Image Segmentation Based Approach for the Purpose of Developing Satellite Image Spatial Information Extraction for Forestation and River Bed Analysis

2019 ◽  
Vol 19 (01) ◽  
pp. 1950002 ◽  
Author(s):  
P. K. Dutta
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Satellite Images ◽  
Spatial Information ◽  
Satellite Image ◽  
Graph Cuts ◽  
Google Earth ◽  
Image Resolution ◽  
Registration Method ◽  
River Bed

Classification of remote sensing spatial information from multi spectral satellite imagery can be used to obtain multiple representation of the image and capture different structure lineaments. Pixels are grouped using clustering and morphology based segmentation for region based spatial information. This is used to calculate the spatial features of the contiguous regions by classifying the region into the statistics of the pixel properties. In the proposed work, analysis of Google Earth images for identification of morphological patterns of the river flow is done for remote sensing image using graph-cuts. Multi-temporal satellite images acquired from Google Earth to identify the digital elevation is used to formulate the energy function from images to compare the displacement in pixel value using similarity measure. A method is proposed to solve non-rigid image transformation via graph-cuts algorithm by modeling the registration process as a discrete labeling problem. A displacement vector associated to each pixel in the source image indicates the corresponding position in the moving image. The transformation matrix produced from change in the intensity of the pixels for a region is then optimized for energy minimization by using the graph-cuts algorithm and demon registration technique. The proposed study enhances the advantages of regional segmentation in order to know homogeneous areas for optimal image segmentation and digital footprints for change in the river bed patterns by identifying the change in LANDSAT data from temporal satellite images. By applying the proposed multi-level registration method, the number of labels used in each level is greatly reduced due to lower image resolution being used in coarser levels. The results demonstrate that the lineament detection for better accuracy compared to traditional sources of lineament identification methods. It has provided better geotectonic understanding of Cudappah rock in Ahobhilam with Quartzite. The imprints of Eastern Ghat orogeny are seen in upper stream section through a graph cut based segmentation approach.

scholarly journals Assessment of the Segmentation of RGB Remote Sensing Images: A Subjective Approach

2020 ◽  
Vol 12 (24) ◽  
pp. 4152
Author(s):  
Giruta Kazakeviciute-Januskeviciene ◽  
Edgaras Janusonis ◽  
Romualdas Bausys ◽  
Tadas Limba ◽  
Mindaugas Kiskis
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Quality Assessment ◽  
Satellite Images ◽  
Clustering Algorithm ◽  
Satellite Image ◽  
Quality Metrics ◽  
Data Set ◽  
Segmentation Quality

The evaluation of remote sensing imagery segmentation results plays an important role in the further image analysis and decision-making. The search for the optimal segmentation method for a particular data set and the suitability of segmentation results for the use in satellite image classification are examples where the proper image segmentation quality assessment can affect the quality of the final result. There is no extensive research related to the assessment of the segmentation effectiveness of the images. The designed objective quality assessment metrics that can be used to assess the quality of the obtained segmentation results usually take into account the subjective features of the human visual system (HVS). A novel approach is used in the article to estimate the effectiveness of satellite image segmentation by relating and determining the correlation between subjective and objective segmentation quality metrics. Pearson’s and Spearman’s correlation was used for satellite images after applying a k-means++ clustering algorithm based on colour information. Simultaneously, the dataset of the satellite images with ground truth (GT) based on the “DeepGlobe Land Cover Classification Challenge” dataset was constructed for testing three classes of quality metrics for satellite image segmentation.

scholarly journals High resolution remote sensing image segmentation based on graph theory and fractal net evolution approach

2015 ◽  
Vol XL-7/W4 ◽  
pp. 197-201 ◽  
Author(s):  
Y. Yang ◽  
H. T. Li ◽  
Y. S. Han ◽  
H. Y. Gu
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Graph Theory ◽  
High Resolution ◽  
Spatial Information ◽  
Remote Sensing Image ◽  
Sensing Applications ◽  
Initial Object ◽  
Object Layer ◽  
Evolution Approach

Image segmentation is the foundation of further object-oriented image analysis, understanding and recognition. It is one of the key technologies in high resolution remote sensing applications. In this paper, a new fast image segmentation algorithm for high resolution remote sensing imagery is proposed, which is based on graph theory and fractal net evolution approach (FNEA). Firstly, an image is modelled as a weighted undirected graph, where nodes correspond to pixels, and edges connect adjacent pixels. An initial object layer can be obtained efficiently from graph-based segmentation, which runs in time nearly linear in the number of image pixels. Then FNEA starts with the initial object layer and a pairwise merge of its neighbour object with the aim to minimize the resulting summed heterogeneity. Furthermore, according to the character of different features in high resolution remote sensing image, three different merging criterions for image objects based on spectral and spatial information are adopted. Finally, compared with the commercial remote sensing software eCognition, the experimental results demonstrate that the efficiency of the algorithm has significantly improved, and the result can maintain good feature boundaries.

Application of Polarization Remote Sensing to Identify Activity of Yitong-Shulan Fault, Jilin Province, China

2014 ◽  
Vol 1065-1069 ◽  
pp. 2246-2250
Author(s):  
Jian Sheng ◽  
Guang Yuan Yu ◽  
Yu Meng Wang ◽  
Han Lv
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Eastern China ◽  
Satellite Image ◽  
Jilin Province ◽  
Geophysical Method ◽  
Fault Activity ◽  
Shitoukoumen Reservoir ◽  
Observation Method ◽  
Buried Fault

Yitong-Shulan fault, one north section of the famed Tanlu grand fault zone in eastern China, is NNE-trending though the Jilin Province, China. In October 2010, Heilongjiang segment of this fault was discovered the evidence of its activity in Holonce, and further inferred it is associated with a paleoearthquake event. So the recognize of Yitong-Shulan fault Jilin section active in the early Quaternary capable of generating moderate quakes is doubted. Yitong-Shulan fault is almost covered by Quaternary strata in Jilin Province. Traditional method is difficult to explore buried fault, and geophysical method is partial and expensive. The polarization remote sensing is a kind of emerging earth observation method, which has high terrain-recognization resolution. The polarization remote sensing method can to indentify the scarps and displaced geomorphic objects along the fault though satellite images. It even can to discover the high of scarps, displacement of geomorphic objects, and so on. The fault activity can be indicated well by the interpretation of polarization remote sensing. In this paper, use the polarization remote sensing method to study the activity of Yitong-Shulan fault Jilin section. Satellite image near the Shulan City, Jilin Province interpreted by polarization remote sensing reveals that the obviously linear scarps which extend long the fault is 1-3m high. Along the fault various kinds of geomorphic objects are displaced. This interpretation result indicated the Shulan-Shitoukoumen Reservoir segment of the fault is active since Holocene. The fault activity also is proved by geophysical method.

scholarly journals Detection of Archaeological Looting from Space: Methods, Achievements and Challenges

2019 ◽  
Vol 11 (20) ◽  
pp. 2389 ◽  
Author(s):  
Deodato Tapete ◽  
Francesca Cigna
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
High Resolution ◽  
Satellite Data ◽  
Satellite Images ◽  
Google Earth ◽  
Optical Data ◽  
Mena Region ◽  
Processing Methods ◽  
Optical Imagery

Illegal excavations in archaeological heritage sites (namely “looting”) are a global phenomenon. Satellite images are nowadays massively used by archaeologists to systematically document sites affected by looting. In parallel, remote sensing scientists are increasingly developing processing methods with a certain degree of automation to quantify looting using satellite imagery. To capture the state-of-the-art of this growing field of remote sensing, in this work 47 peer-reviewed research publications and grey literature are reviewed, accounting for: (i) the type of satellite data used, i.e., optical and synthetic aperture radar (SAR); (ii) properties of looting features utilized as proxies for damage assessment (e.g., shape, morphology, spectral signature); (iii) image processing workflows; and (iv) rationale for validation. Several scholars studied looting even prior to the conflicts recently affecting the Middle East and North Africa (MENA) region. Regardless of the method used for looting feature identification (either visual/manual, or with the aid of image processing), they preferred very high resolution (VHR) optical imagery, mainly black-and-white panchromatic, or pansharpened multispectral, whereas SAR is being used more recently by specialist image analysts only. Yet the full potential of VHR and high resolution (HR) multispectral information in optical imagery is to be exploited, with limited research studies testing spectral indices. To fill this gap, a range of looted sites across the MENA region are presented in this work, i.e., Lisht, Dashur, and Abusir el Malik (Egypt), and Tell Qarqur, Tell Jifar, Sergiopolis, Apamea, Dura Europos, and Tell Hizareen (Syria). The aim is to highlight: (i) the complementarity of HR multispectral data and VHR SAR with VHR optical imagery, (ii) usefulness of spectral profiles in the visible and near-infrared bands, and (iii) applicability of methods for multi-temporal change detection. Satellite data used for the demonstration include: HR multispectral imagery from the Copernicus Sentinel-2 constellation, VHR X-band SAR data from the COSMO-SkyMed mission, VHR panchromatic and multispectral WorldView-2 imagery, and further VHR optical data acquired by GeoEye-1, IKONOS-2, QuickBird-2, and WorldView-3, available through Google Earth. Commonalities between the different image processing methods are examined, alongside a critical discussion about automation in looting assessment, current lack of common practices in image processing, achievements in managing the uncertainty in looting feature interpretation, and current needs for more dissemination and user uptake. Directions toward sharing and harmonization of methodologies are outlined, and some proposals are made with regard to the aspects that the community working with satellite images should consider, in order to define best practices of satellite-based looting assessment.

scholarly journals Estimation of PM10 Distribution using Landsat5 and Landsat8 Remote Sensing

Proceedings ◽  
2018 ◽  
Vol 2 (23) ◽  
pp. 1430
Author(s):  
V. M. Fernández-Pacheco ◽  
C. A. López-Sánchez ◽  
E. Álvarez-Álvarez ◽  
M. J. Suárez López ◽  
L. García-Expósito ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Pollutants ◽  
Satellite Images ◽  
Satellite Image ◽  
Remote Sensing Image ◽  
Regional Studies ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat 5 Tm ◽  
Source Of Information

Air pollution is one of the major environmental problems, especially in industrial and highly populated areas. Remote sensing image is a rich source of information with many uses. This paper is focused on estimation of air pollutants using Landsat-5 TM and Landsat-8 OLI satellite images. Particulate Matter with particle size less than 10 microns (PM10) is estimated for the study area of Principado de Asturias (Spain). When a satellite records the radiance of the surface received at sensor, does not represent the true radiance of the surface. A noise caused by Aerosol and Particulate Matters attenuate that radiance. In many applications of remote sensing, that noise called path radiance is removed during pre-processing. Instead, path radiance was used to estimate the PM10 concentration in the air. A relationship between the path radiance and PM10 measurements from ground stations has been established using Random Forest (RF) algorithm and a PM10 map was generated for the study area. The results show that PM10 estimation through satellite image is an efficient technique and it is suitable for local and regional studies.

scholarly journals Land Use and Land Cover Mapping of Davangere using Google Earth Engine

2019 ◽  
Vol 8 (3) ◽  
pp. 474-477
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Environmental Changes ◽  
Satellite Image ◽  
Google Earth ◽  
Kappa Coefficient ◽  
Error Matrix ◽  
Sensing Applications ◽  
Google Earth Engine

Ever since the advent of modern geo information systems, tracking environmental changes due to natural and/or manmade causes with the aid of remote sensing applications has been an indispensable tool in numerous fields of geography, most of the earth science disciplines, defence, intelligence, commerce, economics and administrative planning. One among these applications is the construction of land use and land cover maps through image classification process. Land Use / Land Cover (LULC) information is a crucial input in designing efficient strategies for managing natural resources and monitoring environmental changes from time to time. The present study aims to know the extent of land cover and its usage in Davangere region of Karnataka, India. In this study, satellite image of Davangere during October-November 2018 was used for LULC supervised classification with the help of remote sensing tools like QGIS and Google Earth Engine. Six LULC classes were decided to locate on the map and the accuracy assessment was done using theoretical error matrix and Kappa coefficient. The key findings include LULC under Water bodies (8%), Built up Area (15.1%), Vegetation (9%), Horticulture (20.8%), Agriculture (39.3%) and Others (7%) with overall accuracy of 94.8% and Kappa coefficient of 0.866 indicating almost accurate goodness of classification

scholarly journals Contenido y calidad de las imágenes de observación terrestre

2019 ◽  
pp. 15-21
Keyword(s):  
Remote Sensing ◽  
Image Quality ◽  
Information Extraction ◽  
Satellite Images ◽  
Satellite Image ◽  
La Paz ◽  
Sensing System ◽  
Manual Intervention ◽  
Shape Structure ◽  
Important Amount

Contenido y calidad de las imágenes de observación terrestre Earth observation image information content and quality Avid Roman-Gonzalez, Natalia Indira Vargas-Cuentas TELECOM ParisTech, 46 rue Barrault, 75013 – Paris, Francia Escuela Militar de Ingeniería – EMI, La Paz, Bolivia DOI: https://doi.org/10.33017/RevECIPeru2012.0015/ Resumen En el presente artículo describiremos la extracción de información de imágenes satelitales y la importancia de la calidad de las imágenes satelitales. Indagaremos con más detalle en el ámbito de los artefactos y su influencia en la extracción de información de las imágenes satelitales. En un sistema de teledetección, si bien, las imágenes son muy importantes, pero lo más importante es la información que podemos extraer de ellas para interpretar y aplicar esta información en diferentes campos. En ese sentido, la calidad de imagen juega un papel importante. Si queremos obtener la mayor e importante cantidad de información de una imagen, es necesario que la imagen tenga una buena calidad. El principal objetivo de cualquier sistema de teledetección es el uso de la información que se puede extraer de las imágenes, esto incluye la detección, medición, identificación e interpretación de diferentes objetivos de interés. Los objetivos de interés en imágenes de teledetección pueden ser cualquier característica, objeto, textura, forma, estructura, espectro o cobertura superficial que están en la imagen. El proceso de un sistema de teledetección y análisis puede ser realizado manualmente o de manera automática, en realidad, hay muchos grupos de investigación que desarrollan diferentes herramientas para detectar, identificar, interpretar y extraer información de los objetivos de interés sin intervención manual de un intérprete humano. Descriptores: teledetección, imágenes satelitales, detección de artefactos, calidad de las imágenes. Abstract In this article we will describe the information extraction from satellite image, the importance of image quality in satellite image. In this paper we will study in more detail the artifacts and their influence on the information extraction from satellite images. In a remote sensing system, although, the images are very important, but more important is the information that we can extract from them to interpret and apply this information in different fields. In this sense, the image quality plays an important role. If we want to get the biggest and most important amount of information from the image, we need to have a good image quality. The main objective of any remote sensing system is the use of information that we can extract from the images, this includes detection, measurement, identification and interpretation of different targets. Targets in remote sensing images may be any feature, object, texture, shape, structure, spectrum or land covers which are in the image. Remote sensing process and analysis could be performed manually or automatically, actually, there are many research groups that develop different tools for detect, identify, extract information and interpret targets without manual intervention by a human interpreter. Keywords: remote sensing, satellite images, artifacts detection, image quality.

scholarly journals REGION OF INTEREST DETECTION BASED ON HISTOGRAM SEGMENTATION FOR SATELLITE IMAGE

2016 ◽  
Vol XLI-B7 ◽  
pp. 249-255
Author(s):  
Warinthorn Kiadtikornthaweeyot ◽  
Adrian R. L. Tatnall
Keyword(s):  
Image Segmentation ◽  
High Resolution ◽  
Satellite Images ◽  
Satellite Image ◽  
Region Of Interest ◽  
Satellite Imaging ◽  
Proposed Model ◽  
High Resolution Satellite Images ◽  
Image Area ◽  
Interest Detection

High resolution satellite imaging is considered as the outstanding applicant to extract the Earth’s surface information. Extraction of a feature of an image is very difficult due to having to find the appropriate image segmentation techniques and combine different methods to detect the Region of Interest (ROI) most effectively. This paper proposes techniques to classify objects in the satellite image by using image processing methods on high-resolution satellite images. The systems to identify the ROI focus on forests, urban and agriculture areas. The proposed system is based on histograms of the image to classify objects using thresholding. The thresholding is performed by considering the behaviour of the histogram mapping to a particular region in the satellite image. The proposed model is based on histogram segmentation and morphology techniques. There are five main steps supporting each other; Histogram classification, Histogram segmentation, Morphological dilation, Morphological fill image area and holes and ROI management. The methods to detect the ROI of the satellite images based on histogram classification have been studied, implemented and tested. The algorithm is be able to detect the area of forests, urban and agriculture separately. The image segmentation methods can detect the ROI and reduce the size of the original image by discarding the unnecessary parts.

scholarly journals Estimation of Forest Stand Parameters Using SPOT-5 Satellite Images and Topographic Information

2017 ◽  
Author(s):  
Shiqin Xie ◽  
Wei Wang ◽  
Qian liu ◽  
Jinghui Meng ◽  
Tianzhong Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Satellite Image ◽  
Forest Stand ◽  
Textural Features ◽  
Spectral Features ◽  
Topographic Information ◽  
Study Results ◽  
Stand Parameters ◽  
Spot 5

In recent years, remote sensing technology has been widely used to predict forest stand parameters. In order to compare the effects of different features of remote sensing images and topographic information on the prediction of forest stand parameters, multivariate stepwise regression analysis method was used to build estimation models for important forest stand parameters by using textural and spectral features as well as topographic information of SPOT-5 satellite images in northeastern Heilongjiang Province in China as independent variables. The study results show that the optimal window to predict forest stand parameters using textural features of SPOT-5 satellite image is 9×9; the ability of textural features was better than that of spectral features in terms of predicting forest stand parameters; with the inclusion of topographic information, the accuracy of prediction of all models was improved, of which elevation has the most significant effect. The highest accuracy was achieved when predicting the stand volume (SV) (R2adj=0.820), followed by basal area (BA) (R2adj =0.778), accuracy of both above models exceeded 75%. The results show that models combined use of textural, spectral features and topographic information of SPOT-5 images have a good application prospect in predicting forest stand parameters.

scholarly journals REMOTE SENSING IMAGE CLASSIFICATION USING ARTIFICIAL BEE COLONY ALGORITHM

2014 ◽  
pp. 155-160
Author(s):  
SRIDEEPA BANERJEE ◽  
AKANKSHA BHARADWAJ ◽  
DAYA GUPTA ◽  
V.K. PANCHAL
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Image Classification ◽  
Artificial Bee Colony ◽  
Spatial Information ◽  
Satellite Image ◽  
Fuzzy Classifier ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

Remote Sensing has been globally used for knowledge elicitation of earth’s surface and atmosphere. Land cover mapping, one of the widely used applications of remote sensing is a method for acquiring geo-spatial information from satellite data. We have attempted here to solve the land cover problem by image classification using one of the newest and most promising Swarm techniques of Artificial Bee Colony optimization (ABC). In this paper we propose an implementation of ABC for satellite image classification. ABC is used for optimal classification of images for mapping the land-usage efficiently. The results produced by ABC algorithm are compared with the results obtained by other techniques like BBO, MLC, MDC, Membrane computing and Fuzzy classifier to show the effectiveness of our proposed implementation.

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