Land use Image Segmentation for Coimbatore District using LANDSAT 7 Satellite Data

2021 ◽  
Vol 30 (8) ◽  
Author(s):  
Y Baby Kalpana
2007 ◽  
Vol 26 (4) ◽  
pp. 247-264
Author(s):  
Elna Van Niekerk

Since the initiation in 1960 of the era of satellite remote sensing to detect the different characteristics of the earth, a powerful tool was created to aid researchers. Many land-use studies were undertaken using Landsat MSS, Landsat TM and ETM, as well as SPOT satellite data. The application of these data to the mapping of land use and land cover at smaller scales was constrained by the limited spectral and/or spatial resolution of the data provided by these satellite sensors. In view of the relatively high cost of SPOT data, and uncertainty regarding the future continuation of the Landsat series, alternative data sources need to be investigated. In the absence of published previous research on this issue in South Africa, the purpose of this article is to investigate the value of visual interpretation of ASTER satellite images for the identification and mapping of land-use in an area in South Africa. The study area is situated in Mpumalanga, in the area of Witbank, around the Witbank and Doorndraai dams. This area is characterised by a variety of urban, rural and industrial land uses. Digital image processing of one Landsat 5 TM, one Landsat 7 ETM and one ASTER satellite image was undertaken, including atmospheric correction and georeferencing, natural colour composites, photo infrared colour composites (or false colour satellite images), band ratios, Normalised Difference Indices, as well as the Brightness, Greenness and Wetness Indices. The efficacy with which land use could be identified through the visual interpretation of the processed Landsat 5 TM, Landsat 7 TM and ASTER satellite images was compared. The published 1:50 000 topographical maps of the area were used for the purpose of initial verification. Findings of the visual interpretation process were verified by field visits to the study area. The study found that the ASTER satellite data produced clearer results and therefore have a higher mapping ability and capacity than the Landsat satellite data. Hence, it is anticipated that the use of the full range of the spectral resolution of the ASTER satellite data – which were not available for this study – in statistical pattern recognition and classification methods will enhance the value of the process. Statistical methods are often used to produce visual information which could be applied to prepare land-use change inventories. This should be addressed in future research projects. Should the Landsat programme be terminated, ASTER satellite data might provide the best alternative for a variety of research projects, but if the Landsat project is continued, the ASTER satellite data could be used very effectively in conjunction with the Landsat satellite data. Since it is foreseen that the ASTER satellite data will be available for at least the next 12 to 15 years, it will continue to provide exciting possibilities for the development of programmes to monitor land-use and land-use change. This could then be used by all three levels of government to reach their goals in terms of agricultural planning, town and regional planning and environmental management. These requirements are described in the Integrated Development Programmes (IDP) of the different local governments.


2021 ◽  
Vol 10 (1) ◽  
pp. 17
Author(s):  
Nicola Case ◽  
Alfonso Vitti

Digital images, and in particular satellite images acquired by different sensors, may present defects due to many causes. Since 2013, the Landsat 7 mission has been affected by a well-known issue related to the malfunctioning of the Scan Line Corrector producing very characteristic strips of missing data in the imagery bands. Within the vast and interdisciplinary image reconstruction application field, many works have been presented in the last few decades to tackle the specific Landsat 7 gap-filling problem. This work proposes another contribution in this field presenting an original procedure based on a variational image segmentation model coupled with radiometric analysis to reconstruct damaged images acquired in a multi-temporal scenario, typical in satellite remote sensing. The key idea is to exploit some specific features of the Mumford–Shah variational model for image segmentation in order to ease the detection of homogeneous regions which will then be used to form a set of coherent data necessary for the radiometric reconstruction of damaged regions. Two reconstruction approaches are presented and applied to SLC-off Landsat 7 data. One approach is based on the well-known histogram matching transformation, the other approach is based on eigendecomposition of the bands covariance matrix and on the sampling from Gaussian distributions. The performance of the procedure is assessed by application to artificially damaged images for self-validation testing. Both of the proposed reconstruction approaches had led to remarkable results. An application to very high resolution WorldView-3 data shows how the procedure based on variational segmentation allows an effective reconstruction of images presenting a great level of geometric complexity.


2020 ◽  
Vol 30 (1) ◽  
pp. 273-286
Author(s):  
Kalyan Mahata ◽  
Rajib Das ◽  
Subhasish Das ◽  
Anasua Sarkar

Abstract Image segmentation in land cover regions which are overlapping in satellite imagery, is one crucial challenge. To detect true belonging of one pixel becomes a challenging problem while classifying mixed pixels in overlapping regions. In current work, we propose one new approach for image segmentation using a hybrid algorithm of K-Means and Cellular Automata algorithms. This newly implemented unsupervised model can detect cluster groups using hybrid 2-Dimensional Cellular-Automata model based on K-Means segmentation approach. This approach detects different land use land cover areas in satellite imagery by existing K-Means algorithm. Since it is a discrete dynamical system, cellular automaton realizes uniform interconnecting cells containing states. In the second stage of current model, we experiment with a 2-dimensional cellular automata to rank allocations of pixels among different land-cover regions. The method is experimented on the watershed area of Ajoy river (India) and Salinas (California) data set with true class labels using two internal and four external validity indices. The segmented areas are then compared with existing FCM, DBSCAN and K-Means methods and verified with the ground truth. The statistical analysis results also show the superiority of the new method.


2012 ◽  
Vol 1 ◽  
pp. 385-389 ◽  
Author(s):  
Arzu Erener ◽  
Sebnem Düzgün ◽  
Ahmet Cevdet Yalciner

2018 ◽  
Vol 10 (12) ◽  
pp. 1910 ◽  
Author(s):  
Joseph Spruce ◽  
John Bolten ◽  
Raghavan Srinivasan ◽  
Venkat Lakshmi

This paper discusses research methodology to develop Land Use Land Cover (LULC) maps for the Lower Mekong Basin (LMB) for basin planning, using both MODIS and Landsat satellite data. The 2010 MODIS MOD09 and MYD09 8-day reflectance data was processed into monthly NDVI maps with the Time Series Product Tool software package and then used to classify regionally common forest and agricultural LULC types. Dry season circa 2010 Landsat top of atmosphere reflectance mosaics were classified to map locally common LULC types. Unsupervised ISODATA clustering was used to derive most LULC classifications. MODIS and Landsat classifications were combined with GIS methods to derive final 250-m LULC maps for Sub-basins (SBs) 1–8 of the LMB. The SB 7 LULC map with 14 classes was assessed for accuracy. This assessment compared random locations for sampled types on the SB 7 LULC map to geospatial reference data such as Landsat RGBs, MODIS NDVI phenologic profiles, high resolution satellite data, and Mekong River Commission data (e.g., crop calendars). The SB 7 LULC map showed an overall agreement to reference data of ~81%. By grouping three deciduous forest classes into one, the overall agreement improved to ~87%. The project enabled updated regional LULC maps that included more detailed agriculture LULC types. LULC maps were supplied to project partners to improve use of Soil and Water Assessment Tool for modeling hydrology and water use, plus enhance LMB water and disaster management in a region vulnerable to flooding, droughts, and anthropogenic change as part of basin planning and assessment.


2017 ◽  
Vol 99 ◽  
pp. 234-244 ◽  
Author(s):  
Massimo Stafoggia ◽  
Joel Schwartz ◽  
Chiara Badaloni ◽  
Tom Bellander ◽  
Ester Alessandrini ◽  
...  
Keyword(s):  
Land Use ◽  

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