optical satellite images
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2022 ◽  
Vol 15 ◽  
Author(s):  
Ying Yu ◽  
Jun Qian ◽  
Qinglong Wu

This article proposes a bottom-up visual saliency model that uses the wavelet transform to conduct multiscale analysis and computation in the frequency domain. First, we compute the multiscale magnitude spectra by performing a wavelet transform to decompose the magnitude spectrum of the discrete cosine coefficients of an input image. Next, we obtain multiple saliency maps of different spatial scales through an inverse transformation from the frequency domain to the spatial domain, which utilizes the discrete cosine magnitude spectra after multiscale wavelet decomposition. Then, we employ an evaluation function to automatically select the two best multiscale saliency maps. A final saliency map is generated via an adaptive integration of the two selected multiscale saliency maps. The proposed model is fast, efficient, and can simultaneously detect salient regions or objects of different sizes. It outperforms state-of-the-art bottom-up saliency approaches in the experiments of psychophysical consistency, eye fixation prediction, and saliency detection for natural images. In addition, the proposed model is applied to automatic ship detection in optical satellite images. Ship detection tests on satellite data of visual optical spectrum not only demonstrate our saliency model's effectiveness in detecting small and large salient targets but also verify its robustness against various sea background disturbances.


2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Van Anh TRAN ◽  
Thi Le LE ◽  
Nhu Hung NGUYEN ◽  
Thanh Nghi LE ◽  
Hong Hanh TRAN

Vietnam is an Asian country with hot and humid tropical climate throughout the year. Forestsaccount for more than 40% of the total land area and have a very rich and diverse vegetation.Monitoring the changes in the vegetation cover is obviously important yet challenging, considering suchlarge varying areas and climatic conditions. A traditional remote sensing technique to monitor thevegetation cover involves the use of optical satellite images. However, in presence of the cloud cover,the analyses done using optical satellite image are not reliable. In such a scenario, radar images are auseful alternative due to the ability of radar pulses in penetrating through the clouds, regardless of day ornight. In this study, we have used multi temporal C band satellite images to monitor vegetation coverchanges for an area in Dau Tieng and Ben Cat districts of Binh Duong province, Mekong Delta,Vietnam. With a collection of 46 images between March 2015 and February 2017, the changes of fiveland cover types including vegetation loss and replanting in 2017 were analyzed by selecting two cases,using 9 images in the dry season of 3 years 2015, 2016 and 2017 and using all of 46 images to conductRandom Forest classifier with 100, 200, 300 and 500 trees respectively. The result in which the modelwith nine images and 300 trees gave the best accuracy with an overall accuracy of 98.4% and a Kappaof 0.97. The results demonstrated that using VH polarization, Sentinel-1 gives quite a good accuracy forvegetation cover change. Therefore, Sentinel-1 can also be used to generate reliable land cover mapssuitable for different applications.


2021 ◽  
Author(s):  
Alexis Anne Denton ◽  
Mary-Louise Timmermans

Abstract. The sea-ice floe size distribution (FSD) characterizes the sea-ice response to atmosphere and ocean forcing and is important for understanding and modeling the evolving ice pack in a warming Arctic. FSDs are evaluated from 78 floe- segmented high-resolution (1-m) optical satellite images capturing a range of settings and sea-ice states during spring through fall from 1999 to 2014 in the Canada Basin. For any given image, the structure of the FSD is found to be sensitive to a classification threshold value (i.e., to specify an image pixel as being either water or ice) used in image segmentation, and an objective approach to minimize this sensitivity is presented. The FSDs are found to exhibit a single power-law regime between floe areas 50 m2 and 5 km2, characterized by exponents (slopes in log-log space) in the range −2.03 to −1.65. A distinct linear relationship between slopes and sea-ice concentrations is found, with steeper slopes (i.e., a larger proportion of smaller to larger floes) corresponding to lower sea-ice concentrations. Further, a seasonal variation in slopes is found for fixed sites in the Canada Basin that undergo a seasonal cycle in sea-ice concentration, while sites with extensive sea-ice cover year-round do not exhibit any seasonal change in FSD properties. Our results suggest that sea-ice concentration should be considered in any characterization of a time-varying FSD (for use in sea-ice models, for example).


Author(s):  
O. M. Bahatska ◽  
◽  
N. A. Pasichnyk ◽  
O. O. Opryshko ◽  
◽  
...  

IoT technologies in the Big Data concept can radically change approaches in agricultural practices, but it is necessary to work out methods of processing and interpreting information that can be effective in crop practice. Since the dimensions of plants are too small for satellite imagery, the development of technologies can be done on trees whose dimensions are sufficient for their identification in satellite imagery. The purpose of the work is to identify and assess the condition of plantations, in particular trees, with the determination of their positioning on satellite images of megacities. Digital photographs created by optical and infrared lenses of the Obolonskyi district of Kyiv were used for the research. It was found that in the optical range for objects under direct sunlight, plant identification is possible, while shaded areas are identified with significant errors. When using the index for IR shooting IRtree = C1 - C2 + 100 it was possible to identify individual ranges that belong to the crown of trees and grass in direct sunlight and to some extent in the shade, which could not be achieved with the index for optical range GBtree = G - B + 100. Monochrome infrared and optical images were not suitable for plant identification, because when objects were in the shadow of buildings, the ranges of intensity of the color components of plants were superimposed on the ranges of foreign objects. For infrared and optical satellite images, spectral indices have been proposed that take into account several color components to assess the condition of plantations. For tree crowns under direct sunlight, approximately the same results were obtained for the proposed indices. However, the indices proposed for infrared photography are more selective, as they were able to identify separately the crowns of trees and plants on lawns, both in direct sunlight and in the shade of buildings.


Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 999
Author(s):  
Joanna Pluto-Kossakowska

This paper presents a review of the conducted research in the field of multitemporal classification methods used for the automatic identification of crops and arable land using optical satellite images. The review and systematization of these methods in terms of the effectiveness of the obtained results and their accuracy allows for the planning towards further development in this area. The state of the art analysis concerns various methodological approaches, including selection of data in terms of spatial resolution, selection of algorithms, as well as external conditions related to arable land use, especially the structure of crops. The results achieved with use of various approaches and classifiers and subsequently reported in the literature vary depending on the crops and area of analysis and the sources of satellite data. Hence, their review and systematic conclusions are needed, especially in the context of the growing interest in automatic processes of identifying crops for statistical purposes or monitoring changes in arable land. The results of this study show no significant difference between the accuracy achieved from different machine learning algorithms, yet on average artificial neural network classifiers have results that are better by a few percent than others. For very fragmented regions, better results were achieved using Sentinel-2, SPOT-5 rather than Landsat images, but the level of accuracy can still be improved. For areas with large plots there is no difference in the level of accuracy achieved from any HR images.


Author(s):  
Giuseppina Andresini ◽  
Annalisa Appice ◽  
Daniele Iaia ◽  
Donato Malerba ◽  
Nicolò Taggio ◽  
...  

AbstractVarious applications in remote sensing demand automatic detection of changes in optical satellite images of the same scene acquired over time. This paper investigates how to leverage autoencoders in change vector analysis, in order to better delineate possible changes in a couple of co-registered, optical satellite images. Let us consider both a primary image and a secondary image acquired over time in the same scene. First an autoencoder artificial neural network is trained on the primary image. Then the reconstruction of both images is restored via the trained autoencoder so that the spectral angle distance can be computed pixelwise on the reconstructed data vectors. Finally, a threshold algorithm is used to automatically separate the foreground changed pixels from the unchanged background. The assessment of the proposed method is performed in three couples of benchmark hyperspectral images using different criteria, such as overall accuracy, missed alarms and false alarms. In addition, the method supplies promising results in the analysis of a couple of multispectral images of the burned area in the Majella National Park (Italy).


2021 ◽  
Vol 13 (17) ◽  
pp. 3535
Author(s):  
Zhongli Fan ◽  
Li Zhang ◽  
Yuxuan Liu ◽  
Qingdong Wang ◽  
Sisi Zlatanova

Accurate geopositioning of optical satellite imagery is a fundamental step for many photogrammetric applications. Considering the imaging principle and data processing manner, SAR satellites can achieve high geopositioning accuracy. Therefore, SAR data can be a reliable source for providing control information in the orientation of optical satellite images. This paper proposes a practical solution for an accurate orientation of optical satellite images using SAR reference images to take advantage of the merits of SAR data. Firstly, we propose an accurate and robust multimodal image matching method to match the SAR and optical satellite images. This approach includes the development of a new structural-based multimodal applicable feature descriptor that employs angle-weighted oriented gradients (AWOGs) and the utilization of a three-dimensional phase correlation similarity measure. Secondly, we put forward a general optical satellite imagery orientation framework based on multiple SAR reference images, which uses the matches of the SAR and optical satellite images as virtual control points. A large number of experiments not only demonstrate the superiority of the proposed matching method compared to the state-of-the-art methods but also prove the effectiveness of the proposed orientation framework. In particular, the matching performance is improved by about 17% compared with the latest multimodal image matching method, namely, CFOG, and the geopositioning accuracy of optical satellite images is improved, from more than 200 to around 8 m.


2021 ◽  
Vol 13 (17) ◽  
pp. 3453
Author(s):  
Michael Dieter Martin ◽  
Iestyn Barr ◽  
Benjamin Edwards ◽  
Matteo Spagnolo ◽  
Sanaz Vajedian ◽  
...  

Globally, about 250 Holocene volcanoes are either glacier-clad or have glaciers in close proximity. Interactions between volcanoes and glaciers are therefore common, and some of the most deadly (e.g., Nevado del Ruiz, 1985) and most costly (e.g., Eyjafjallajökull, 2010) eruptions of recent years were associated with glaciovolcanism. An improved understanding of volcano-glacier interactions is therefore of both global scientific and societal importance. This study investigates the potential of using optical satellite images to detect volcanic impacts on glaciers, with a view to utilise detected changes in glacier surface morphology to improve glacier-clad volcano monitoring and eruption forecasting. Roughly 1400 optical satellite images are investigated from key, well-documented eruptions around the globe during the satellite remote sensing era (i.e., 1972 to present). The most common observable volcanic impact on glacier morphology (for both thick and thin ice-masses) is the formation of ice cauldrons and openings, often associated with concentric crevassing. Other observable volcanic impacts include ice bulging and fracturing due to subglacial dome growth; localized crevassing adjacent to supraglacial lava flows; widespread glacier crevassing, presumably, due to meltwater-triggered glacier acceleration and advance. The main limitation of using optical satellite images to investigate changes in glacier morphology is the availability of cloud- and eruption-plume-free scenes of sufficient spatial- and temporal resolution. Therefore, for optimal monitoring and eruption prediction at glacier-clad volcanoes, optical satellite images are best used in combination with other sources, including SAR satellite data, aerial images, ground-based observations and satellite-derived products (e.g., DEMs).


Author(s):  
Klaudia Kryniecka ◽  
Artur Magnuszewski ◽  
Artur Radecki-Pawlik

The amount of sediments transported by a river is very difficult to estimate, however this parameter has an important influence on channel geometry. It is possible to estimate the bedload transport rate per unit width of a river channel by measuring bedform profiles’ migration distance (Δl) in time (Δt) and depth of bedload in motion (hb). Another method is instrumental measurements using bedload traps and empirical formulas. Sentinel-1 images at mid latitudes have a temporal resolution of 2–3 days and spatial resolution of 25 m, which allows them to be used on large rivers. The research area in this paper is the Lower Vistula River from km 814 to km 820, where seven alternate sandbars were selected. The coast lines of the sandbars were delineated on Sentinel-1 images taken during two low flow periods 2018.08.04–09.26 and 2019.07.01–08.31 with similar discharges at low flow phase on the hydrograph. From water stage observations at the Chełmno and Grudziądz gauge stations, water elevations were assigned to every coast line of the alternate sandbars. The centers, volumes and longitudinal profile of the alternate sandbars were calculated. Average daily movement of the sandbars in the period 2018.08.04–2019.07.01 was calculated as 0.97 m·day˗1. Similar alternate sandbar movement velocities were obtained from a study of Sentinel-2 optical satellite images and hydro-acoustic measurements on the Lower Vistula River. Having the height of the alternate sandbars and velocity of movement, it was possible to calculate the rate of the bedload transport as qb = 5 kg·s˗1·m˗1. This value is similar to results of empirical formulas accepted for use on large lowland rivers in Poland: Goncarov – 5 kg·s˗1·m˗1, Samov – 3 kg·s˗1·m˗1; Meyer-Peter and Müller – 9 kg·s˗1·m˗1; Skibiński (1976) – 15 kg·s˗1·m˗1. The novelty of this research is showing the use of Sentinel-1 images for the study of river channel dynamics and calculation of bedload transport.


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