scholarly journals Chlorophyll Segmentation Of Satellite Image With Region-Based Active Contours

2020 ◽  
Author(s):  
Herman Kabetta
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Image Segmentation ◽  
Satellite Imagery ◽  
Active Contours ◽  
Satellite Image ◽  
Accurate Method ◽  
Complex Task ◽  
Surrounding Areas

In image processing, segmentation is a complex task that requires the use of an accurate method. Interpretation of satellite image today is important in remote sensing tasks. A defined area of satellite image segmentation to be performed. The purpose of this study was to use a method for extracting boundary Active Contours of chlorophyll from satellite imagery, so it can be known areas that contain chlorophyll and areas that do not contain chlorophyll. The results demonstrate the effectiveness of this method of segmentation between the chlorophyll and the surrounding areas that contain little or no chlorophyll.

RNN-based multispectral satellite image processing for remote sensing applications

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Venkata Dasu Marri ◽  
Veera Narayana Reddy P. ◽  
Chandra Mohan Reddy S.
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Deep Learning ◽  
Image Classification ◽  
Satellite Image ◽  
Multispectral Image ◽  
Content Type ◽  
Colliding Bodies Optimization ◽  
Sensing Applications ◽  
Remote Sensing Applications

Purpose Image classification is a fundamental form of digital image processing in which pixels are labeled into one of the object classes present in the image. Multispectral image classification is a challenging task due to complexities associated with the images captured by satellites. Accurate image classification is highly essential in remote sensing applications. However, existing machine learning and deep learning–based classification methods could not provide desired accuracy. The purpose of this paper is to classify the objects in the satellite image with greater accuracy. Design/methodology/approach This paper proposes a deep learning-based automated method for classifying multispectral images. The central issue of this work is that data sets collected from public databases are first divided into a number of patches and their features are extracted. The features extracted from patches are then concatenated before a classification method is used to classify the objects in the image. Findings The performance of proposed modified velocity-based colliding bodies optimization method is compared with existing methods in terms of type-1 measures such as sensitivity, specificity, accuracy, net present value, F1 Score and Matthews correlation coefficient and type 2 measures such as false discovery rate and false positive rate. The statistical results obtained from the proposed method show better performance than existing methods. Originality/value In this work, multispectral image classification accuracy is improved with an optimization algorithm called modified velocity-based colliding bodies optimization.

scholarly journals Ground Data Inputs To Image Processing for Estimating Terrain Characteristics for Glacio-Hydrological Analysis

1987 ◽  
Vol 9 ◽  
pp. 45-49 ◽  
Author(s):  
M.J. Clark ◽  
A.M. Gurnell ◽  
P.J. Hancock
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Satellite Imagery ◽  
Geometric Correction ◽  
Data Set ◽  
Glacial Environments ◽  
Methodological Problems ◽  
Terrain Characteristics ◽  
Radiometric Data

Remote-sensing research in glacial and pro-glacial environments raises several methodological problems relating to the handling of ground and satellite radiometric data. An evaluation is undertaken of the use of ground radiometry to elucidate properties of relevant surface types in order to interpret satellite imagery. It identifies the influence that geometric correction and re-sampling have on the radiometric purity of the resulting data set. Methodological problems inherent in deriving catchment terrain characteristics are discussed with reference to currently glacierized and pro-glacial zones of south-western Switzerland.

scholarly journals Optical Remote Sensing

2021 ◽  
pp. 315-344
Author(s):  
Man Sing Wong ◽  
Xiaolin Zhu ◽  
Sawaid Abbas ◽  
Coco Yin Tung Kwok ◽  
Meilian Wang
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Information Extraction ◽  
Urban Areas ◽  
Satellite Image ◽  
Background Information ◽  
Optical Remote Sensing ◽  
Basic Operation ◽  
City Development ◽  
Satellite Image Processing

AbstractApplications of Earth-observational remote sensing are rapidly increasing over urban areas. The latest regime shift from conventional urban development to smart-city development has triggered a rise in smart innovative technologies to complement spatial and temporal information in new urban design models. Remote sensing-based Earth-observations provide critical information to close the gaps between real and virtual models of urban developments. Remote sensing, itself, has rapidly evolved since the launch of the first Earth-observation satellite, Landsat, in 1972. Technological advancements over the years have gradually improved the ground resolution of satellite images, from 80 m in the 1970s to 0.3 m in the 2020s. Apart from the ground resolution, improvements have been made in many other aspects of satellite remote sensing. Also, the method and techniques of information extraction have advanced. However, to understand the latest developments and scope of information extraction, it is important to understand background information and major techniques of image processing. This chapter briefly describes the history of optical remote sensing, the basic operation of satellite image processing, advanced methods of object extraction for modern urban designs, various applications of remote sensing in urban or peri-urban settings, and future satellite missions and directions of urban remote sensing.

scholarly journals Analysis of Changes in Area and Density of Mangroves through Landsat 8 Satellite Image Processing

2019 ◽  
Vol 9 (2) ◽  
pp. 16-22
Author(s):  
Nadya Fiqi Nurcahyani
Keyword(s):  
Image Processing ◽  
Satellite Imagery ◽  
Good Accuracy ◽  
Social Values ◽  
Satellite Image ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Android Application ◽  
Mangrove Area ◽  
Satellite Image Processing

Mangrove forests have high ecological, economic and social values ??which function to maintain shoreline stability, protect beaches and riverbanks, filter and remediate waste, and to withstand floods and waves. The facts show that mangrove damage is everywhere, even the intensity of damage and its area tends to increase significantly. Many roles of mangroves require proper management to maintain the existence of mangroves. One way to determine the area of ??mangroves is by processing Landsat 8 satellite imagery. The stages of mangrove identification are carried out by using 564 RGB band merger, then separating the mangrove and non-mangrove objects. Next step is to analyze the density of mangroves using NDVI formula. To maximize monitoring of mangrove area, an android application was created that provides information on the area and density of mangroves at several locations, namely Clungup, Bangsong Teluk Asmara and Cengkrong from 2015 to 2018.The results showed that Landsat 8 satellite imagery can be used to identify changes in the area of ??mangrove forests with good accuracy, namely in the Clungup area of ??90% and Cengkrong of 86.67%. From processing results, the mangrove area in the Clungup area has also decreased from 2015 to 2017 but has increased in 2018 so that the application provides recommendations for embroidering mangroves in 2016 to 2017 and mangrove recommendations are maintained in 2018. As for Bangsong Teluk area Asmara and Cengkrong have increased the area of ??mangroves every year so that the application provides recommendations to be maintained from 2016 to 2018.

scholarly journals STUDY OF DUST POLLUTION DETECTING ABILITY IN URBAN AREAS BY REMOTE SENSING TECHNOLOGY TO SUPPORT AIR ENVIRONMENT OBSERVATION

2012 ◽  
Vol 15 (4) ◽  
pp. 33-47
Author(s):  
Van Thi Tran ◽  
Binh Thi Trinh ◽  
Bao Duong Xuan Ha
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Satellite Image ◽  
Regression Function ◽  
Statistical Correlation ◽  
Step Test ◽  
Residential Areas ◽  
Remote Sensing Technology ◽  
Sensing Technology

This paper presents the approach towards application of remote sensing technology to monitor the air environemnt. Specific inital research is findings PM10 dust from SPOT 5 satellite image. The calculation based on reflectance value on remote sensing satellite images. The main method is to calculate statistical correlation regression between the PM10 concentration from ground station observations and reflectance value on each image band and the main components of satellite imagery in 2003 to find the best regression function, applied then to images 2011 where its radiance value was relatively normalized under atmospheric, geometric, environmental conditions of image 2003. The results showed the best correlation in nonlinear regression case. Spatial distribution of PM10 concentrations > 200μg/m3 found on most main roads, industrial parks and residential areas. This study is a first step test, but the results have demonstrated that satellite imagery can be used as a useful, effective tool, to monitor air environment in cities.

Remote Sensing of Broom Snakeweed (Gutierrezia Sarothrae) with Noaa-10 Spectral Image Processing

1992 ◽  
Vol 6 (4) ◽  
pp. 1015-1020 ◽  
Author(s):  
Albert J. Peters ◽  
Bradley C. Reed ◽  
Marlen D. Eve ◽  
Kirk C. McDaniel
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
New Mexico ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Spectral Image ◽  
Late Season ◽  
Shortgrass Prairie ◽  
Gutierrezia Sarothrae ◽  
Meteorological Satellite

Low-spatial resolution satellite imagery from the NOAA-10 polar-orbiting meteorological satellite was analyzed to determine if central New Mexico grasslands infested by broom snakeweed could be discriminated from unaffected areas. Distinctive phenological characteristics of broom snakeweed, including an early season growth flush and late season flowering, enable moderate to heavily infested areas to be separated from grasslands having few or no weeds present. The procedure used shows promise as a tool for locating and monitoring brown snakeweed and other weeds growing on shortgrass prairie.

scholarly journals THERMAL AND VISIBLE SATELLITE IMAGE FUSION USING WAVELET IN REMOTE SENSING AND SATELLITE IMAGE PROCESSING

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 11-15
Author(s):  
A. H. Ahrari ◽  
M. Kiavarz ◽  
M. Hasanlou ◽  
M. Marofi
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Image Fusion ◽  
Satellite Image ◽  
Thermal Infrared ◽  
Qualitative Assessment ◽  
Spectral Information ◽  
Visible Image ◽  
Qualitative Approaches ◽  
Satellite Image Processing

Multimodal remote sensing approach is based on merging different data in different portions of electromagnetic radiation that improves the accuracy in satellite image processing and interpretations. Remote Sensing Visible and thermal infrared bands independently contain valuable spatial and spectral information. Visible bands make enough information spatially and thermal makes more different radiometric and spectral information than visible. However low spatial resolution is the most important limitation in thermal infrared bands. Using satellite image fusion, it is possible to merge them as a single thermal image that contains high spectral and spatial information at the same time. The aim of this study is a performance assessment of thermal and visible image fusion quantitatively and qualitatively with wavelet transform and different filters. In this research, wavelet algorithm (Haar) and different decomposition filters (mean.linear,ma,min and rand) for thermal and panchromatic bands of Landast8 Satellite were applied as shortwave and longwave fusion method . Finally, quality assessment has been done with quantitative and qualitative approaches. Quantitative parameters such as Entropy, Standard Deviation, Cross Correlation, Q Factor and Mutual Information were used. For thermal and visible image fusion accuracy assessment, all parameters (quantitative and qualitative) must be analysed with respect to each other. Among all relevant statistical factors, correlation has the most meaningful result and similarity to the qualitative assessment. Results showed that mean and linear filters make better fused images against the other filters in Haar algorithm. Linear and mean filters have same performance and there is not any difference between their qualitative and quantitative results.

SOME RESULTS OF CROP STRESS MONITORING BY REMOTE SENSING IN NORTHERN MONGOLIA

2018 ◽  
Vol 21 (02) ◽  
pp. 59-63 ◽  
Author(s):  
Tuvshinbayar D ◽  
Erdenetuya B ◽  
Erkhembayar E ◽  
Batbileg B ◽  
Sarangerel J
Keyword(s):  
Remote Sensing ◽  
Stress Field ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Satellite Image ◽  
Stress Monitoring ◽  
Wheat Growth ◽  
Northern Mongolia ◽  
Middle Period ◽  
Farming Industry

This paper presents the spatiotemporal monitoring crop stress in the first period of wheat phenology by satellite image in northern Mongolia. We used 2 satellite images Landsat8 that are dated June 23rd and July 12th of this year. Also calculated are same ratio-based indices such as NDVI, LAI and GNDVI of 2 images in the middle period of wheat phrenology, which are indicated crop stress field reports. NDVI and LAI, derived from satellite imagery are the most important characteristics of wheat stress monitoring. According to our result, as shown satellite image, wheat growth is critical and fuzzily, which is predicted necessary some management for farming. Our results show the ability of pre-processing image to analyze and visualize agricultural environments and workflows has proven to be beneficial to those involved in the farming industry.

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