# Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors

arXiv: https://arxiv.org/abs/2301.13422 · HTML: https://mlsanity.com/paper/2301.13422/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 31 Jan 2023.

## Builds on (most cited first, 15 of 15)

- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://mlsanity.com/paper/2104.04015/) (2021)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images](https://mlsanity.com/paper/1805.06561/) (2018)
- [PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2010.05903/) (2020)
- [Deep Learning for Medical Anomaly Detection -- A Survey](https://mlsanity.com/paper/2012.02364/) (2020)
- [Adversarially Learned Anomaly Detection](https://mlsanity.com/paper/1812.02288/) (2018)
- [Anomaly Detection using One-Class Neural Networks](https://mlsanity.com/paper/1802.06360/) (2018)
- [Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis](https://mlsanity.com/paper/2001.01306/) (2020)
- [Fence GAN: Towards Better Anomaly Detection](https://mlsanity.com/paper/1904.01209/) (2019)
- [Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection](https://mlsanity.com/paper/2206.00515/) (2022)
- [Fast Feature Extraction with CNNs with Pooling Layers](https://mlsanity.com/paper/1805.03096/) (2018)

## Built on by (most cited first, 8 of 8)

- [One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning](https://mlsanity.com/paper/2303.12342/) (2023)
- [Learning a Cross-modality Anomaly Detector for Remote Sensing Imagery](https://mlsanity.com/paper/2310.07511/) (2023)
- [Towards High-Resolution Industrial Image Anomaly Detection](https://mlsanity.com/paper/2508.12931/) (2025)
- [DisasterBench: Benchmarking LLM Planning under Typed Tool Interface Constraints](https://mlsanity.com/paper/2605.27957/) (2026)
- [Label-free Anomaly Detection in Aerial Agricultural Images with Masked Image Modeling](https://mlsanity.com/paper/2404.08931/) (2024)
- [IrrNet: Advancing Irrigation Mapping with Incremental Patch Size Training on Remote Sensing Imagery](https://mlsanity.com/paper/2404.11762/) (2024)
- [Joint-Optimized Unsupervised Adversarial Domain Adaptation in Remote Sensing Segmentation with Prompted Foundation Model](https://mlsanity.com/paper/2411.05878/) (2024)
- [Zero-Shot Industrial Anomaly Segmentation with Image-Aware Prompt Generation](https://mlsanity.com/paper/2504.13560/) (2025)

## Abstract (arXiv)

Anomaly segmentation in high spatial resolution (HSR) remote sensing imagery is aimed at segmenting anomaly patterns of the earth deviating from normal patterns, which plays an important role in various Earth vision applications. However, it is a challenging task due to the complex distribution and the irregular shapes of objects, and the lack of abnormal samples. To tackle these problems, an anomaly segmentation model based on pixel descriptors (ASD) is proposed for anomaly segmentation in HSR imagery. Specifically, deep one-class classification is introduced for anomaly segmentation in the feature space with discriminative pixel descriptors. The ASD model incorporates the data argument for generating virtual ab-normal samples, which can force the pixel descriptors to be compact for normal data and meanwhile to be diverse to avoid the model collapse problems when only positive samples participated in the training. In addition, the ASD introduced a multi-level and multi-scale feature extraction strategy for learning the low-level and semantic information to make the pixel descriptors feature-rich. The proposed ASD model was validated using four HSR datasets and compared with the recent state-of-the-art models, showing its potential value in Earth vision applications.