerial image of 1944; a fragment of a space image of Cherkasy obtained from the GeoEye-1 satellite in 2018. Geometric correction of the input materials was performed in the Mercator projection and the WGS84 coordinate system, in which the transformed image was obtained. The next step was to vectorize the objects of historical and cultural heritage of Cherkasy, according to the list obtained on the city’s website. There are two types of objects: point and polygonal. When vectorizing polygonal objects, the historical boundaries were specified with the help of archival maps and aerial images. Special symbols have been developed for each of the types of historical and cultural heritage sites, according to the proposed classification. In addition, an attributive database of these objects was created, which had the following structure: number of the passport of object, the name of the object, the address of the OHCH, the number of the decision to take under protection, information about the OHCH. Also, the obtained vector data was exported to the exchange format with the extension kmz and an online version of the thematic map was created on the basis of the free GISFile resource. Results. As a result of the conducted researches, the thematic GIS of the objects of historical and cultural heritage of Cherkasy was created, which are plotted on the space image of high spatial resolution, obtained in 2018. An on-line version of the GIS of Cherkasy historical and cultural heritage sites has been created on the basis of the free GISFile cartographic service, with the possibility of analyzing the location of these objects and building optimal tourist routes. Scientific novelty. Possible algorithms for creating offline and on-line versions of thematic GIS are proposed. Practical value. The obtained results of mapping the objects of historical and cultural heritage of Cherkasy can be used by the structures of protection of objects of historical and cultural heritage of Cherkasy at the Ministry of Culture.

Combined Geometric Positioning and Performance Analysis of Multi-Resolution Optical Imageries from Satellite and Aerial Platforms Based on Weighted RFM Bundle Adjustment

Combined geometric positioning using images with different resolutions and imaging sensors is being increasingly widely utilized in practical engineering applications. In this work, we attempt to perform the combined geometric positioning and performance analysis of multi-resolution optical images from satellite and aerial platforms based on weighted rational function model (RFM) bundle adjustment without using ground control points (GCPs). Firstly, we introduced an integrated image matching method combining least squares and phase correlation. Next, for bundle adjustment, a combined model of the geometric positioning based on weighted RFM bundle adjustment was derived, and a method for weight determination was given to make the weights of all image points variable. Finally, we conducted experiments using a case study in Shanghai with ZiYuan-3 (ZY-3) satellite imagery, GeoEye-1 satellite imagery, and Digital Mapping Camera (DMC) aerial imagery to validate the effectiveness of the proposed weighted method, and to investigate the positioning accuracy by using different combination scenarios of multi-resolution heterogeneous images. The experimental results indicate that the proposed weighted method is effective, and the positioning accuracy of different combination scenarios can give a good reference for the combined geometric positioning of multi-stereo heterogeneous images in future practical engineering applications.

Использование тепловых снимков со спутника Landsat 7 при картографировании тундровых ландшафтов на примере участка газопровода "Бованенково – Байдарацкая Губа"

The article considers the possibility of using thermal images from Landsat 7 satellites to characterize and clarify the degree of surface drainage when mapping tundra landscapes by the case of the test section of the Bovanenkovo – Baydaratskaya Bay gas pipeline (the Yamal Peninsula). The images constructed using the LST (surface temperature) and NDWI (surface moisture) parameters based on the 1999 and 2000 survey data from the Landsat 7 satellite have a fairly high repeatability and good correspondence with the gradations of the degree of surface drainage according to the landscape map (scale 1:10 000), built using an image from the GeoEye-1 satellite. After a number of transformations, thermal images from the Landsat 7 satellite with a spatial resolution of 60 m can be used as an additional material for characterizing and clarifying the degree of drainage of the surface of tundra landscapes, including for large-scale thematic mapping.

Fusion of Panchromatic and Multispectral Images Using Multiscale Convolution Sparse Decomposition

In this article, we proposed a novel image fusion method based on multiscale convolution sparse decomposition (MCSD). A unified framework based on MCSD is first utilized to decompose panchromatic (PAN) image and the spatial component of upsampled low spatial resolution multispectral (LR MS) images, which can produce the corresponding low frequencies and feature maps. By combining convolution sparse decomposition with multiscale analysis, MCSD can efficiently approximate the spatial and spectral information in images. Next, a binary map generated from gradient information is utilized to integrate the low frequencies of LR MS and PAN images. For feature maps, the fusion gain for each pixel is calculated according to the similarity between the local patches from them. Finally, the fused image is reconstructed by the sum of fused low frequency and feature maps. Some experiments are conducted on QuickBird and GeoEye-1 satellite datasets. Compared with other methods, the proposed method performs better in visual and numerical evaluations.

Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images

Mapping plant species at the regional scale to provide information for ecologists and forest managers is a challenge for the remote sensing community. Here, we use a deep learning algorithm called U-net and very high-resolution multispectral images (0.5 m) from GeoEye satellite to identify, segment and map canopy palms over ∼3000 km 2 of Amazonian forest. The map was used to analyse the spatial distribution of canopy palm trees and its relation to human disturbance and edaphic conditions. The overall accuracy of the map was 95.5% and the F1-score was 0.7. Canopy palm trees covered 6.4% of the forest canopy and were distributed in more than two million patches that can represent one or more individuals. The density of canopy palms is affected by human disturbance. The post-disturbance density in secondary forests seems to be related to the type of disturbance, being higher in abandoned pasture areas and lower in forests that have been cut once and abandoned. Additionally, analysis of palm trees’ distribution shows that their abundance is controlled naturally by local soil water content, avoiding both flooded and waterlogged areas near rivers and dry areas on the top of the hills. They show two preferential habitats, in the low elevation above the large rivers, and in the slope directly below the hill tops. Overall, their distribution over the region indicates a relatively pristine landscape, albeit within a forest that is critically endangered because of its location between two deforestation fronts and because of illegal cutting. New tree species distribution data, such as the map of all adult canopy palms produced in this work, are urgently needed to support Amazon species inventory and to understand their distribution and diversity.

Analysis of Very High Spatial Resolution Images for Automatic Shoreline Extraction and Satellite-Derived Bathymetry Mapping

The amount of Earth observation images available to the public has been the main source of information, helping governments and decision-makers tackling the current world’s most pressing global challenge. However, a number of highly skilled and qualified personnel are still needed to fill the gap and help turn these data into intelligence. In addition, the accuracy of this intelligence relies on the quality of these images in times of temporal, spatial, and spectral resolution. For the purpose of contributing to the global effort aiming at monitoring natural and anthropic processes affecting coastal areas, we proposed a framework for image processing to extract the shoreline and the shallow water depth on GeoEye-1 satellite image and orthomosaic image acquired by an unmanned aerial vehicle (UAV) on the coast of San Vito Lo Capo, with image preprocessing steps involving orthorectification, atmospheric correction, pan sharpening, and binary imaging for water and non-water pixels analysis. Binary imaging analysis step was followed by automatic instantaneous shoreline extraction on a digital image and satellite-derived bathymetry (SDB) mapping on GeoEye-1 water pixels. The extraction of instantaneous shoreline was conducted automatically in ENVI software using a raster to vector (R2V) algorithm, whereas the SDB was computed in ArcGIS software using a log-band ratio method applied on the satellite image and available field data for calibration and vertical referencing. The results obtained from these very high spatial resolution images demonstrated the ability of remote sensing techniques in providing information where techniques using traditional methods present some limitations, especially due to their inability to map hard-to-reach areas and very dynamic near shoreline waters. We noticed that for the period of 5 years, the shoreline of San Vito Lo Capo sand beach migrated about 15 m inland, indicating the high dynamism of this coastal area. The bathymetric information obtained on the GeoEye-1 satellite image provided water depth until 10 m deep with R2 = 0.753. In this paper, we presented cost-effective and practical methods for automatic shoreline extraction and bathymetric mapping of shallow water, which can be adopted for the management and the monitoring of coastal areas.

Penerapan Algoritma LZ77 dan Algoritma Difference Coding Untuk Memperkecil Ukuran pada Citra Satelite Geoeye

Geoeye satellite imagery is one of the satellite images that has a high resolution and is gaining in popularity in recent years. As one of the global satellite imagery, it is clear that this satellite image offers something that can amaze anyone of its users, this satellite image offers an earth surface image with a level of detail that can be considered extraordinary, and the level of accuracy after compared to satellite imagery that has high resolution the other