scholarly journals STUDY ON MOSAIC AND UNIFORM COLOR METHOD OF SATELLITE IMAGE FUSION IN LARGE SREA

2018 ◽  
Vol XLII-3 ◽  
pp. 1099-1102
Author(s):  
S. Liu ◽  
H. Li ◽  
X. Wang ◽  
L. Guo ◽  
R. Wang
Keyword(s):  
Information Entropy ◽  
Satellite Images ◽  
Satellite Image ◽  
Image Data ◽  
Texture Features ◽  
Color Difference ◽  
Large Area ◽  
Color Mapping ◽  
Multi Temporal ◽  
Mosaic Images

Due to the improvement of satellite radiometric resolution and the color difference for multi-temporal satellite remote sensing images and the large amount of satellite image data, how to complete the mosaic and uniform color process of satellite images is always an important problem in image processing. First of all using the bundle uniform color method and least squares mosaic method of GXL and the dodging function, the uniform transition of color and brightness can be realized in large area and multi-temporal satellite images. Secondly, using Color Mapping software to color mosaic images of 16bit to mosaic images of 8bit based on uniform color method with low resolution reference images. At last, qualitative and quantitative analytical methods are used respectively to analyse and evaluate satellite image after mosaic and uniformity coloring. The test reflects the correlation of mosaic images before and after coloring is higher than 95 % and image information entropy increases, texture features are enhanced which have been proved by calculation of quantitative indexes such as correlation coefficient and information entropy. Satellite image mosaic and color processing in large area has been well implemented.

Assessment of mangrove forest change in Ca Mau province using satellite images in the period of 1988 - 2018

2021 ◽  
Vol 66 (1) ◽  
pp. 175-187
Author(s):  
Duong Phung Thai ◽  
Son Ton
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Image Interpretation ◽  
Image Data ◽  
Vegetation Coverage ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Forest Change ◽  
Shrimp Ponds ◽  
Area Classification

On the basis of using practical methods, satellite image processing methods, the vegetation coverage classification system of the study area, interpretation key for the study area, classification and post-classification pro cessing, this research introduces how to exploit and process multi-temporal satellite images in evaluating the changes of forest area. Landsat 4, 5 TM and Landsat 8 OLI remote sensing image data were used to evaluate the changes in the area of mangrove forests (RNM) in Ca Mau province in the periods of 1988 - 1998, 1998 - 2013, 2013 - 2018, and 1988 - 2018. The results of the image interpretation in 1988, 1998, 2013, 2018 and the overlapping of the above maps show: In the 30-year period from 1988 to 2018, the total area of mangroves in Ca Mau province was decreased by 28% compared to the beginning, from 71,093.3 ha in 1988 reduced to 51,363.5 ha in 2018, decreasing by 19,729.8 ha. The recovery speed of mangroves is 2 times lower than their disappearance speed. Specifically, from 1988 to 2018, mangroves disappeared on an area of 42,534.9 hectares and appeared on the new area of 22,805 hectares, only 12,154.5 hectares of mangroves remained unchanged. The fluctuation of mangrove area in Ca Mau province is related to the process of deforestation to dig shrimp ponds, coastal erosion, the formation of mangroves on new coastal alluvial lands and soil dunes in estuaries, as well as planting new mangroves in inefficient shrimp ponds.

scholarly journals ALGORITHM AND APPLICATION OF GCP-INDEPENDENT BLOCK ADJUSTMENT FOR SUPER LARGE-SCALE DOMESTIC HIGH RESOLUTION OPTICAL SATELLITE IMAGERY

2018 ◽  
Vol XLII-3 ◽  
pp. 1607-1615
Author(s):  
Y. S. Sun ◽  
L. Zhang ◽  
B. Xu ◽  
Y. Zhang
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
Satellite Images ◽  
Satellite Image ◽  
Optimization Method ◽  
Adjustment Process ◽  
Large Area ◽  
Rank Defect ◽  
And Performance

The accurate positioning of optical satellite image without control is the precondition for remote sensing application and small/medium scale mapping in large abroad areas or with large-scale images. In this paper, aiming at the geometric features of optical satellite image, based on a widely used optimization method of constraint problem which is called Alternating Direction Method of Multipliers (ADMM) and RFM least-squares block adjustment, we propose a GCP independent block adjustment method for the large-scale domestic high resolution optical satellite image – GISIBA (GCP-Independent Satellite Imagery Block Adjustment), which is easy to parallelize and highly efficient. In this method, the virtual "average" control points are built to solve the rank defect problem and qualitative and quantitative analysis in block adjustment without control. The test results prove that the horizontal and vertical accuracy of multi-covered and multi-temporal satellite images are better than 10 m and 6 m. Meanwhile the mosaic problem of the adjacent areas in large area DOM production can be solved if the public geographic information data is introduced as horizontal and vertical constraints in the block adjustment process. Finally, through the experiments by using GF-1 and ZY-3 satellite images over several typical test areas, the reliability, accuracy and performance of our developed procedure will be presented and studied in this paper.

scholarly journals FEATURES OF THE THERMAL REGIME AND WATER SUPPLY FOR STEPPE BURNED AREA BASED ON LANDSAT SATELLITE IMAGES

2020 ◽  
Author(s):  
V.M. Pavleychik ◽  
◽  
K.V. Myachina ◽  
Keyword(s):  
Thermal Regime ◽  
Satellite Images ◽  
Landscape Heterogeneity ◽  
Satellite Image ◽  
Image Data ◽  
Burned Area ◽  
Recovery Processes ◽  
Satellite Image Data ◽  
Landsat Satellite ◽  
Thermal Background

Based on the analysis of Landsat satellite image data, microclimatic features of steppe burned area were identified, consisting in an increased thermal background, reduced depth and duration of snow cover. The duration of recovery processes is estimated taking into account landscape heterogeneity and regularities in the daily and seasonal dynamics of the thermal regime due to uneven insolation are revealed.

scholarly journals Pemetaan Mangrove Kepulauan Lease, Provinsi Maluku Menggunakan Data Multi-Temporal Dan Multi-Sensor Citra Satelit Landsat

2018 ◽  
Vol 3 (1) ◽  
pp. 19
Author(s):  
Sam Wouthuyzen ◽  
Fasmi Ahmad
Keyword(s):  
Cloud Cover ◽  
Supervised Classification ◽  
Satellite Images ◽  
Satellite Image ◽  
Multi Temporal ◽  
The Mean ◽  
Landsat 5 Tm ◽  
Landsat Satellite ◽  
The Cost ◽  
Near Future

<strong>Mangrove Mapping of The Lease Islands, Maluku Province Using Multi-Temporal And Multi-Sensor Of Landsat Satellite Images.</strong> Mangrove mapping in the Lease Islands, Maluku Province has been done, but using only a single date satellite image. Therefore, it is difficult to know the dynamics of their changes.  The aim of this study is to map mangroves every 5 year (1985-2015) using multi-sensors (MSS, TM, ETM+ and OLI) of Landsat and field data. Supervised classification using maximum likelihood was used for classifying mangrove and other habitats, and counting their areas. Results showed that mangrove in the Saparua and Nusalaut Islands, consisted of 22 and 13 species, respectively, with the longest distribution along the cost line of Tuhaha Bay due to freshwater supplay from the surrounding river, while the rest are grown in the hardy reef flat substrates. The mean overall acurracies of the maps was good enough (74.7%), except for one Landsat-5 TM and Landat-8 OLI because of the influences of cloud cover or haze.  During 30 years, the areas of mangrove are relatively stable since they are protected by local wisdom called "Kewang". The highest bias of 11.4% that made the areas of mangrove increase or decrease was not due to the utilization or conversion of mangrove, but mainly due to the influences of cloud cover/haze and the geometric differences among Landsat sensors. In the near future, the OBIA method should be try, because it seems to be able to produce mangrove maps with better accuracy.

scholarly journals AUTOMATIC DETECTION OF CLOUDS AND SHADOWS USING HIGH RESOLUTION SATELLITE IMAGE TIME SERIES

2016 ◽  
Vol XLI-B3 ◽  
pp. 475-479 ◽  
Author(s):  
Nicolas Champion
Keyword(s):  
Time Series ◽  
Satellite Images ◽  
Satellite Image ◽  
Region Growing ◽  
Shadow Detection ◽  
The Other ◽  
Landsat 8 ◽  
Cloud Detection ◽  
Large Area ◽  
The Difference

Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled <i>seeds</i> if the difference of reflectance (in the blue channel) with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled <i>shadows</i> if the difference of reflectance (in the NIR channel) with the <i>synthetic</i> ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled <i>clouds</i> during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8 and Pléiades-HR images and our first experiments show the feasibility to automate the detection of shadows and clouds in satellite image sequences.

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.

THE RECONSTRUCTION OF SATELLITE IMAGES BASED ON FRACTAL INTERPOLATION

Fractals ◽  
2011 ◽  
Vol 19 (03) ◽  
pp. 347-354 ◽  
Author(s):  
CHING-JU CHEN ◽  
SHU-CHEN CHENG ◽  
Y. M. HUANG
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Satellite Image ◽  
Image Data ◽  
Image Resolution ◽  
Source Image ◽  
Fractal Interpolation ◽  
Low Resolution ◽  
Resolution Image ◽  
High Resolution Image

This study discussed the application of a fractal interpolation method in satellite image data reconstruction. It used low-resolution images as the source data for fractal interpolation reconstruction. Using this approach, a high-resolution image can be reconstructed when there is only a low-resolution source image available. The results showed that the high-resolution image data from fractal interpolation can effectively enhance the sharpness of the border contours. Implementing fractal interpolation on an insufficient image resolution image can avoid jagged edges and mosaic when enlarging the image, as well as improve the visibility of object features in the region of interest. The proposed approach can thus be a useful tool in land classification by satellite images.

Using High-Resolution Satellite Images for Post-Earthquake Building Damage Assessment: A Study following the 26 January 2001 Gujarat Earthquake

2004 ◽  
Vol 20 (1) ◽  
pp. 145-169 ◽  
Author(s):  
Keiko Saito ◽  
Robin J. S. Spence ◽  
Christopher Going ◽  
Michael Markus
Keyword(s):  
Damage Assessment ◽  
Satellite Images ◽  
Satellite Image ◽  
Building Damage ◽  
Large Area ◽  
Rapid Visual Screening ◽  
High Resolution Satellite Images ◽  
Collapsed Buildings ◽  
Optical Satellite Images ◽  
The City

Newly available optical satellite images with 1-m ground resolution such as IKONOS mean that rapid postdisaster damage assessment might be made over large areas. Such surveys could be of great value to emergency management and post-event recovery operations and have particular promise for earthquake areas, where damage distribution is often very uneven. In this paper three satellite images taken before and after the 26 January 2001 Gujarat earthquake were studied for damage assessment purposes. The images comprised a post-earthquake cover of the city of Bhuj, which was close to the epicenter, and pre- and post-earthquake cover of the city Ahmedabad. The assessment data was then compared with damage surveys actually made on-site. Three separate experiments were conducted. In the first, the satellite image of Bhuj was compared with detailed ground photos of 28 severely damaged buildings taken at about the same time as the satellite image, to investigate the levels and types of damage that can and cannot be identified. In the second experiment, the whole city center of Bhuj was damage mapped using only the satellite image. This was subsequently compared with a map produced from a building-by-building damage survey. In the third experiment, pre- and post-earthquake images for a large area of Ahmedabad were compared and totally collapsed buildings were identified. These sites were subsequently visited to confirm the accuracy of the observations. The experiment results indicate that rapid visual screening can identify areas of heavy damage and individual collapsed buildings, even when comparative cover does not exist. The need to develop a tool with direct application to support emergency response is discussed.

APLIKASI DATA CITRA SATELIT LANDSAT UNTUK PEMANTAUAN DINAMIKA PESISIR MUARA DAS BARITO DAN SEKITARNYA

2016 ◽  
Vol 2 (3) ◽  
pp. 13
Author(s):  
Abdur Rahman
Keyword(s):  
Coastal Area ◽  
Satellite Image ◽  
River Estuary ◽  
Image Data ◽  
Research Area ◽  
Multi Temporal ◽  
Satellite Image Data

Telah terjadi terjadi kerusakan habitat lingkungan mangrove, abrasi dan akresi yang menyebabkan semakin tingginya muka air  di sepanjang DAS Sungai Barito (DAS Martapura, DAS Alalak dan DAS Kuin), sebab erjadinya proses abrasi dan akresi  yang terjadi di sepanjang garis pantai, terutama DAS Martapura, DAS Alalak dan DAS Kuin.Klasifikasi pemanfaatan lahan dan konversinya serta perubahan pesisir berupa akresi dan abrasi di sepanjang pantai area penelitian di analisis dengan memanfaatkan informasi dari data citra satelit Landsat multi temporal yang di peroleh pada tanggal 29 Juni tahun 1985, dan 03 September 2006.Dominasi pemanfaatan lahan berupa HPH, pertambangan dan pemukiman dengan konversi lahan pada hutan untuk pemanfaatan lain memberikan dampak erosi yang cukup besar dengan ditunjukannya wilayah pesisir yang mengalami peningkatan akresi terutama pada bagian muara sungai (delta). Tren perubahan yang terlihat pada kawasan pesisir di area penelitian selama 21 tahun adalah abrasi sebesar 294,55 m2 di daerah Muara S. Martapura, 75,53 m2 di sekitar muara S. Alalak. Dan perubahan Abrasi sebesar 177,42 m2 , dan akresi sebesar 610,86 m2 di sekitar Muara S. Barito/Kuin).Have happened happened damage of environmental habitat of mangrove, and abrasi of akresi causing its excelsior of face irrigate alongside DAS River of Barito (DAS Martapura, DAS Alalak and of DAS Kuin), because the happening of process of abrasi and of akresi that happened alongside coastline, especially DAS Martapura, DAS Alalak and  DAS Kuin. Classification exploiting of farm and its conversion and also change of coastal area in the form of and akresi of abrasialongside research area coast in analysis by exploiting information of satellite image data of Landsat temporal multi which in obtaining on 29 June year 1985, and 03 September 2006.Domination exploiting of farm in the form of HPH, settlement and mining with farm conversion at forest for other exploiting give big enough erosion impact with  regional and natural coastal area that make-up of akresi especially  part of river estuary    (delta).  Seen Change Tren at coastal area in research area during 21 year was abrasi equal to 294,55 m2 in Estuary area S. Martapura, 75,53 m2 around estuary S. Alalak. And change of Abrasi equal to 177,42 m2 , and akresi equal to 610,86 m2 around Estuary S. Barito / kuin).           

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