Deep Learning-Based Change Detection in Remote Sensing Images: A Review

Shafique, Ayesha; Cao, Guo; Khan, Zia U.; Asad, Muhammad; Aslam, Muhammad

doi:10.3390/rs14040871

Cited by 198 publications

(84 citation statements)

References 191 publications

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“…Furthermore, events like wars occasionally cause large-scale destruction and human displacement [7]. Aerial and satellite image based change detection (CD) methods [8] are used to quantify the impact of such events. Optical/multi-spectral images are used for…”

Section: Introductionmentioning

confidence: 99%

Supervised Change Detection Using Prechange Optical-SAR and Postchange SAR Data

Saha

Shahzad

Ebel

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Change detection using satellite/aerial images is used to quantify the impacts of many natural and man-made disasters. At the occurrence of such events, both pre-change optical and Synthetic Aperture Radar (SAR) images can be obtained by going back in time. However, the availability of the post-change optical image is often hindered by the presence of artifacts like clouds.To circumnavigate this, we propose a novel change detection data setting which uses both optical and SAR images prechange, yet only SAR imagery post-change. For this challenging scenario, we propose a Siamese network that processes the pre-change and post-change SAR inputs using a shared set of weights, while the pre-change optical input is processed using a network that do not share the weights with the SAR inputs. The encoded weights from the three networks are fused and finally decoded using a common decoder to obtain the change map. Our model effectively fuses multi-sensor information and can obtain satisfactory result despite the absence of the post-change optical image. Experimental results on a multi-sensor urban dataset demonstrate the effectiveness of the proposed approach. Code is available at https:

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Section: Introductionmentioning

confidence: 99%

Supervised Change Detection Using Prechange Optical-SAR and Postchange SAR Data

Saha

Shahzad

Ebel

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In recent years, research on the extraction of building information has been conducted using a high-resolution remote sensing image classification method based on the convolutional neural network (CNN) [ 6 ]. These studies reviewed the semantic representation capacity of the neural network for road network extraction, building detection, and crop classification [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Method of Building Detection in Optical Remote Sensing Images Based on SegFormer

Rui

Yang

et al. 2023

Sensors

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An appropriate detection network is required to extract building information in remote sensing images and to relieve the issue of poor detection effects resulting from the deficiency of detailed features. Firstly, we embed a transposed convolution sampling module fusing multiple normalization activation layers in the decoder based on the SegFormer network. This step alleviates the issue of missing feature semantics by adding holes and fillings, cascading multiple normalizations and activation layers to hold back over-fitting regularization expression and guarantee steady feature parameter classification. Secondly, the atrous spatial pyramid pooling decoding module is fused to explore multi-scale contextual information and to overcome issues such as the loss of detailed information on local buildings and the lack of long-distance information. Ablation experiments and comparison experiments are performed on the remote sensing image AISD, MBD, and WHU dataset. The robustness and validity of the improved mechanism are demonstrated by control groups of ablation experiments. In comparative experiments with the HRnet, PSPNet, U-Net, DeepLabv3+ networks, and the original detection algorithm, the mIoU of the AISD, the MBD, and the WHU dataset is enhanced by 17.68%, 30.44%, and 15.26%, respectively. The results of the experiments show that the method of this paper is superior to comparative methods such as U-Net. Furthermore, it is better for integrity detection of building edges and reduces the number of missing and false detections.

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“…Remote sensing (RS) data is widely used in land use and land cover (LULC) research, and automated image classification is one of the easiest and preferable techniques to prepare LULC of a resource survey area [1]. With the progress of RS satellite observation technology and the improvement of image acquisition convenience, pixelbased multi-classification of land resources has become an important research topic [2]. In the past few decades, the maximum likelihood classification (MLC) and random forests (RF) of traditional machine learning are fast and feasible RS image classification methods for LULC, but they have high requirements for RS image quality [3].…”

Section: Introductionmentioning

confidence: 99%

Land Cover Classification of Resources Survey Remote Sensing Images Based on Segmentation Model

et al. 2022

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Land type survey is an important task of land resources survey and the basis of scientific management of land resources. With the increasingly prominent problems of population, resources, and environment, there is an urgent need for a fast and accurate classification method of large-scale land use and land cover based on remote sensing data. Traditional machine learning classification methods based on pixel classification achieved sufficient results and are widely used, such as maximum likelihood classification and random forests method. However, with the development of the novel technology of deep learning, in practical application, for multi-classified land resources, how to use the fast and effective classification method of low and medium resolution RS images needs further research. This paper takes the land resource classification of the Tonghe medium resolution RS dataset of the third land survey in China as an example to screen and compare traditional machine learning classification methods and semantic segmentation models FC-DenseNet56, GCN, BiSeNet, U-Net, DeepLabV3, AdapNet, and PSPNet, which aim to select the optimal feature extraction model. The results show that the classification accuracy of the U-Net model can reach 93.62%, which is more accurate and effective than traditional machine learning methods and other semantic segmentation models. It is suitable for multi-classification tasks of land cover resources in low and medium resolution RS images and shows a superior effect in practical application. Besides, the conclusion of this study can provide a demonstration for large-scale land cover resources investigation using low and medium resolution RS images.INDEX TERMS land use and land cover, semantic segmentation, multi-classification, deep learning, U-Net.

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Deep Learning-Based Change Detection in Remote Sensing Images: A Review

Cited by 198 publications

References 191 publications

Supervised Change Detection Using Prechange Optical-SAR and Postchange SAR Data

Supervised Change Detection Using Prechange Optical-SAR and Postchange SAR Data

Method of Building Detection in Optical Remote Sensing Images Based on SegFormer

Land Cover Classification of Resources Survey Remote Sensing Images Based on Segmentation Model

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