Deep Learning-Based Classification Methods for Remote Sensing Images in Urban Built-Up Areas

Abstract

Urban areas have been focused recently on the remote sensing applications since their function closely relates to the distribution of built-up areas, where reflectivity or scattering characteristics are the same or similar. Traditional pixel-based methods cannot discriminate the types of urban built-up areas very well. This paper investigates a deep learning-based classification method for remote sensing images, particularly for high spatial resolution remote sensing (HSRRS) images with various changes and multiscene classes. Specifically, to help develop the corresponding classification methods in urban built-up areas, we consider four deep neural networks (DNNs): 1) convolutional neural network (CNN); 2) capsule networks (CapsNet); 3) same model with a different training rounding based on CNN (SMDTR-CNN); and 4) same model with different training rounding based on CapsNet (SMDTR-CapsNet). The performances of the proposed methods are evaluated in terms of overall accuracy, kappa coefficient, precision, and confusion matrix. The results revealed that SMDTR-CNN obtained the best overall accuracy (95.0%) and kappa coefficient (0.944) while also improving the precision of parking lot and resident samples by 1% and 4%, respectively.INDEX TERMS Deep learning, convolution neural network, urban built-up area, capsule network, model ensemble, high resolution remote sensing classification.

A New Remote Sensing Images and Point-of-Interest Fused (RPF) Model for Sensing Urban Functional Regions

“…In recent years, deep neural network has shown excellent performance in the task of remote sensing image classification and segmentation [16,[39][40][41]. Feature maps output from different layers in neural network reflect different characteristics of remote sensing images.…”

“…Kaliraj [15] researched the Kanyakumari coast in India and exploited Maximum Likelihood Classifier (MLC) algorithm from Landsat ETM+ and TM images to analyze the land use and land cover characteristics. LI [16] investigated a neural network based on deep learning to classify the high spatial resolution remote sensing (HSRRS) images. From the above studies, remote sensing technology can effectively extract the spatial and geographic characteristics of urban functional areas in remote sensing maps [17], which is considered to be the key technology to study the functional alteration of urban land use [18][19][20][21].…”

High Spatial Resolution Remote Sensing Scene Classification Method using Transfer Learning and Deep Convolutional Neural Network

“…HSRRS image scene classification problem can be extracted subregions into different semantic classes, and it is a fundamental task and significant for remote sensing applications, such as urban planning, object detection, and natural resource management. Many recent works have demonstrated that CNN is the most successful and widely applied deep learning method, and has been used to make HSRRS image scene classification task [18]. Especially, the DeCNN performs well in semantic features extraction with a lot of convolutional layers and a large amount of training data set.…”

“…Recently, deep learning has represented the state of the art in a variety of domains, and CNN as a typical deep learning method, has obtained excellent results in the field of computer vision [22], wireless communications [23], [24] and remote sensing image processing [18]. HSRRS image scene classification based on CNN has achieved excellent results recently.…”

“…Penatti et al evaluated the generalization power of CNN features from fully-connected layers and obtained a state of the art result with a public HSRRS image data sets [25]. Feature fusion strategies to integrate the multilayers features to CNNs for HSRRS image scene classification have been proposed to complete the classification tasks [18], [26]- [29]. Gong et al proposed a deep structural metric based learning approach for HSRRS image scene classification [30].…”