Analysis of various change detection techniques using satellite images

Kotkar, Snehal R.; Jadhav, B. D.

doi:10.1109/infop.2015.7489466

Cited by 10 publications

(8 citation statements)

References 6 publications

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“…The average of the neighbors' membership have been calculated first. Then, norm2 of the difference between the two vectors of the average fuzzy partition have been measured according to the Equation (10).…”

Section: Change Metric Analysismentioning

confidence: 99%

“…These circumstances necessitate the development of new human-machine teaming approaches for visual imagery analysis workflows, specifically techniques that leverage state-of-the-art computer vision and machine learning. This increasing amount of geospatial big data are utilized in a key task in the earth observation, change detection (CD), which is one of the most active topics in the remote sensing area due to the persistent occurrence of catastrophes such as hurricanes, earthquakes and floods [6][7][8][9], in addition to the interactivity of human with natural phenomena [10]. The objective of CD is finding the change in the land cover utilizing co-located multitemporal remote sensed images [11].…”

Section: Introductionmentioning

confidence: 99%

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Large-Scale, Multiple Level-of-Detail Change Detection from Remote Sensing Imagery Using Deep Visual Feature Clustering

Gargees

Scott

2021

Remote Sensing

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In the era of big data, where massive amounts of remotely sensed imagery can be obtained from various satellites accompanied by the rapid change in the surface of the Earth, new techniques for large-scale change detection are necessary to facilitate timely and effective human understanding of natural and human-made phenomena. In this research, we propose a chip-based change detection method that is enabled by using deep neural networks to extract visual features. These features are transformed into deep orthogonal visual features that are then clustered based on land cover characteristics. The resulting chip cluster memberships allow arbitrary level-of-detail change analysis that can also support irregular geospatial extent based agglomerations. The proposed methods naturally support cross-resolution temporal scenes without requiring normalization of the pixel resolution across scenes and without requiring pixel-level coregistration processes. This is achieved with configurable spatial locality comparisons between years, where the aperture of a unit of measure can be a single chip, a small neighborhood of chips, or a large irregular geospatial region. The performance of our proposed method has been validated using various quantitative and statistical metrics in addition to presenting the visual geo-maps and the percentage of the change. The results show that our proposed method efficiently detected the change from a large scale area.

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Section: Change Metric Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Large-Scale, Multiple Level-of-Detail Change Detection from Remote Sensing Imagery Using Deep Visual Feature Clustering

Gargees

Scott

2021

Remote Sensing

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“…The LULC transition areas and their percentages for classified images from 2000, 2007, 2013, and 2018 were subsequently derived from the classification results using ENVI 5.3. The change detection process of classified images can be performed in several ways: image differencing, thematic change workflow, or change detection difference mapping [70][71][72]. In this paper, the thematic change workflow process was carried out to detect 2000-2007, 2007-2013, 2013-2018, and 2000-2018 land use/cover differences.…”

Section: Image Classificationmentioning

confidence: 99%

Dynamics of Shrimp Farming in the Southwestern Coastal Districts of Bangladesh Using a Shrimp Yield Dataset (SYD) and Landsat Satellite Archives

Karim

Zhang

2019

Sustainability

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The shrimp-farming area and shrimp yield are continuously changing in the southwestern coastal districts of Bangladesh. The three southwestern coastal districts, Bagerhat, Satkhira, and Khulna, along with Rampal, a subdistrict of Bagerhat, contribute 75% of the total shrimp yield of Bangladesh. However, the shrimp yield and farming area have declined in Bagerhat district, and the cause of this decline is uncertain. In this research, the differences in the shrimp yield were quantified using a shrimp yield dataset (SYD) and k-means classification. A supervised image classification approach was applied to quantify the spatiotemporal changes and identify the influencing factors behind the declining shrimp-farming area and yield in Rampal, Bagerhat district, using Landsat satellite archives. K-means classification reveals that, between 2015 and 2017, the shrimp yield in Bagerhat district declined significantly compared to Satkhira and Khulna. The satellite-based monitoring results affirm that the shrimp-farming area of Rampal also decreased rapidly, from 21.82% in 2013 to 6.52% in 2018. This research estimates that approximately 70% of the shrimp-farming area was lost in Rampal since December 2013. Hence, the findings of this research might motivate the responsible bodies to declare the shrimp-farming coastal area as a "shrimp zone" and implement an active policy to protect the vulnerable shrimp-farming industry and shrimp farmers, considering it is the second-largest export earning source in Bangladesh after ready-made garments.

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“…Change detection is defined as identifying differences in the state of an object or phenomena by observing it at different times [1]. Change detection framework utilizes multi time varying datasets to analyze time dependent event and determine changes which are required in current system [2]. The remote sensing data becomes major source for change detection studies because of its accuracy in determining stabilized point in given time series [3].…”

Section: Introductionmentioning

confidence: 99%

“…Digital format which suits for computation, synoptic view and vast selection of space dependent and spectral resolution is great application supported by remote sensing application. [2]The general objectives of change detection in remote sensing includes determine location over wide area along with distinct changes and then determining amount of changes in particular location. Also accuracy of result is analyzed produced through change detection using remote sensing.…”

Section: Introductionmentioning

confidence: 99%

Review of Techniques Used For Change Detection in Remotely Sensed Images

khullar

Marwaha²

2017

IJECS

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The propensity towards up to date information about the earth's surface is ever increasing. The information generated through the satellites is used in wide range of applications such as land monitoring, whether forecasting, resource monitoring and environment monitoring. Legion of techniques are developed to detect change by utilizing the applications of remote sense data and new techniques through research are still emerging. This paper begins by first of all discussing slandered pixel based techniques and statistics based techniques for change detection. This is preceded by describing object oriented techniques. Data mining approaches for change detection is comprehensively described. The relative merits and demerits of each technique is described to determine optimal methodology to be used in future research.

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Analysis of various change detection techniques using satellite images

Cited by 10 publications

References 6 publications

Large-Scale, Multiple Level-of-Detail Change Detection from Remote Sensing Imagery Using Deep Visual Feature Clustering

Large-Scale, Multiple Level-of-Detail Change Detection from Remote Sensing Imagery Using Deep Visual Feature Clustering

Dynamics of Shrimp Farming in the Southwestern Coastal Districts of Bangladesh Using a Shrimp Yield Dataset (SYD) and Landsat Satellite Archives

Review of Techniques Used For Change Detection in Remotely Sensed Images

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