Abstract
Remote sensing streams continuous data feed from the satellite to ground station for data analysis. Often the data analytics involves analyzing data in real-time, such as emergency control, surveillance of military operations or scenarios that change rapidly. Traditional data mining requires all the data to be available prior to inducing a model by supervised learning, for automatic image recognition or classification. Any new update on the data prompts the model to be built again by loading in all the previous and new data. Therefore, the training time will increase indefinitely making it unsuitable for real-time application in remote sensing. As a contribution to solving this problem, a new approach of data analytics for remote sensing for data stream mining is formulated and reported in this paper. Fresh data feed collected from afar is used to approximate an image recognition model without reloading the history, which helps eliminate the latency in building the model again and again. In the past, data stream mining has a drawback in approximating a classification model with a sufficiently high level of accuracy. This is due to the one-pass incremental learning mechanism inherently exists in the design of the data stream mining algorithm. In order to solve this problem, a novel streamlined sensor data processing method is proposed called evolutionary expand-and-contract instance-based learning algorithm (EEAC-IBL). The multivariate data stream is first expanded into many subspaces, and then the subspaces, which are corresponding to the characteristics of the features are selected and condensed into a significant feature subset. The selection operates stochastically instead of deterministically by evolutionary optimization, which approximates the best subgroup. Followed by data stream mining, the model learning for image recognition is done on the fly. This stochastic approximation method is fast and accurate, offering an alternative to the traditional machine learning method for image recognition application in remote sensing. Our experimental results show computing advantages over other classical approaches, with a mean accuracy improvement at 16.62%.
Highlights
Remote sensing (RS) is considered as a process of acquiring information about a specific target, a region, or some event through analyzing the data collected by a probe that is free of contact with the target, region, or event for investigation purpose [1]
Based on multiple instance learning [15], EEAC-IBL is a is a simple approach in converting the attributes values pertaining to a specific class of simple approach in converting the attributes values pertaining to a specific class of landtype into subspaces to be added into training data
Accuracy and Kappa concerns about how sharp and useful the incremental classifier is, in machine learning for RS image recognition; the memory and time costs are associated with the applicability of the model pertaining to hardware embedding implementation where memory size is desired to be kept as compact as possible, and the data analysis speed in terms of time consumption should be fast enough especially for real-time applications
Summary
Remote sensing (RS) is considered as a process of acquiring information about a specific target, a region, or some event through analyzing the data collected by a probe that is free of contact with the target, region, or event for investigation purpose [1]. Additional information may be added, depending on the model of RS satellite and the interpretation mechanism used [6] For relieving this latency bottleneck in RS where a massive amount of data are to be transmitted and processed, the authors proposed to use data stream mining together with a novel preprocessing mechanism that is designed to speed up the machine learning latency. Followed by data stream mining, the model learning for image recognition is done on the fly [9] This stochastic approximation method is fast and accurate, offering an alternative to the traditional machine learning method for image recognition application in remote sensing. Some flexibility is made possible, especially for data whose characteristics have some grey-areas or overlaps Thereby this approach is inherently suitable for learning from data for pattern recognition, computer vision, and image classification, which are fuzzy and noisy in nature. EEAC-IBL is useful for pattern recognition such as satellite RS, and other image recognition domains where accuracy, speed, and memory constraints are of a concern
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