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.
In this paper, a weakly supervised HSI one-class classifier, namely HOneCls is proposed to solve the problem of under-fitting of the positive class occurs in the HSI data with low target proportion, where a risk estimator -- the One-Class Risk Estimator -- is particularly introduced to make the full convolutional neural network (FCN) with the ability of one class classification.
no code implementations • 6 Sep 2022 • Omid Ghorbanzadeh, Yonghao Xu, Hengwei Zhao, Junjue Wang, Yanfei Zhong, Dong Zhao, Qi Zang, Shuang Wang, Fahong Zhang, Yilei Shi, Xiao Xiang Zhu, Lin Bai, Weile Li, Weihang Peng, Pedram Ghamisi
The objective of the competition is to automatically detect landslides based on large-scale multiple sources of satellite imagery collected globally.
Land-cover classification has long been a hot and difficult challenge in remote sensing community.
Deep learning approaches have shown promising results in remote sensing high spatial resolution (HSR) land-cover mapping.
Ranked #4 on Semantic Segmentation on LoveDA
For high spatial resolution (HSR) remote sensing images, bitemporal supervised learning always dominates change detection using many pairwise labeled bitemporal images.
Ranked #2 on Change Detection on LEVIR-CD
The small object semantic segmentation task is aimed at automatically extracting key objects from high-resolution remote sensing (HRS) imagery.
Ranked #2 on Semantic Segmentation on iSAID
However, FPGA has difficulty extracting the most discriminative features when the sample data is imbalanced.
Some start-of-art hyperspectral image classification methods benchmarked the WHU-Hi dataset, and the experimental results show that WHU-Hi is a challenging dataset.
However, general semantic segmentation methods mainly focus on scale variation in the natural scene, with inadequate consideration of the other two problems that usually happen in the large area earth observation scene.
Ranked #7 on Semantic Segmentation on iSAID
In this paper, a fast patch-free global learning (FPGA) framework is proposed for HSI classification.
Ranked #2 on Hyperspectral Image Classification on Salinas
With the acceleration of the urban expansion, urban change detection (UCD), as a significant and effective approach, can provide the change information with respect to geospatial objects for dynamical urban analysis.
Over the past two decades, a large amount of research on this task has been carried out, which mainly focuses on the following three core issues: feature extraction, similarity metric and relevance feedback.
The goal of AID is to advance the state-of-the-arts in scene classification of remote sensing images.