Details
Original language | English |
---|---|
Pages (from-to) | 235-250 |
Number of pages | 16 |
Journal | ISPRS Journal of Photogrammetry and Remote Sensing |
Volume | 224 |
Early online date | 12 Apr 2025 |
Publication status | Published - Jun 2025 |
Abstract
In recent years, three-dimensional point clouds are used increasingly to document natural environments. Each dataset contains a diverse set of objects, at varying shapes and sizes, distributed throughout the data and intricately intertwined with the topography. Therefore, regions of interest are difficult to find and consequent analyses become a challenge. Inspired from visual perception principles, we propose to differentiate objects of interest from the cluttered environment by evaluating how much they stand out from their surroundings, i.e., their geometric salience. Previous saliency detection approaches suggested mostly handcrafted attributes for the task. However, such methods fail when the data are too noisy or have high levels of texture. Here we propose a learning-based mechanism that accommodates noise and textured surfaces. We assume that within the natural environment any change from the prevalent surface would suggest a salient object. Thus, we first learn the underlying surface and then search for anomalies within it. Initially, a deep neural network is trained to reconstruct the surface. Regions where the reconstructed part deviates significantly from the original point cloud yield a substantial reconstruction error, signifying an anomaly, i.e., saliency. We demonstrate the effectiveness of the proposed approach by searching for salient features in various natural scenarios, which were acquired by different acquisition platforms. We show the strong correlation between the reconstruction error and salient objects. To promote benchmarking and reproducibility, the code used in this work can be found on https://github.com/rarav/salient_anomaly/releases/tag/v1.0.0 while the datasets are published on doi: 10.48436/mps0m-c9n43 and 10.48436/fh0am-at738.
Keywords
- Anomaly detection, Deep neural network, Geomorphological entities, Salient object detection (SOD)
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
- Engineering(all)
- Engineering (miscellaneous)
- Computer Science(all)
- Computer Science Applications
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
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In: ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 224, 06.2025, p. 235-250.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Evaluating saliency scores in point clouds of natural environments by learning surface anomalies
AU - Arav, Reuma
AU - Wittich, Dennis
AU - Rottensteiner, Franz
N1 - Publisher Copyright: © 2025 The Authors
PY - 2025/6
Y1 - 2025/6
N2 - In recent years, three-dimensional point clouds are used increasingly to document natural environments. Each dataset contains a diverse set of objects, at varying shapes and sizes, distributed throughout the data and intricately intertwined with the topography. Therefore, regions of interest are difficult to find and consequent analyses become a challenge. Inspired from visual perception principles, we propose to differentiate objects of interest from the cluttered environment by evaluating how much they stand out from their surroundings, i.e., their geometric salience. Previous saliency detection approaches suggested mostly handcrafted attributes for the task. However, such methods fail when the data are too noisy or have high levels of texture. Here we propose a learning-based mechanism that accommodates noise and textured surfaces. We assume that within the natural environment any change from the prevalent surface would suggest a salient object. Thus, we first learn the underlying surface and then search for anomalies within it. Initially, a deep neural network is trained to reconstruct the surface. Regions where the reconstructed part deviates significantly from the original point cloud yield a substantial reconstruction error, signifying an anomaly, i.e., saliency. We demonstrate the effectiveness of the proposed approach by searching for salient features in various natural scenarios, which were acquired by different acquisition platforms. We show the strong correlation between the reconstruction error and salient objects. To promote benchmarking and reproducibility, the code used in this work can be found on https://github.com/rarav/salient_anomaly/releases/tag/v1.0.0 while the datasets are published on doi: 10.48436/mps0m-c9n43 and 10.48436/fh0am-at738.
AB - In recent years, three-dimensional point clouds are used increasingly to document natural environments. Each dataset contains a diverse set of objects, at varying shapes and sizes, distributed throughout the data and intricately intertwined with the topography. Therefore, regions of interest are difficult to find and consequent analyses become a challenge. Inspired from visual perception principles, we propose to differentiate objects of interest from the cluttered environment by evaluating how much they stand out from their surroundings, i.e., their geometric salience. Previous saliency detection approaches suggested mostly handcrafted attributes for the task. However, such methods fail when the data are too noisy or have high levels of texture. Here we propose a learning-based mechanism that accommodates noise and textured surfaces. We assume that within the natural environment any change from the prevalent surface would suggest a salient object. Thus, we first learn the underlying surface and then search for anomalies within it. Initially, a deep neural network is trained to reconstruct the surface. Regions where the reconstructed part deviates significantly from the original point cloud yield a substantial reconstruction error, signifying an anomaly, i.e., saliency. We demonstrate the effectiveness of the proposed approach by searching for salient features in various natural scenarios, which were acquired by different acquisition platforms. We show the strong correlation between the reconstruction error and salient objects. To promote benchmarking and reproducibility, the code used in this work can be found on https://github.com/rarav/salient_anomaly/releases/tag/v1.0.0 while the datasets are published on doi: 10.48436/mps0m-c9n43 and 10.48436/fh0am-at738.
KW - Anomaly detection
KW - Deep neural network
KW - Geomorphological entities
KW - Salient object detection (SOD)
UR - http://www.scopus.com/inward/record.url?scp=105002282736&partnerID=8YFLogxK
U2 - 10.1016/j.isprsjprs.2025.03.022
DO - 10.1016/j.isprsjprs.2025.03.022
M3 - Article
AN - SCOPUS:105002282736
VL - 224
SP - 235
EP - 250
JO - ISPRS Journal of Photogrammetry and Remote Sensing
JF - ISPRS Journal of Photogrammetry and Remote Sensing
SN - 0924-2716
ER -