scholarly journals Remote Sensing Methods for the Retrieval of Inventory and Bioproductivity Parameters of Forests Using High Resolution Satellite Images

2019 ◽  
Vol 75 ◽  
pp. 01003 ◽  
Author(s):  
Egor Dmitriev ◽  
Vladimir Kozoderov ◽  
Sergey Donskoy ◽  
Petr Melnik ◽  
Anton Sokolov
Keyword(s):  
Satellite Images ◽  
High Spatial Resolution ◽  
Texture Features ◽  
Forest Species ◽  
Effective Learning ◽  
Estimated Parameters ◽  
High Resolution Satellite Images ◽  
Automated Processing ◽  
Tver Region ◽  
Remote Sensing Methods

A method for automated processing high spatial resolution satellite images is proposed to retrieve inventory and bioproductivity parameters of forest stands. The method includes effective learning classifiers, inverse modeling, and regression modeling of the estimated parameters. Spectral and texture features are used to classify forest species. The results of test experiments for the selected area of Savvatievskoe forestry (Russia, Tver region) are presented. Accuracy estimates obtained using ground-based measurements demonstrate the effectiveness of using the proposed techniques to automate the process of updating information for the State Forest Inventory program of Russia.

scholarly journals Agricultural Greenhouses Detection in High-Resolution Satellite Images Based on Convolutional Neural Networks: Comparison of Faster R-CNN, YOLO v3 and SSD

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4938
Author(s):  
Min Li ◽  
Zhijie Zhang ◽  
Liping Lei ◽  
Xiaofan Wang ◽  
Xudong Guo
Keyword(s):  
High Resolution ◽  
Visual Inspection ◽  
Satellite Images ◽  
High Spatial Resolution ◽  
Fine Tuning ◽  
Single Shot ◽  
Modern Agriculture ◽  
High Resolution Satellite Images ◽  
Independent Test ◽  
Test Sets

Agricultural greenhouses (AGs) are an important facility for the development of modern agriculture. Accurately and effectively detecting AGs is a necessity for the strategic planning of modern agriculture. With the advent of deep learning algorithms, various convolutional neural network (CNN)-based models have been proposed for object detection with high spatial resolution images. In this paper, we conducted a comparative assessment of the three well-established CNN-based models, which are Faster R-CNN, You Look Only Once-v3 (YOLO v3), and Single Shot Multi-Box Detector (SSD) for detecting AGs. The transfer learning and fine-tuning approaches were implemented to train models. Accuracy and efficiency evaluation results show that YOLO v3 achieved the best performance according to the average precision (mAP), frames per second (FPS) metrics and visual inspection. The SSD demonstrated an advantage in detection speed with an FPS twice higher than Faster R-CNN, although their mAP is close on the test set. The trained models were also applied to two independent test sets, which proved that these models have a certain transability and the higher resolution images are significant for accuracy improvement. Our study suggests YOLO v3 with superiorities in both accuracy and computational efficiency can be applied to detect AGs using high-resolution satellite images operationally.

scholarly journals Adaptive Enhancement Algorithm of High-Resolution Satellite Image Based on Feature Fusion

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Ruizhe Wang ◽  
Wang Xiao
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Feature Fusion ◽  
Satellite Image ◽  
Texture Features ◽  
Noise Removal ◽  
Enhancement Effect ◽  
Adaptive Enhancement ◽  
High Resolution Satellite Images ◽  
Enhancement Algorithm

Since the traditional adaptive enhancement algorithm of high-resolution satellite images has the problems of poor enhancement effect and long enhancement time, an adaptive enhancement algorithm of high-resolution satellite images based on feature fusion is proposed. The noise removal and quality enhancement areas of high-resolution satellite images are determined by collecting a priori information. On this basis, the histogram is used to equalize the high-resolution satellite images, and the local texture features of the images are extracted in combination with the local variance theory. According to the extracted features, the illumination components are estimated by Gaussian low-pass filtering. The illumination components are fused to complete the adaptive enhancement of high-resolution satellite images. Simulation results show that the proposed algorithm has a better adaptive enhancement effect, higher image definition, and shorter enhancement time.

scholarly journals An Approach for the Pan Sharpening of Very High Resolution Satellite Images Using a CIELab Color Based Component Substitution Algorithm

2019 ◽  
Vol 9 (23) ◽  
pp. 5234 ◽  
Author(s):  
Rahimzadeganasl ◽  
Alganci ◽  
Goksel
Keyword(s):  
Spatial Resolution ◽  
Satellite Images ◽  
Spatial Information ◽  
High Spatial Resolution ◽  
Critical Role ◽  
Study Region ◽  
High Resolution Satellite Images ◽  
Very High ◽  
Component Substitution ◽  
Cielab Color

Recent very high spatial resolution (VHR) remote sensing satellites provide high spatial resolution panchromatic (Pan) images in addition to multispectral (MS) images. The pan sharpening process has a critical role in image processing tasks and geospatial information extraction from satellite images. In this research, CIELab color based component substitution Pan sharpening algorithm was proposed for Pan sharpening of the Pleiades VHR images. The proposed method was compared with the state-of-the-art Pan sharpening methods, such as IHS, EHLERS, NNDiffuse and GIHS. The selected study region included ten test sites, each of them representing complex landscapes with various land categories, to evaluate the performance of Pan sharpening methods in varying land surface characteristics. The spatial and spectral performance of the Pan sharpening methods were evaluated by eleven accuracy metrics and visual interpretation. The results of the evaluation indicated that proposed CIELab color-based method reached promising results and improved the spectral and spatial information preservation.

scholarly journals Deep Hybrid Network for Land Cover Semantic Segmentation in High-Spatial Resolution Satellite Images

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 230
Author(s):  
Sultan Daud Khan ◽  
Louai Alarabi ◽  
Saleh Basalamah
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Spatial Resolution ◽  
Satellite Images ◽  
High Spatial Resolution ◽  
Multiple Scales ◽  
Semantic Segmentation ◽  
Vital Role ◽  
Hybrid Network ◽  
High Resolution Satellite Images

Land cover semantic segmentation in high-spatial resolution satellite images plays a vital role in efficient management of land resources, smart agriculture, yield estimation and urban planning. With the recent advancement in remote sensing technologies, such as satellites, drones, UAVs, and airborne vehicles, a large number of high-resolution satellite images are readily available. However, these high-resolution satellite images are complex due to increased spatial resolution and data disruption caused by different factors involved in the acquisition process. Due to these challenges, an efficient land-cover semantic segmentation model is difficult to design and develop. In this paper, we develop a hybrid deep learning model that combines the benefits of two deep models, i.e., DenseNet and U-Net. This is carried out to obtain a pixel-wise classification of land cover. The contraction path of U-Net is replaced with DenseNet to extract features of multiple scales, while long-range connections of U-Net concatenate encoder and decoder paths are used to preserve low-level features. We evaluate the proposed hybrid network on a challenging, publicly available benchmark dataset. From the experimental results, we demonstrate that the proposed hybrid network exhibits a state-of-the-art performance and beats other existing models by a considerable margin.

scholarly journals AUTOMATED PANSHARPENING INFORMATION TECHNOLOGY OF SATELLITE IMAGES

2021 ◽  
pp. 123-132
Author(s):  
V. V. Hnatushenko ◽  
V. Yu. Kashtan
Keyword(s):  
Image Processing ◽  
Information Technology ◽  
High Resolution ◽  
Spatial Resolution ◽  
Satellite Images ◽  
High Spatial Resolution ◽  
Image Resolution ◽  
Resolution Image ◽  
Ndvi Index ◽  
High Resolution Satellite Images

Context. Nowadays, information technologies are widely used in digital image processing. The task of joint processing of satellite image obtained by different space systems that have different spatial differences is important. The already known pansharpening methods to improve the quality of the resulting image, there are new scientific problems associated with increasing the requirements for high-resolution image processing and the development of automated technology for processing the satellite data for further thematic analysis. Most spatial resolution techniques result in artifacts. Our work explores the major remote sensing data fusion techniques at pixel level and reviews the concept, principals, limitations and advantages for each technique with the program implementation of research. Objective. The goal of the work is analyze the effectiveness of the traditional pan-sharpening methods like the Brovey, the wavelet-transform, the GIHS, the HCT and the combined pansharpening method for satellite images of high-spatial resolution. Method. In this paper we propose the information technology for pansharpening high spatial resolution images with automation of choosing the best method based on the analysis of quantitative and qualitative evolutions. The method involves the scaling multispectral image to the size of the panchromatic image; using histogram equalization to adjust the primary images by aligning the integral areas of the sections with different brightness; conversion of primary images after the spectral correction on traditional pansharpening methods; analyze the effectiveness of the results obtained for conducts their detailed comparative visual and quantitative evaluation. The technology allows determining the best method of pansharpening by analyzing quantitative metrics: the NDVI index, the RMSE and the ERGAS. The NDVI index for the methods Brovey and HPF indicate color distortion in comparison with the reference data. This is due to the fact that the Brovey and HPF methods are based on the fusion of three channel images and do not include the information contained in the near infrared range. The RMSE and the ERGAS show the superiority of the combined HSVHCT-Wavelet method over the conventional and state-of-art image resolution enhancement techniques of high resolution satellite images. This is achieved, in particular, by preliminary processing of primary images, data processing localized spectral bases, optimized performance information, and the information contained in the infrared image. Results. The software implementing proposed method is developed. The experiments to study the properties of the proposed algorithm are conducted. Experimental evaluation performed on eight-primary satellite images of high spatial resolution obtained WorldView-2 satellite. The experimental results show that a synthesized high spatial resolution image with high information content is achieved with the complex use of fusion methods, which makes it possible to increase the spatial resolution of the original multichannel image without color distortions. Conclusions. The experiments confirmed the effectiveness of the proposed automated information technology for pansharpening high-resolution satellite images with the development of a graphical interface to obtain a new synthesized image. In addition, the proposed technology will effectively carry out further recognition and real-time monitoring infrastructure.

scholarly journals SEMI-SUPERVISED SPECTRAL-TEXTURE IMAGE CLASSIFICATION ALGORITHM

2019 ◽  
Vol 4 (1) ◽  
pp. 37-43
Author(s):  
Sergey Rylov
Keyword(s):  
Supervised Classification ◽  
Satellite Images ◽  
Spatial Information ◽  
Spectral Characteristics ◽  
Texture Features ◽  
Training Sample ◽  
Classification Algorithm ◽  
High Resolution Satellite Images ◽  
Texture Image Classification ◽  
Supervised Classification Methods

When classifying satellite images, training sample often turns out to be unrepresentative. This leads to low segmentation quality. In such conditions, it is advisable to use semi-supervised classification methods, which simultaneously utilize both training sample and unclassified data. At the same time, high resolution satellite images are characterized by high interclass heterogeneity of spectral characteristics, which demands to take spatial information into account. We propose a new semi-supervised classification algorithm for multispectral images, that utilizes both spectral and texture features. The use of the semi-supervised concept allows improving the classification quality when the amount of training sample is small. The results of experiments on model and satellite images confirming the effectiveness of the proposed algorithm are given.

scholarly journals ANCIENT ROADS NEAR DAVYDIV BRID AT INGULETS RIVER

2021 ◽  
Vol 40 (3) ◽  
pp. 169-174
Author(s):  
O. V. Kariaka
Keyword(s):  
Satellite Images ◽  
Field Research ◽  
Line Of Sight ◽  
The Internet ◽  
The Past ◽  
High Resolution Satellite Images ◽  
Close Relationship ◽  
Relationship Of ◽  
The 19Th Century ◽  
Remote Sensing Methods

In recent years, due to expansion of the capabilities of using high-resolution satellite images and their availability on many resources on the Internet, the possibility of identifying new sites is becoming especially relevant. In order to reveal the ability of land communications as well as for the study of close relationship of the barrows and steppe roads we made the attempt to map them as completely as possible using the remote sensing methods in the area between the Dnieper and the Lower Ingulets Rivers. The coverage of large territory made it possible to identify the place of the highest concentration of road residues which was located near the modern village of Davydiv Brid (Beryslav district, Kherson region). There were three identified so-called «Stars» — Bajdak-Mohyla, Shurzhyna and Bilohirka. At the base of each of them was a large mound of the 6—8 m high. All of them were in the line of sight from one to another and marked the place of the traditional crossing of the Ingulets River. On the basis of the remnants of roads associated with them the main directions of movement were identified: 1) the largest to the southeast to the area of the modern city of Beryslav; 2) a less saturated but clearly traced eastern route to the area of the modern Dudchany village; 3) along the Ingulets valley on the top of the steppe plateau. The internal structure and arrangement of the mounds and remains of roads is very complicated and can only be partially reconstructed. For example, we consider the fact of the seasonal use of Shurzhin’s star to bypass two lowlands located near Bajdak-Mogyla as obvious. From the last point several direct routes of the southeastern direction can be traced which were convenient in the dry season. The attempt to compare the roads identified by us with the paths of the «three-verst Schubert map» of the 19th century had a few results. Only in some cases it is possible to trace the coincidence of the historical paths with those recorded by us in the southeastern direction and only on certain sections. In the eastern direction and the route along Ingulets there are no this kind of coincidences were actually revealed. As a result we consider it necessary to admit that the remains of the roads we have recorded should be considered a special type of archaeological monuments accompanying other objects, primarily mounds. The phenomenon of the remains of these roads and the «stars» described by us is the phenomenon which reflects the certain part of human activity in the past and should be studied with the involvement of the as largest as possible territories and conducting field research.

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