CHANGE DETECTION OF BUILDINGS WITH THE UTILIZATION OF A DEEP BELIEF NETWORK AND HIGH-RESOLUTION REMOTE SENSING IMAGES

Fenghua Huang, Guiping Shen, Huiqun Hong and Liying Wei
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Fenghua Huang: Fujian Key Laboratory of Spatial Information Perception and Intelligent Processing, Yango University, Fuzhou 350015, P. R. China†Fujian University Engineering Research Center of Spatial Data Mining and Application, Yango University, Fuzhou 350015, P. R. China
Guiping Shen: Fujian Key Laboratory of Spatial Information Perception and Intelligent Processing, Yango University, Fuzhou 350015, P. R. China†Fujian University Engineering Research Center of Spatial Data Mining and Application, Yango University, Fuzhou 350015, P. R. China
Huiqun Hong: Fujian Key Laboratory of Spatial Information Perception and Intelligent Processing, Yango University, Fuzhou 350015, P. R. China†Fujian University Engineering Research Center of Spatial Data Mining and Application, Yango University, Fuzhou 350015, P. R. China
Liying Wei: ��Guangzexian Hongxiang Intelligent Technology Co., Ltd., Guangze County, Nanping 354199, P. R. China

FRACTALS (fractals), 2022, vol. 30, issue 10, 1-16

Abstract: To enhance the accuracy results of the change detection of buildings utilizing high-resolution remote sensing (HRRS) images, a novel method was proposed by combining both tensor and deep belief network (DBN). To optimize the description of the essential characteristics for changes in buildings, a tensor-based structure covering time-space-spectrum-shadow features integrated into the model (TSSS-Cube) is proposed. The changes occurring as a combination of shadow and spectral features and spatio-temporal autocorrelation at each pixel are represented by a third-order tensor to maintain the structural information and the constraint integrity between them. Then, a restricted Boltzmann machine (TC-RBM) that can be directly used to process TSSS-Cube data is designed, and the support tensor machine (STM) is used to replace the conventional backpropagation neural network at the top of the DBN to construct a multi-tensor deep belief network (MTR-DBN) composing of multi-layer TC-RBMs and an STM classifier. Finally, the multi-layer TC-RBMs in MTR-DBN are trained layer by layer, and the global parameters of the MTR-DBN are optimized by combining a limited number of labeled data and fine-tuning the STM classifier. The implementation of both supervised and unsupervised learning methods comprehensively provides advantages to increase the accuracy result of the MTR-DBN network to detect changes. Three representative different sub-regions are selected from the whole original experimental area respectively for building change detection experiments, and a dataset composed of double-temporal HRRS images in 2012 and 2016 is used as the related experimental dataset. The experimental results show that both a change detection accuracy result with a higher average and better detection efficiency is attained by the proposed method called the MTR-DBN when compared with other similar methods.

Keywords: Building Change Detection; Deep Belief Network; High-Resolution Remote Sensing Images; Tensor Model (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0218348X22402551

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