Search Results for author:
Found 13 papers, 8 papers with code
LUWA Dataset: Learning Lithic Use-Wear Analysis on Microscopic Images
Most importantly, the LUWA dataset provides an underexplored opportunity for vision and learning communities and complements existing image classification problems on common objects.
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.
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
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.
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)
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.
