Automated methods of natural resource mapping with Remote Sensing Big data in Hadoop MapReduce framework

Abstract

For several decades, remote sensing (RS) tools have provided platforms for the large-scale exploration of natural resources across the planetary bodies of our solar system. In the context of Indian remote sensing, mineral resources are being explored, and mangrove resources are being monitored towards a sustainable socio-economic structure and coastal eco-system, respectively, by utilising several remote analytical techniques. However, RS technologies and the corresponding data analytics have made a vast paradigm shift, which eventually has produced “RS Big data” in our scientific world of large-scale remote analysis. Consequently, the current practices in remote sensing need a systematic improvisation of data analytics to provide a real-time, accurate and feasible remote exploration of the RS Big data. Towards this, the improvement of corresponding scientific analysis has opened up new opportunities and research perspectives for both academia and industry in remote sensing. In this favour, different automated methods are proposed in the Hadoop MapReduce framework as a part of this thesis aiming to develop both decisive and time-efficient remote analysis under the RS Big data environment. This thesis studies the remote exploration of various surface types covering the mineralogy and mangrove regions, respectively, as two significant applications in natural resource mapping. Before starting, the reliability and outreach of RS Big data analysis in the Hadoop MapReduce framework are also assessed in the laboratory environment. In this thesis, each proposed automated method is validated first in the single node analysis as a standalone process for individual RS applications. Then the corresponding MapReduce designs of the proposed methods make them scaled to conduct the distributed analysis in a pseudo-distributed Hadoop architecture for a prototype RS Big data environment in this thesis. In particular, a “per-pixel” mapping of the mineralised belt is conducted with a proposition of Extreme Learning Machine (ELM)-based scaled-ML algorithm in the Hadoop MapReduce framework by addressing the primary challenge because of impurity in the representative spectra of an observed pixel. To an extent, the same mineralogical province is explored with a proposition of a fraction cover mapping model in the Hadoop MapReduce framework by addressing the primary challenge due to the spectral variation of pure mineral spectra within an observed pixel. These mineralogical explorations on Earth utilise airborne-based hyperspectral imagery, whereas mineralogical explorations on Moon utilise spaceborne-based hyperspectral lunar imagery in this thesis. An automated mineralogical anomaly detection method identifies the prominent lunar mineral occurrences by addressing the consequences of space weathering on lunar exposures. On the other side, the spaceborne-based active remote sensing of polarimetric Earth imagery is utilised for land cover classification over the mangrove region in the Hadoop MapReduce framework. The land features of fully polarimetric (FP) and compact polarimetric (CP) observations are explored with a proposition of Active learning Multi-Layered Perceptron (AMLP) by addressing the primary challenge due to the uncertainties in class labelling. The robustness, stability, and generalisation of all proposed shallow neural networks of single hidden layered ML models are analysed for varietal informative data classification. In fact, the advancements in methodology and architecture support each other in attaining a better remote analysis with less computational automated methods in this thesis. Some of the crucial findings of this thesis are as follows: For a reliable and generalised mineral mapping, the perturbed/mixed spectra of hydrothermal minerals are required to be mapped along with the pure spectra of hydrothermal minerals. Further, the fraction cover mapping of hydrothermal minerals should address the spectral variation of pure spectra and the underlying physics of spectral mixing to get a reliable and accurate fractional contribution of minerals. In contrast to Earth mineralogy, the automated lunar mineral exploration needs to identify the potential mineralogical map of the lunar surface because of the space weathering effect. On the other hand, the underlying physics behind the polarimetric synthetic aperture radar (SAR) remote sensing plays a vital role in better discrimination of land features within the mangrove regions. The inherent data parallelism technique of the Hadoop architecture simply makes the analytical algorithm scaled and time-efficient, which can be extended for real-time Big data environments even with other MapReduce frameworks. In conclusion, even shallow learning of an automated method can provide an efficient real-time analysis of the RS Big data prototype if the physical constraints or prior physics-based insights of remote observations are undertaken. It is evident in this thesis that such consideration makes the prototype RS Big data analysis more reliable, accurate, scalable, automated and widely acceptable under varietal remote sensing environments. In summary, this thesis builds a bridge between academia and industry to provide new directional research on RS Big data analysis in making a better real-time futuristic plan for the natural resource management of any country like India.