scholarly journals END-TO-END BUILDING CHANGE DETECTION MODEL IN AERIAL IMAGERY AND DIGITAL SURFACE MODEL BASED ON NEURAL NETWORKS

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1239-1246
Author(s):  
X. Lian ◽  
W. Yuan ◽  
Z. Guo ◽  
Z. Cai ◽  
X. Song ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Change Detection ◽  
Surface Model ◽  
Digital Surface Model ◽  
Model Framework ◽  
Detection Techniques ◽  
Model Based ◽  
Multi Temporal ◽  
End To End

Abstract. Multi-temporal building change detection is one of the most essential major issues of photogrammetry and remote sensing at current stage, which is of great significance for wide applications as offering real estate indicators as well as monitoring urban environment. Although current photogrammetry methodologies could be applicated to 2-D remote sensing imagery for rectification with sensor parameters, multi-temporal aerial or satellite imagery is not adequate to offer spectral and textual features for building change detection. Alongside recent development of Dense Image Matching (DIM) technology, the acquisition of 3-D point cloud and Digital Surface Model (DSM) has been generally realized, which could be combined with imagery, making building change detection more effective with greater spatial structure and texture information. Over the past years, scholars have put forward vast change detection techniques including traditional and model-based solutions. Nevertheless, existing appropriate methodology combined with Neural Networks (NN) for accurate building change detection with multi-temporal imagery and DSM remains to be of great research focus currently due to the inevitable limitations and omissions of existing NN-based methods, which is of great research prospect. This study proposed a novel end-to-end model framework based on deep learning for pixel-level building change detection from high-spatial resolution aerial ortho imagery and corresponding DSM sharing same resolution, which is from the dataset of Tokyo whole area.

scholarly journals IMPROVING THE ACCURACY OF ESTIMATED 3D POSITIONS USING MULTI-TEMPORAL ALOS/PRISM TRIPLET IMAGES

2015 ◽  
Vol II-3/W4 ◽  
pp. 223-230
Author(s):  
J. Susaki ◽  
H. Kishimoto
Keyword(s):  
Remote Sensing ◽  
Experimental Results ◽  
Polynomial Coefficient ◽  
Surface Model ◽  
Digital Surface Model ◽  
Rational Polynomial ◽  
Multi Temporal ◽  
Alos Prism

In this paper, we present a method to improve the accuracy of a digital surface model (DSM) by utilizing multi-temporal triplet images. The Advanced Land Observing Satellite (ALOS) / Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) measures triplet images in the forward, nadir, and backward view directions, and a DSM is generated from the obtained set of triplet images. To generate a certain period of DSM, multiple DSMs generated from individual triplet images are compared, and outliers are removed. Our proposed method uses a traditional surveying approach to increase observations and solves multiple observation equations from all triplet images via the bias-corrected rational polynomial coefficient (RPC) model. Experimental results from using five sets of PRISM triplet images taken of the area around Saitama, north of Tokyo, Japan, showed that the average planimetric and height errors in the coordinates estimated from multi-temporal triplet images were 3.26 m and 2.71 m, respectively, and that they were smaller than those generated by using each set of triplet images individually. As a result, we conclude that the proposed method is effective for stably generating accurate DSMs from multi-temporal triplet images.

scholarly journals AI-ForestWatch: semantic segmentation based end-to-end framework for forest estimation and change detection using multi-spectral remote sensing imagery

2021 ◽  
Vol 15 (02) ◽  
Author(s):  
Annus Zulfiqar ◽  
Muhammad M. Ghaffar ◽  
Muhammad Shahzad ◽  
Christian Weis ◽  
Muhammad I. Malik ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Semantic Segmentation ◽  
Remote Sensing Imagery ◽  
End To End

scholarly journals Mapping Rice Paddy Based on Machine Learning with Sentinel-2 Multi-Temporal Data: Model Comparison and Transferability

2020 ◽  
Vol 12 (10) ◽  
pp. 1620 ◽  
Author(s):  
Weichun Zhang ◽  
Hongbin Liu ◽  
Wei Wu ◽  
Linqing Zhan ◽  
Jing Wei
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Rice Paddy ◽  
Temporal Data ◽  
Model Transferability ◽  
Local Classification ◽  
Multi Temporal ◽  
Sentinel 2

Rice is an important agricultural crop in the Southwest Hilly Area, China, but there has been a lack of efficient and accurate monitoring methods in the region. Recently, convolutional neural networks (CNNs) have obtained considerable achievements in the remote sensing community. However, it has not been widely used in mapping a rice paddy, and most studies lack the comparison of classification effectiveness and efficiency between CNNs and other classic machine learning models and their transferability. This study aims to develop various machine learning classification models with remote sensing data for comparing the local accuracy of classifiers and evaluating the transferability of pretrained classifiers. Therefore, two types of experiments were designed: local classification experiments and model transferability experiments. These experiments were conducted using cloud-free Sentinel-2 multi-temporal data in Banan District and Zhongxian County, typical hilly areas of Southwestern China. A pure pixel extraction algorithm was designed based on land-use vector data and a Google Earth Online image. Four convolutional neural network (CNN) algorithms (one-dimensional (Conv-1D), two-dimensional (Conv-2D) and three-dimensional (Conv-3D_1 and Conv-3D_2) convolutional neural networks) were developed and compared with four widely used classifiers (random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM) and multilayer perceptron (MLP)). Recall, precision, overall accuracy (OA) and F1 score were applied to evaluate classification accuracy. The results showed that Conv-2D performed best in local classification experiments with OA of 93.14% and F1 score of 0.8552 in Banan District, OA of 92.53% and F1 score of 0.8399 in Zhongxian County. CNN-based models except Conv-1D provided more desirable performance than non-CNN classifiers. Besides, among the non-CNN classifiers, XGBoost received the best result with OA of 89.73% and F1 score of 0.7742 in Banan District, SVM received the best result with OA of 88.57% and F1 score of 0.7538 in Zhongxian County. In model transferability experiments, almost all CNN classifiers had low transferability. RF and XGBoost models have achieved acceptable F1 scores for transfer (RF = 0.6673 and 0.6469, XGBoost = 0.7171 and 0.6709, respectively).

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