scholarly journals Change Detection Based on Multi-Feature Clustering Using Differential Evolution for Landsat Imagery

2018 ◽  
Vol 10 (10) ◽  
pp. 1664 ◽  
Author(s):  
Mi Song ◽  
Yanfei Zhong ◽  
Ailong Ma
Keyword(s):  
Land Cover ◽  
Differential Evolution ◽  
Change Detection ◽  
Landsat Imagery ◽  
Feature Space ◽  
Landsat Data ◽  
Landsat Images ◽  
Feature Clustering ◽  
De Algorithm ◽  
Natural Land

Change detection (CD) of natural land cover is important for environmental protection and to maintain an ecological balance. The Landsat series of satellites provide continuous observation of the Earth’s surface and is sensitive to reflection of water, soil and vegetation. It offers fine spatial resolutions (15–80 m) and short revisit times (16–18 days). Therefore, Landsat imagery is suitable for monitoring natural land cover changes. Clustering-based CD methods using evolutionary algorithms (EAs) can be applied to Landsat images to obtain optimal changed and unchanged clustering centers (clusters) with minimum clustering index. However, they directly analyze difference image (DI), which finds itself subject to interference by Gaussian noise and local brightness distortion in Landsat data, resulting in false alarms in detection results. In order to reduce image interferences and improve CD accuracy, we proposed an unsupervised CD method based on multi-feature clustering using the differential evolution algorithm (M-DECD) for Landsat Imagery. First, according to characteristics of Landsat data, a multi-feature space is constructed with three elements: Wiener de-noising, detail enhancement, and structural similarity. Then, a CD method based on differential evolution (DE) algorithm and fuzzy clustering is proposed to obtain global optimal clusters in the multi-feature space, and generate a binary change map (CM). In addition, the control parameters of the DE algorithm are adjusted to improve the robustness of M-DECD. The experimental results obtained with four Landsat datasets confirm the effectiveness of M-DECD. Compared with the results of conventional methods and the current state-of-the-art methods based on evolutionary clustering, the detection accuracies of the M-DECD on the Mexico dataset and the Sardinia dataset are very close to the best results. The accuracies of the M-DECD in the Alaska dataset and the large Canada dataset increased by about 3.3% and 11.9%, respectively. This indicates that multiple features are suitable for Landsat images and the DE algorithm is effective in searching for an optimal CD result.

Persistent Homology for Land Cover Change Detection

2021 ◽  
Author(s):  
Djamel Bouchaffra ◽  
Faycal Ykhlef
Keyword(s):  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Vegetation Index ◽  
Homology Theory ◽  
Urban Morphology ◽  
Detection Methods ◽  
Landsat Images ◽  
Data Repositories ◽  
Land Cover Change Detection

The need for environmental protection, monitoring, and security is increasing, and land cover change detection (LCCD) can aid in the valuation of burned areas, the study of shifting cultivation, the monitoring of pollution, the assessment of deforestation, and the analysis of desertification, urban growth, and climate change. Because of the imminent need and the availability of data repositories, numerous mathematical models have been devised for change detection. Given a sample of remotely sensed images from the same region acquired at different dates, the models investigate if a region has undergone change. Even if there is no substantial advantage to using pixel-based classification over object-based classification, a pixel-based change detection approach is often adopted. A pixel can encompass a large region, and it is imperative to determine whether this pixel (input) has changed or not. A changed image is compared to the available ground truth image for pixel-based performance evaluation. Some existing change detection systems do not take into account reversible changes due to seasonal weather effects. In other words, when snow falls in a region, the land cover is not considered as a change because it is seasonal (reversible). Some approaches exploit time series of Landsat images, which are based on the Normalized Difference Vegetation Index technique. Others evaluate built-up expansion to assess urban morphology changes using an unsupervised approach that relies on labels clustering. Change detection methods have also been applied to the field of disaster management using object-oriented image classification. Some methodologies are based on spectral mixture analysis. Other techniques invoke a similarity measure based on the evolution of the local statistics of the image between two dates for vegetation LCCD. Probabilistic approaches based on maximum entropy have been applied to vegetation and forest areas, such as Hustai National Park in Mongolia. Researchers in this field have proposed an LCCD scheme based on a feed-forward neural network using backpropagation for training. This paper invokes the new concept of homology theory, a subfield of algebraic topology. Homology theory is incorporated within a Structural Hidden Markov Model.

scholarly journals Change Detection Analysis Using Surveying and Geoinformatics Techniques

2018 ◽  
Vol 2 (1) ◽  
pp. 28-38
Author(s):  
I. C. Onuigbo ◽  
J. Y. Jwat
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Research Work ◽  
Temporal Data ◽  
Landsat Images ◽  
Detection Analysis ◽  
Ground Truthing ◽  
Landsat Satellite ◽  
Geoinformatics Techniques

The study was on change detection using Surveying and Geoinformatics techniques. For effective research study, Landsat satellite images and Quickbird imagery of Minna were acquired for three periods, 2000, 2005 and 2012. The research work demonstrated the possibility of using Surveying and Geoinformatics in capturing spatial-temporal data. The result of the research work shows a rapid growth in built-up land between 2000 and 2005, while the periods between 2005 and 2012 witnessed a reduction in this class. It was also observed that change by 2020 may likely follow the trend in 2005 – 2012 all things being equal. Built up area may increase to 11026.456 hectares, which represent 11% change. The study has shown clearly the extent to which MSS imagery and Landsat images together with extensive ground- truthing can provide information necessary for land use and land cover mapping. Attempt was made to capture as accurate as possible four land use and land cover classes as they change through time.

scholarly journals IMPACT ANALYSIS OF URBANIZATION ON LAND USE LAND COVER CHANGE FOR KHULNA CITY, BANGLADESH USING TEMPORAL LANDSAT IMAGERY

2018 ◽  
Vol XLII-5 ◽  
pp. 757-760 ◽  
Author(s):  
M. Moniruzzam ◽  
A. Roy ◽  
C. M. Bhatt ◽  
A. Gupta ◽  
N. T. T. An ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Impact Analysis ◽  
Landsat Imagery ◽  
Training Dataset ◽  
Anthropogenic Factors ◽  
Vacant Land ◽  
Landsat Data ◽  
Land Use Land Cover ◽  
Decadal Scale

<p><strong>Abstract.</strong> Urbanization has given a massive pace in Land Use Land Cover (LULC) changes in rapidly growing cities like Khulna, i.e. the third largest city of Bangladesh. Such impacting changes have taken place in over-decadal scale. It is important because detailed analysis with regularly monitoring will be fruitful to drag the attention of decision maker and urban planner for sustainable development and to overcome the problem of urban sprawl. In this present study, changes in LULC as an impact of urbanization, have been investigated for years 1997, 2002, 2007, 2012 and 2017; using three generation of Landsat data in geographic information system (GIS) domain which has the height competence in recent time. Initially, LULC have categorised into Built-up, Vegetation, Vacant Land, and Waterbody with the help of supervised classification technique. Field work had been carried out for acquiring training dataset and validation. The accuracy has been achieved more than 85% for the changes assessed. Analysis has an outlet with increase in built-up area by 27.92% in year 1997 to 2017 and continued respectively in each successive interval of half a decade at the given years. On the other side waterbody and vacant land decreased correspondingly. Bound to mention, instead to having largest temporal durability, the moderate spatial resolution of Landsat data has a limitation for such urban studies. These changes are responsible by both of natural or anthropogenic factors. Such study will provide a better way out of optimization of land-use to prepare detail area plan (DAP) of Khulna City Corporation (KCC) and Khulna development authority (KDA).</p>

scholarly journals Fusion of Change Vector Analysis in Posterior Probability Space and Postclassification Comparison for Change Detection from Multispectral Remote Sensing Data

2019 ◽  
Vol 11 (13) ◽  
pp. 1511 ◽  
Author(s):  
Fatemeh Zakeri ◽  
Bo Huang ◽  
Mohammad Reza Saradjian
Keyword(s):  
Land Cover ◽  
Change Detection ◽  
Probability Space ◽  
Posterior Probability ◽  
Feature Space ◽  
Change Vector Analysis ◽  
Different Types ◽  
Change Vector ◽  
Types Of Change ◽  
Change Map

Postclassification Comparison (PCC) has been widely used as a change-detection method. The PCC algorithm is straightforward and easily applicable to all satellite images, regardless of whether they are acquired from the same sensor or in the same environmental conditions. However, PCC is prone to cumulative error, which results from classification errors. Alternatively, Change Vector Analysis in Posterior Probability Space (CVAPS), which interprets change based on comparing the posterior probability vectors of a pixel, can alleviate the classification error accumulation present in PCC. CVAPS identifies the type of change based on the direction of a change vector. However, a change vector can be translated to a new position within the feature space; consequently, it is not inconceivable that identical measures of direction may be used by CVAPS to describe multiple types of change. Our proposed method identifies land-cover transitions by using a fusion of CVAPS and PCC. In the proposed algorithm, contrary to CVAPS, a threshold does not need to be specified in order to extract change. Moreover, the proposed method uses a Random Forest as a trainable fusion method in order to obtain a change map directly in a feature space which is obtained from CVAPS and PCC. In other words, there is no need to specify a threshold to obtain a change map through the CVAPS method and then combine it with the change map obtained from the PCC method. This is an advantage over other change-detection methods focused on fusing multiple change-detection approaches. In addition, the proposed method identifies different types of land-cover transitions, based on the fusion of CVAPS and PCC, to improve the results of change-type determination. The proposed method is applied to images acquired by Landsat and Quickbird. The resultant maps confirm the utility of the proposed method as a change-detection/labeling tool. For example, the new method has an overall accuracy and a kappa coefficient relative improvement of 7% and 9%, respectively, on average, over CVAPS and PCC in determining different types of change.

scholarly journals MONITORING KEANEKARAGAMAN JENIS BURUNG PADA BERBAGAI TUTUPAN LAHAN DI CIBINONG SCIENCE CENTER (CSC), JAWA BARAT

2020 ◽  
Vol 19 (3B) ◽  
Author(s):  
Yohanna Dalimunthe
Keyword(s):  
Land Cover ◽  
Urban Landscape ◽  
Landsat Imagery ◽  
Community Response ◽  
Paddy Fields ◽  
Land Cover Changes ◽  
Urban Landscapes ◽  
Science Center ◽  
Landsat Images ◽  
Point Count

Birds play an essential role in ecosystems, especially in urban landscapes such as the Cibinong Science Center (CSC). As an urban landscape, CSC always experiences land cover changes due to the development of research infrastructure resulting in various human-made land cover types. This study aims to determine the diversity of birds in various types of land cover as a community response to CSC development dynamics. Bird data was collected using the point count method modified with a grid (plot) measuring 200mx200m with a radius of observation as far as 50m at 34 points. Landsat images were analyzed from 2006 to 2018 to see changes in land cover changes. The observations show that there are 35 species of birds. Among those, three birds are protected by Indonesian regulation. There are six type of land cover in CSC paddy fields, buildings, opened area, farm land, plantation, and water. CSC has total diversity S(mean) = 32 covering 65% of all recorded birds. At land cover level, building area show the highest diversity (Shannon=2.03) while paddy fields is the lowest (Shannon=1.45). Based on the Landsat imagery, there are several changes in vegetation and the addition of some buildings.   

Sign in / Sign up

Export Citation Format

Share Document