Search Results for author:
Found 23 papers, 14 papers with code
Segment Any Change
Visual foundation models have achieved remarkable results in zero-shot image classification and segmentation, but zero-shot change detection remains an open problem.
MapChange: Enhancing Semantic Change Detection with Temporal-Invariant Historical Maps Based on Deep Triplet Network
Semantic Change Detection (SCD) is recognized as both a crucial and challenging task in the field of image analysis.
EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question Answering
Earth vision research typically focuses on extracting geospatial object locations and categories but neglects the exploration of relations between objects and comprehensive reasoning.
A Unified Remote Sensing Anomaly Detector Across Modalities and Scenes via Deviation Relationship Learning
Firstly, we reformulate the anomaly detection task as an undirected bilayer graph based on the deviation relationship, where the anomaly score is modeled as the conditional probability, given the pattern of the background and normal objects.
Scalable Multi-Temporal Remote Sensing Change Data Generation via Simulating Stochastic Change Process
To solve these two problems, we present the change generator (Changen), a GAN-based GPCM, enabling controllable object change data generation, including customizable object property, and change event.
Ranked #1 on
Change Detection
on S2Looking
Seeing Beyond the Patch: Scale-Adaptive Semantic Segmentation of High-resolution Remote Sensing Imagery based on Reinforcement Learning
This shortcoming poses a significant challenge in processing complex and variable geo-objects, which results in semantic inconsistency in segmentation results.
Class Prior-Free Positive-Unlabeled Learning with Taylor Variational Loss for Hyperspectral Remote Sensing Imagery
Positive-unlabeled learning (PU learning) in hyperspectral remote sensing imagery (HSI) is aimed at learning a binary classifier from positive and unlabeled data, which has broad prospects in various earth vision applications.
FarSeg++: Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery
Generic semantic segmentation methods mainly focus on the scale variation in natural scenarios.
One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning
In this paper, an unsupervised transferred direct detection (TDD) model is proposed, which is optimized directly for the anomaly detection task (one-step paradigm) and has transferability.
Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors
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.
One-Class Risk Estimation for One-Class Hyperspectral Image Classification
Hyperspectral imagery (HSI) one-class classification is aimed at identifying a single target class from the HSI by using only knowing positive data, which can significantly reduce the requirements for annotation.
The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery
The objective of the competition is to automatically detect landslides based on large-scale multiple sources of satellite imagery collected globally.
AutoLC: Search Lightweight and Top-Performing Architecture for Remote Sensing Image Land-Cover Classification
Land-cover classification has long been a hot and difficult challenge in remote sensing community.
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
Deep learning approaches have shown promising results in remote sensing high spatial resolution (HSR) land-cover mapping.
Ranked #12 on
Semantic Segmentation
on LoveDA
Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery
For high spatial resolution (HSR) remote sensing images, bitemporal supervised learning always dominates change detection using many pairwise labeled bitemporal images.
Ranked #12 on
Change Detection
on LEVIR-CD
Building change detection for remote sensing images
Change detection for remote sensing images
+1
FactSeg: Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery
The small object semantic segmentation task is aimed at automatically extracting key objects from high-resolution remote sensing (HRS) imagery.
Ranked #13 on
Semantic Segmentation
on iSAID
A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification
However, FPGA has difficulty extracting the most discriminative features when the sample data is imbalanced.
Ranked #1 on
Hyperspectral Image Classification
on Indian Pines
(Kappa metric, using extra
training data)
WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets for hyperspectral image classification
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.
Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery
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 #18 on
Semantic Segmentation
on iSAID
FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification
In this paper, a fast patch-free global learning (FPGA) framework is proposed for HSI classification.
Ranked #1 on
Hyperspectral Image Classification
on Pavia University
(OA@200 metric)
Hi-UCD: A Large-scale Dataset for Urban Semantic Change Detection in Remote Sensing Imagery
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.
Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation
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.
AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification
The goal of AID is to advance the state-of-the-arts in scene classification of remote sensing images.
