Roy, S and Das, A and Omkar, SN (2023) A distributed land cover classification of FP and CP SAR observation using MapReduce-based multi-layer perceptron algorithm over the Mumbai mangrove region of India. In: International Journal of Remote Sensing, 44 (5). pp. 1510-1532.
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Globally, the rapid loss of mangrove forests often creates long-lasting environmental damage alongside coastlines, mudflats, and river banks. All-weather, physical monitoring of such areas is almost impossible because of inaccessibility to swampy areas and a hostile substrate. Subsequently, conventional field surveys are relatively unavailable to monitor human encroachment in coastal areas like the Mumbai mangrove region of India. In this context, the polarimetric synthetic aperture radar (PolSAR) remote sensing tool becomes a potential candidate for mangrove conservation and management. Since the Mumbai mangrove region of India exists over the extensive land cover, the large-scale classification can articulate the continuous encroachment because of the location of human settlements or activities across this metropolitan coastal city. For this, the traditional algorithms need the essential improvisation to apply for large-scale data analysis, aiming simultaneously at getting the highest decisive and time efficiency. Here, we introduce a shallow learning model of MapReduce-based Multi-Layer Perception (MLP) algorithm to classify the hybrid compact polarimetric (CP) and fully polarimetric (FP) feature space. Even though a shallow learning model of the automated method is easily scalable, it requires a distinctive shallow learnable feature set for better land cover classification. In this effort, this paper investigates the efficacy of derived feature space compared to direct polarimetric measurements of sensors and shows that the shallow learnable feature set is more effective with both CP & FP observations. Simultaneously, the relevancy of the proposed distributed model of MLP is also justified compared to distributed extreme learning machine (DELM) algorithm and provides a practically implementable scaled-MLP algorithm of shallow learning model. Ultimately, this paper comes up with a better polarimetric signature of land types for both the CP and FP datasets, which can be used in an alternative manner as per data availability for a multi-sensor data analysis. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
| Item Type: | Journal Article |
|---|---|
| Publication: | International Journal of Remote Sensing |
| Publisher: | Taylor and Francis Ltd. |
| Additional Information: | The copyright for this article belongs to Taylor and Francis Ltd. |
| Keywords: | Classification (of information); Data handling; Learning algorithms; Learning systems; Machine learning; Polarimeters; Remote sensing; Space-based radar; Synthetic aperture radar, Feature space; Land cover; Land cover classification; Landuse - landcover; Learning models; Mangrove; Map-reduce; Multi-layer perception; Neural-networks; Polarimetric SAR, MapReduce |
| Department/Centre: | Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering) |
| Date Deposited: | 25 Apr 2023 05:11 |
| Last Modified: | 25 Apr 2023 05:11 |
| URI: | https://eprints.iisc.ac.in/id/eprint/81273 |
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