SAR image segmentation for land cover change detection

Das, Ananya; Sahi, Abhishek; Nandini, Durgesh

doi:10.1109/get.2016.7916810

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…Change detection is usually based on a ratio operation, which is sensitive to calibration and radiometric errors, in addition to the speckle effect in SAR data. Although several authors have suggested the adoption of the logarithmic ratio operator [29] to minimize the speckle effect in detecting changes, Zhuang et al [30] pointed out that there is no gain in relation to the use of the simple ratio image. The multitemporal Coefficient of Variation (CV) is an approach presented as being advantageous for detecting changes due to its simple formulation and notable statistical properties [31].…”

Section: Introductionmentioning

confidence: 99%

Change Detection of Selective Logging in the Brazilian Amazon Using X-Band SAR Data and Pre-Trained Convolutional Neural Networks

Kuck

Filho²,

Sano

et al. 2021

Remote Sensing

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It is estimated that, in the Brazilian Amazon, forest degradation contributes three times more than deforestation for the loss of gross above-ground biomass. Degradation, in particular those caused by selective logging, result in features whose detection is a challenge to remote sensing, due to its size, space configuration, and geographical distribution. From the available remote sensing technologies, SAR data allow monitoring even during adverse atmospheric conditions. The aim of this study was to test different pre-trained models of Convolutional Neural Networks (CNNs) for change detection associated with forest degradation in bitemporal products obtained from a pair of SAR COSMO-SkyMed images acquired before and after logging in the Jamari National Forest. This area contains areas of legal and illegal logging, and to test the influence of the speckle effect on the result of this classification by applying the classification methodology on previously filtered and unfiltered images, comparing the results. A method of cluster detections was also presented, based on density-based spatial clustering of applications with noise (DBSCAN), which would make it possible, for example, to guide inspection actions and allow the calculation of the intensity of exploitation (IEX). Although the differences between the tested models were in the order of less than 5%, the tests on the RGB composition (where R = coefficient of variation; G = minimum values; and B = gradient) presented a slightly better performance compared to the others in terms of the number of correct classifications for selective logging, in particular using the model Painters (accuracy = 92%) even in the generalization tests, which presented an overall accuracy of 87%, and in the test on RGB from the unfiltered image pair (accuracy of 90%). These results indicate that multitemporal X-band SAR data have the potential for monitoring selective logging in tropical forests, especially in combination with CNN techniques.

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Section: Introductionmentioning

confidence: 99%

Change Detection of Selective Logging in the Brazilian Amazon Using X-Band SAR Data and Pre-Trained Convolutional Neural Networks

Kuck

Filho²,

Sano

et al. 2021

Remote Sensing

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“…Moreover, speckle noise in SAR images may affect the performance. Minimisation of the effect of speckle noise can be performed based on a log ratio operator (Das et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Another method used log ratio and mean ratio to obtain a difference image. The authors used k-means clustering for segmentation (Das et al 2016). A improved fuzzy c-means algorithm was used to classify the difference map to generate the resulting change detection map (Ronghua Shang et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Registration-based change detection for SAR images

Gharbia

Amin

Mousa

et al. 2020

NRIAG Journal of Astronomy and Geophysics

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This paper presents an efficient change detection approach for Synthetic Aperture Radar (SAR) images. The basic idea of this approach is to use the log ratio of the two images for change detection after being registered with Scale-Invariant Feature Transform (SIFT). These two images are a reference image and another image for the same area acquired at a different time. The log ratio variations include changes in certain areas corresponding to the natural changes in the test image. Usually, SAR images contain some sort of noise. So, there is a need for a denoising process prior to estimating the log ratio to enhance the change detection results. A segmentation process is performed on the test image based on the log ratio values. Large values in the log ratio image correspond to detected changes in the test image. Simulation results on SAR images for a region of Jeddah demonstrate the success of the proposed approach.

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“…The process of initializing the image as input and getting an output as an attribute or the portion of an image is called Image Processing. The image processing techniques are used in the areas like image sharpening, color processing, pattern recognition, encoding, medical, and so on [2]. The image processing techniques are also used in high data processing and high-performance applications like face detection, face recognition etc.…”

Section: Introductionmentioning

confidence: 99%

A Study of Retrieval Methods of Multi-Dimensional Images in Different Domains

Garg¹

2018

ijacsa

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Multiple amount of multi-dimensional images are designed and most of them are available on internet at free of cost. The 3D images include three characteristics namely width, height, and depth. The images which are created as 3D can describe the geometry in terms of 3D coordinates. These coordinates help to obtain the object from the image much easier and accurate. In this paper, we presented a review about the Multi-dimensional image retrieval. Multi-dimensional image retrieval is a process of extracting the relevant 2D or 3D images from the huge database. To perform image retrieval process on large database, several methods like text based, Content based, Annotation based, semantic based, and sketch based were used. The image retrieval techniques are mostly used in the fields like Digital library, medical, forensic science, and so on. A systematic literature review has been shown for image retrieval methods reported on 2010 to 2017. The aim of this article is to show the various concept and efforts of different authors on image retrieval technique.

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SAR image segmentation for land cover change detection

Cited by 7 publications

References 22 publications

Change Detection of Selective Logging in the Brazilian Amazon Using X-Band SAR Data and Pre-Trained Convolutional Neural Networks

Change Detection of Selective Logging in the Brazilian Amazon Using X-Band SAR Data and Pre-Trained Convolutional Neural Networks

Registration-based change detection for SAR images

A Study of Retrieval Methods of Multi-Dimensional Images in Different Domains

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