Remote Sensing Image Classification: A Comprehensive Review and Applications

Abstract

Remote sensing is mainly used to investigate sites of dams, bridges, and pipelines to locate construction materials and provide detailed geographic information. In remote sensing image analysis, the images captured through satellite and drones are used to observe surface of the Earth. The main aim of any image classification-based system is to assign semantic labels to captured images, and consequently, using these labels, images can be arranged in a semantic order. The semantic arrangement of images is used in various domains of digital image processing and computer vision such as remote sensing, image retrieval, object recognition, image annotation, scene analysis, content-based image analysis, and video analysis. The earlier approaches for remote sensing image analysis are based on low-level and mid-level feature extraction and representation. These techniques have shown good performance by using different feature combinations and machine learning approaches. These earlier approaches have used small-scale image dataset. The recent trends for remote sensing image analysis are shifted to the use of deep learning model. Various hybrid approaches of deep learning have shown much better results than the use of a single deep learning model. In this review article, a detailed overview of the past trends is presented, based on low-level and mid-level feature representation using traditional machine learning concepts. A summary of publicly available image benchmarks for remote sensing image analysis is also presented. A detailed summary is presented at the end of each section. An overview regarding the current trends of deep learning models is presented along with a detailed comparison of various hybrid approaches based on recent trends. The performance evaluation metrics are also discussed. This review article provides a detailed knowledge related to the existing trends in remote sensing image classification and possible future research directions.

Increasing The Taxonomic Accuracy of Remote Sensing Data Using Traditional Pattern Recognition Methods

“…Since in all types of undirected classification, the identification of the varieties is not determined until after the classification process, that is, the process of interpreting the results comes after the (Possvier Classification) stage original study areas. Table (1) Euclidean distance between alternating pairs of classes of K-means algorithm.…”

CNN-Based Automatic Helmet Violation Detection of Motorcyclists for an Intelligent Transportation System

“…Computer vision and digital image processing are used in various applied domains such as remote sensing, pose detection, decision making, path detection, defect detection, and automatic driving [21][22][23][24][25][26]. e recent focus of research in this field is the use of deep learning models that have shown good results in various applied domains [27][28][29].…”

A Hybrid CUDA, OpenMP, and MPI parallel TCA-based Domain Adaptation for Classification of Very High-Resolution Remote Sensing Images

“…where X S corresponds with the data of the first distribution (source), X T with the data of the other one (target), N s and N t are the number of samples from the source and the target domains, respectively, H is a universal RKHS [41], and φ : X → H is a nonlinear mapping function that can be found minimizing the distance as defined by (1). Using the kernel trick (i.e., K(x i , x j ) = φ(x i ) ′ φ(x j )), [42] proposed convert this problem into a kernel learning problem, where (1) can be rewritten as:…”

“…The classification of images is one of the most common processes in the Remote Sensing (RS) field [1][2][3]. During this process, each of the pixels that comprises the image is classified, that is, it is assigned to one of the previously defined class labels.…”