Comparison of novelty detection methods for multispectral images in rover-based planetary exploration missions

Kerner, Hannah; Wagstaff, Kiri L.; Bue, Brian D.; Wellington, Danika; Jacob, Samantha; Horton, Paul; Bell, James F.; Kwan, Chiman; Amor, Heni Ben

doi:10.1007/s10618-020-00697-6

Cited by 33 publications

(32 citation statements)

References 90 publications

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“…• Mastcam [66] is a Mars geological image datasets collected by the Mastcam imaging system, including 9,728 64x64x6 Mars geological images. Each image includes three color channels and three grayscale channels.…”

Section: Datasetsmentioning

confidence: 99%

Deep Anomaly Detection with Deviation Networks

Pang

Shen

Hengel

2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

264

168

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Although deep learning has been applied to successfully address many data mining problems, relatively limited work has been done on deep learning for anomaly detection. Existing deep anomaly detection methods, which focus on learning new feature representations to enable downstream anomaly detection methods, perform indirect optimization of anomaly scores, leading to data-inefficient learning and suboptimal anomaly scoring. Also, they are typically designed as unsupervised learning due to the lack of large-scale labeled anomaly data. As a result, they are difficult to leverage prior knowledge (e.g., a few labeled anomalies) when such information is available as in many real-world anomaly detection applications.This paper introduces a novel anomaly detection framework and its instantiation to address these problems. Instead of representation learning, our method fulfills an end-to-end learning of anomaly scores by a neural deviation learning, in which we leverage a few (e.g., multiple to dozens) labeled anomalies and a prior probability to enforce statistically significant deviations of the anomaly scores of anomalies from that of normal data objects in the upper tail. Extensive results show that our method can be trained substantially more data-efficiently and achieves significantly better anomaly scoring than state-of-the-art competing methods.

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Section: Datasetsmentioning

confidence: 99%

Deep Anomaly Detection with Deviation Networks

Pang

Shen

Hengel

2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

264

168

View full text Add to dashboard Cite

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“…Then we look at deviations from that common representation to identify anomalies. The majority of machine applications are for novelty detection are using read the [34] detected method that computes pixelized anomaly scores, that is, the Mahalanobis distance [9] between a single pixel and a background distribution, which could define the background to be a window around the pixel the rest of the image or even an entire dataset of typical images. The most common methods for anomaly detection in the machine learning literature are reconstruction-based methods where minimizing model reconstruction of not common examples such that this distance reconstruction error will be significant for the novel [34].…”

Section: Novelty or Anomaly Detectionmentioning

confidence: 99%

Multi-Granularity Neural Network Encoding Method for Land Cover and Land Use Image Classification

Wang¹,

Bosco²,

Hategekimana³

2021

Preprint

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Deep learning classification is the state-of-the-art of machine learning approach. Earlier work proves that the deep convolutional neural network has successfully and brilliantly in different applications such as images or video data. Recognizing and clarifying the remote sensing aspect of the earth's surface and exploit land cover and land use (LCLU). First, this article summarized the remote sensing emerging application and challenges for deep learning methods. Second, we propose four approaches to learn efficient and effective CNNs to transfer image representation on the ImageNet dataset to recognize LCLU datasets. We use VGG16, Inception-ResNet-V2, Inception-V3, and DenseNet201 models to extract features from the EACC dataset. We use pre-trained CNNs on ImageNet to extract features. For feature selection we proposed principal component analysis (PCA) to improve accuracy and speed up the model. We train our model by multi-layer perceptron (MLP) as a classifier. Lastly, we apply the multi-granularity encoding ensemble model. We achieve an overall accuracy of 92.3% for the nine-class classification problem. This work will help remote sensing scientists understand deep learning tools and apply them in large-scale remote sensing challenges

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“…Interest in planetary exploration missions has increased significantly in the last decade [1] as such missions enrich our knowledge about the solar system [2]. A crucial role in most such missions is played by the scientific imaging instruments that are used for various purposes, including planetary surface characterization and spectral mapping for mineralogy.…”

Section: Introductionmentioning

confidence: 99%

Patch-Level Unsupervised Planetary Change Detection

Saha

Zhu

2022

IEEE Geosci. Remote Sensing Lett.

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Change detection (CD) is critical for analyzing data collected by planetary exploration missions, e.g., for identification of new impact craters. However, CD is still a relatively new topic in the context of planetary exploration. Sheer variation of planetary data makes CD much more challenging than in the case of Earth observation (EO). Unlike CD for EO, patch-level decision is preferred in planetary exploration as it is difficult to obtain perfect pixelwise alignment/co-registration between the bi-temporal planetary images. Lack of labeled bi-temporal data impedes supervised CD. To overcome these challenges, we propose an unsupervised CD method that exploits a pretrained feature extractor to obtain bi-temporal deep features that are further processed using global max-pooling to obtain patch-level feature description. Bi-temporal patch-level features are further analyzed based on difference to determine whether a patch is changed. Additionally, a self-supervised method is proposed to estimate the decision boundary between the changed and unchanged patches. Experimental results on three planetary CD datasets from two different planetary bodies (Mars and Moon) demonstrate that the proposed method often outperforms supervised planetary CD methods. Code is available at https://gitlab.lrz.de/ai4eo/cd/-/tree/main/planetaryCDUnsup.

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Comparison of novelty detection methods for multispectral images in rover-based planetary exploration missions

Cited by 33 publications

References 90 publications

Deep Anomaly Detection with Deviation Networks

Deep Anomaly Detection with Deviation Networks

Multi-Granularity Neural Network Encoding Method for Land Cover and Land Use Image Classification

Patch-Level Unsupervised Planetary Change Detection

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