A Meta-Methodology for Improving Land Cover and Land Use Classification with SAR Imagery

Land cover is the most critical information required for land management and planning because human interference on land can be easily detected through it. However, mapping land cover utilizing optical remote sensing is not easy due to the acute shortage of cloud-free images. Google Earth Engine (GEE) is an efficient and effective tool for huge land cover analysis by providing access to large volumes of imagery available within a few days after acquisition in one consolidated system. This article demonstrates the use of Sentinel-1 datasets to create a land cover map of Pusad, Maharashtra using the GEE platform. Sentinel-1 provides Synthetic Aperture Radar (SAR) datasets that have a temporally dense and high spatial resolution, which is renowned for its cloud penetration characteristics and round-the-year observations irrespective of the weather. VV and VH polarization sentinel-1 time series data were automatically classified using a support vector machine (SVM) and Random Forest (RF) machine learning algorithms. Overall accuracies (OA), ranging from 82.3% to 90%, were obtained depending on polarization and methodology used. RF algorithm with VV polarization dataset stands better in comparison to SVM achieving OA of 90% and Kappa coefficient of 0.86. The highest user accuracy was obtained for the water class with both classifiers.

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“…In [20] authors have proposed the SAR LC classification approach which is based on region based classification to boost classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

Automatic land cover classification with SAR imagery and Machine learning using Google Earth Engine

Desai

Gaikwad

2022

Int. j. electr. comput. eng. syst. (Online)

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“…Dividing SAR images into these meaningful areas helps to understand the image from a high level and is convenient for further processing and analysis. However, due to the special imaging mechanism, the SAR image itself contains many speckle noises [5]. This multiplicative noise makes the processing of SAR images very challenging.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation for SAR Image Based on Texture Complexity Analysis and Key Superpixels

et al. 2020

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In recent years, regional algorithms have shown great potential in the field of synthetic aperture radar (SAR) image segmentation. However, SAR images have a variety of landforms and a landform with complex texture is difficult to be divided as a whole. Due to speckle noise, traditional over-segmentation algorithm may cause mixed superpixels with different labels. They are usually located adjacent to two areas or contain more noise. In this paper, a new semantic segmentation method of SAR images based on texture complexity analysis and key superpixels is proposed. Texture complexity analysis is performed and on this basis, mixed superpixels are selected as key superpixels. Specifically, the texture complexity of the input image is calculated by a new method. Then a new superpixels generation method called neighbourhood information simple linear iterative clustering (NISLIC) is used to over-segment the image. For images with high texture complexity, the complex areas are first separated and key superpixels are selected according to certain rules. For images with low texture complexity, key superpixels are directly extracted. Finally, the superpixels are pre-segmented by fuzzy clustering based on the extracted features and the key superpixels are processed at the pixel level to obtain the final result. The effectiveness of this method has been successfully verified on several kinds of images. Comparing with the state-of-the-art algorithms, the proposed algorithm can more effectively distinguish different landforms and suppress the influence of noise, so as to achieve semantic segmentation of SAR images.

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Geological and terrain attributes for predicting soil classes using pixel- and geographic object-based image analysis in the Brazilian Cerrado

et al. 2021

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A Meta-Methodology for Improving Land Cover and Land Use Classification with SAR Imagery

Cited by 6 publications

References 18 publications

Automatic land cover classification with SAR imagery and Machine learning using Google Earth Engine

Automatic land cover classification with SAR imagery and Machine learning using Google Earth Engine

Semantic Segmentation for SAR Image Based on Texture Complexity Analysis and Key Superpixels

Geological and terrain attributes for predicting soil classes using pixel- and geographic object-based image analysis in the Brazilian Cerrado

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