Big Remote Sensing Image Classification Based on Deep Learning Extraction Features and Distributed Spark Frameworks

Abstract

Big data analysis assumes a significant role in Earth observation using remote sensing images, since the explosion of data images from multiple sensors is used in several fields. The traditional data analysis techniques have different limitations on storing and processing massive volumes of data. Besides, big remote sensing data analytics demand sophisticated algorithms based on specific techniques to store to process the data in real-time or in near real-time with high accuracy, efficiency, and high speed. In this paper, we present a method for storing a huge number of heterogeneous satellite images based on Hadoop distributed file system (HDFS) and Apache Spark. We also present how deep learning algorithms such as VGGNet and UNet can be beneficial to big remote sensing data processing for feature extraction and classification. The obtained results prove that our approach outperforms other methods.

A Fast Large-Scale Path Planning Method on Lunar DEM Using Distributed Tile Pyramid Strategy

“…DAGs are a set of combinations of vertices and edges, where vertices are used to represent RDDs and edges are used to represent the operational relationships between RDDs. DAGs in Spark are essential for ensuring that distributed computations can perform tasks sequentially [22,31]. Research shows that the main reason Spark runs faster than MapReduce is that Spark reduces unnecessary disk IO operations by building DAGs to improve task execution efficiency; however, as MapReduce operations are independent of each other, the results produced by each MapReduce operation are written to the disk [32,33].…”

“…Compared with a single computer, distributed storage (e.g., Hadoop distributed file system (HDFS), HBase) and computational technologies (e.g., MapReduce, Spark) use the storage and computational resources of clusters and show tremendous advantages when data increases dramatically. Therefore, they are extensively used in the storage [19][20][21], calculation [22,23], segmentation [24], and path planning of massive remote sensing data. Wang et al used the MapReduce-based distributed parallel Dijkstra algorithm to solve the shortest path problem.…”

Coastline detection method of remote sensing image based on local information fuzzy clustering

“…In the process of image processing, it is necessary to analyze and understand, enhance, denoise, restore, reconstruct, compress and process the corresponding image areas, so as to obtain more important feature information in the image [3]. However, in the process of using remote sensing technology to detect the coastline, it is often affected by the ocean, sediment, reclamation and other factors, which reduces the detection accuracy [4]. Therefore, this paper designs a shoreline detection method using a fuzzy clustering algorithm of local information.…”

Hyperspectral Remote Sensing Image Classification Using Improved Metaheuristic with Deep Learning

“…The RSI datasets show colour and texture information due to their higher spatial resolution. RSIs have many more scene classes and changes than conventional RSI and become highly difficult to identify with conventional pixel-related approaches [5]. Deep learning (DL) allows the object-level classification and recognition of RSI and a better understanding of the RSIs contents at the semantic levels.…”