scholarly journals 3D CHANGE DETECTION IN URBAN AREAS BASED ON DCNN USING A SINGLE IMAGE

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 89-95
Author(s):  
H. Amini Amirkolaee ◽  
H. Arefi
Keyword(s):  
Change Detection ◽  
Urban Areas ◽  
Satellite Images ◽  
Satellite Image ◽  
Light Detection And Ranging ◽  
Surface Model ◽  
Lidar Data ◽  
Single Image ◽  
Convolutional Network ◽  
Novel Approach

Abstract. In this paper, a novel approach is proposed for 3D change detection in urban areas using only a single satellite images. To this purpose, a dense convolutional neural network (DCNN) is utilized in order to estimate a digital surface model (DSM) from a single image. In this regard, a densely connected convolutional network is employed for feature extraction and an upsampling method based on dilated convolution is employed for estimating the height values. The proposed DCNN is trained using satellite and Light Detection and Ranging (LiDAR) data which are provided in 2012 from Isfahan, Iran. Subsequently, the trained network is utilized in order to estimate DSM of a single satellite image that is provided in 2006. Finally, the changed areas are detected by subtracting the estimated DSMs. Evaluating the accuracy of the detected changed areas indicates 66.59, 72.90 and 67.90 for correctness, completeness, and kappa, respectively.

scholarly journals Novel approach of association rule mining for tree canopy assessment

2021 ◽  
Vol 10 (3) ◽  
pp. 771
Author(s):  
Nilkamal More ◽  
V. B. Nikam ◽  
Biplab Banerjee
Keyword(s):  
Urban Areas ◽  
Satellite Images ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Principal Component ◽  
Optimal Number ◽  
Tree Canopy ◽  
Urban Region ◽  
Apriori Algorithm ◽  
Novel Approach

The evolution of technology and availability of voluminous satellite images are bringing a new scenario in satellite image classification where a performance efficient method for predictive analysis of satellite images for land cover classification needs to be devised. As urban areas are growing at faster rate, special attention needs to be given to solve tree canopy assessment problem. Vegetation indices are calculated from spectral information of satellite images. Hundreds of such vegetation indices are available to detect vegetation from a satellite image. The contribution of this paper is designing an improved Apriori algorithm to select optimal number of vegetation indices for tree canopy assessment. In this research, we propose a novel computational approach that allows the improvement of results. It selects optimal combination of vegetation indices and applies principal component analysis on it. It uses a greedy approach based on Apriori algorithm. This study emphasizes on assessment of tree canopy using GPU-enabled environment for performance-efficient assessment. The results achieved, are comparable to state-of-the-art techniques, with an accuracy of 96%. The research has considered 4 years data for Mumbai city of India. This research is useful for Green India Mission of India to assess tree canopy of urban region.

scholarly journals CHANGE DETECTION AND DEFORMATION ANALYSIS BASED ON MOBILE LASER SCANNING DATA OF URBAN AREAS

2020 ◽  
Vol V-2-2020 ◽  
pp. 703-710
Author(s):  
J. Gehrung ◽  
M. Hebel ◽  
M. Arens ◽  
U. Stilla
Keyword(s):  
Change Detection ◽  
Urban Areas ◽  
Laser Scanning ◽  
State Of The Art ◽  
Point Clouds ◽  
Deformation Analysis ◽  
Lidar Data ◽  
Efficient Manner ◽  
Incomplete Observations ◽  
Novel Method

Abstract. Change detection is an important tool for processing multiple epochs of mobile LiDAR data in an efficient manner, since it allows to cope with an otherwise time-consuming operation by focusing on regions of interest. State-of-the-art approaches usually either do not handle the case of incomplete observations or are computationally expensive. We present a novel method based on a combination of point clouds and voxels that is able to handle said case, thereby being computationally less expensive than comparable approaches. Furthermore, our method is able to identify special classes of changes such as partially moved, fully moved and deformed objects in addition to the appeared and disappeared objects recognized by conventional approaches. The performance of our method is evaluated using the publicly available TUM City Campus datasets, showing an overall accuracy of 88 %.

scholarly journals Improvement of Clay and Sand Quantification Based on a Novel Approach with a Focus on Multispectral Satellite Images

2018 ◽  
Vol 10 (10) ◽  
pp. 1555 ◽  
Author(s):  
Caio Fongaro ◽  
José Demattê ◽  
Rodnei Rizzo ◽  
José Lucas Safanelli ◽  
Wanderson Mendes ◽  
...  
Keyword(s):  
Model Calibration ◽  
Large Scale ◽  
Satellite Images ◽  
Satellite Image ◽  
Soil Mapping ◽  
Spectral Signatures ◽  
Novel Approach ◽  
Multispectral Satellite Images ◽  
Reduced Costs ◽  
Multispectral Satellite Image

Soil mapping demands large-scale surveys that are costly and time consuming. It is necessary to identify strategies with reduced costs to obtain detailed information for soil mapping. We aimed to compare multispectral satellite image and relief parameters for the quantification and mapping of clay and sand contents. The Temporal Synthetic Spectral (TESS) reflectance and Synthetic Soil Image (SYSI) approaches were used to identify and characterize texture spectral signatures at the image level. Soil samples were collected (0–20 cm depth, 919 points) from an area of 14,614 km2 in Brazil for reference and model calibration. We compared different prediction approaches: (a) TESS and SYSI; (b) Relief-Derived Covariates (RDC); and (c) SYSI plus RDC. The TESS method produced highly similar behavior to the laboratory convolved data. The sandy textural class showed a greater increase in average spectral reflectance from Band 1 to 7 compared with the clayey class. The prediction using SYSI produced a better result for clay (R2 = 0.83; RMSE = 65.0 g kg−1) and sand (R2 = 0.86; RMSE = 79.9 g kg−1). Multispectral satellite images were more stable for the identification of soil properties than relief parameters.

scholarly journals Towards Sub-Pixel Automatic Geometric Corrections of Very-High Resolution Panchromatic Satellite Data of Urban Areas

2019 ◽  
Vol 11 (9) ◽  
pp. 1097 ◽  
Author(s):  
Aleš Marsetič ◽  
Peter Pehani
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Urban Areas ◽  
Satellite Images ◽  
Reference Data ◽  
Geometric Model ◽  
Control Point ◽  
Satellite Image ◽  
Extraction Algorithm ◽  
Very High

This paper presents an automatic procedure for the geometric corrections of very-high resolution (VHR) optical panchromatic satellite images. The procedure is composed of three steps: an automatic ground control point (GCP) extraction algorithm that matches the linear features that were extracted from the satellite image and reference data; a geometric model that applies a rational function model; and, the orthorectification procedure. Accurate geometric corrections can only be achieved if GCPs are employed to precisely correct the geometric biases of images. Due to the high resolution and the varied acquisition geometry of images, we propose a fast, segmentation based method for feature extraction. The research focuses on densely populated urban areas, which are very challenging in terms of feature extraction and matching. The proposed algorithm is capable of achieving results with a root mean square error of approximately one pixel or better, on a test set of 14 panchromatic Pléiades images. The procedure is robust and it performs well in urban areas, even for images with high off-nadir angles.

scholarly journals Geometric Accuracy Assessment of Deimos-2 Panchromatic Stereo Pairs: Sensor Orientation and Digital Surface Model Production

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7234
Author(s):  
Manuel A. Aguilar ◽  
Rafael Jiménez-Lao ◽  
Abderrahim Nemmaoui ◽  
Fernando J. Aguilar
Keyword(s):  
Urban Areas ◽  
Satellite Images ◽  
Accuracy Assessment ◽  
Bare Soil ◽  
Surface Model ◽  
Stereo Pair ◽  
Rational Polynomial ◽  
Sensor Orientation ◽  
Vertical Accuracy ◽  
Land Covers

Accurate elevation data, which can be extracted from very high-resolution (VHR) satellite images, are vital for many engineering and land planning applications. In this way, the main goal of this work is to evaluate the capabilities of VHR Deimos-2 panchromatic stereo pairs to obtain digital surface models (DSM) over different land covers (bare soil, urban and agricultural greenhouse areas). As a step prior to extracting the DSM, different orientation models based on refined rational polynomial coefficients (RPC) and a variable number of very accurate ground control points (GCPs) were tested. The best sensor orientation model for Deimos-2 L1B satellite images was the RPC model refined by a first-order polynomial adjustment (RPC1) supported on 12 accurate and evenly spatially distributed GCPs. Regarding the Deimos-2 based DSM, its completeness and vertical accuracy were compared with those obtained from a WorldView-2 panchromatic stereo pair by using exactly the same methodology and semiglobal matching (SGM) algorithm. The Deimos-2 showed worse completeness values (about 6% worse) and vertical accuracy results (RMSEZ 42.4% worse) than those computed from WorldView-2 imagery over the three land covers tested, although only urban areas yielded statistically significant differences (p < 0.05).

scholarly journals STATE OF THE ART IN DIGITAL SURFACE MODELLING FROM MULTI-VIEW HIGH-RESOLUTION SATELLITE IMAGES

2020 ◽  
Vol V-2-2020 ◽  
pp. 351-356 ◽  
Author(s):  
Y. Han ◽  
S. Wang ◽  
D. Gong ◽  
Y. Wang ◽  
Y. Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Satellite Images ◽  
Satellite Image ◽  
Ground Truth ◽  
Semantic Interpretation ◽  
Surface Model ◽  
Three Solutions ◽  
Surface Modelling ◽  
High Resolution Satellite Images

Abstract. Data from the optical satellite imaging sensors running 24/7, is collecting in embarrassing abundance nowadays. Besides more suitable for large-scale mapping, multi-view high-resolution satellite images (HRSI) are cheaper when comparing to Light Detection And Ranging (LiDAR) data and aerial remotely sensed images, which are more accessible sources for digital surface modelling and updating. Digital Surface Model (DSM) generation is one of the most critical steps for mapping, 3D modelling, and semantic interpretation. Computing DSM from this dataset is relatively new, and several solutions exist in the market, both commercial and open-source solutions, the performances of these solutions have not yet been comprehensively analyzed. Although some works and challenges have focused on the DSM generation pipeline and the geometric accuracy of the generated DSM, the evaluations, however, do not consider the latest solutions as the fast development in this domain. In this work, we discussed the pipeline of the considered both commercial and opensource solutions, assessed the accuracy of the multi-view satellite image-based DSMs generation methods with LiDAR-derived DSM as the ground truth. Three solutions, including Satellite Stereo Pipeline (S2P), PCI Geomatica, and Agisoft Metashape, are evaluated on a WorldView-3 multi-view satellite dataset both quantitatively and qualitatively with the LiDAR ground truth. Our comparison and findings are presented in the experimental section.

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