Comparative  Analysis  of  Scene  Classification  Methods  for Remotely Sensed Images using Various Convolutional Neural Network

L.R. Sudha,P. Deepan

doi:10.4108/eai.11-2-2021.168714

Abstract

Remote sensing image (RSI) scene classification has received growing attention from the research community in recent days. Over the past few decades, with the rapid development of deep learning models particularly Convolutional Neural Network (CNN), the performance o

Highlights

With the rapid development of earth observation technology, image scene classification plays a significant role in the field of Remote sensing image (RSI)
The histogram of gradients (HoG), color histogram (CH), gray level cooccurrence matrix (GLCM), local binary pattern (LBP) and scale in-variant feature transform (SIFT) are some of the familiar handcraft feature extraction methods that are used for image scene classification [5,6]
The remainder of the paper is structured as follows: Section “Related works” contains the literature survey of Convolutional Neural Network (CNN) classification for remote sensing images; Section “Proposed work” presents the newly developed different CNN models such as dilated convolutional neural network, RESISC-16 model and feature fusion of CNN and VGG-16; Section “Experimental result and analysis” discusses how the performance is improved from traditional CNN to new proposed convolutional neural network models; and in Section “Conclusion” we reiterate the focus of the paper and summarize the work presented

Summary

Introduction

With the rapid development of earth observation technology, image scene classification plays a significant role in the field of RSI. The histogram of gradients (HoG), color histogram (CH), gray level cooccurrence matrix (GLCM), local binary pattern (LBP) and scale in-variant feature transform (SIFT) are some of the familiar handcraft feature extraction methods that are used for image scene classification [5,6] These low level features are producing better results, but they require domain expertise and consume more time for the limited data. The main reasons for the popularity of deep learning are the highly improved parallel processing capability of hardware especially the general-purpose graphical processing units (GPUs), the substantially increased size of data available for training, and the recent improvements in machine learning algorithms These advancements enable deep learning methods to effectively utilize complex data, compositional nonlinear functions, learn distributed and hierarchical features automatically by utilizing both labeled and unlabeled data effectively. The remainder of the paper is structured as follows: Section “Related works” contains the literature survey of CNN classification for remote sensing images; Section “Proposed work” presents the newly developed different CNN models such as dilated convolutional neural network, RESISC-16 model and feature fusion of CNN and VGG-16; Section “Experimental result and analysis” discusses how the performance is improved from traditional CNN to new proposed convolutional neural network models; and in Section “Conclusion” we reiterate the focus of the paper and summarize the work presented

Related Works

Proposed Work

Dilated Convolutional Neural Network

Fine tuning the hyper parameter of RSISC-16 Model

Feature Fusion of CNN and RSISC-16 Model

Experimental Analysis

Dataset Description

Performance Metrics

Experimental Results of different Scene Classification Methods