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Found 18 papers, 2 papers with code
S$^2$Mamba: A Spatial-spectral State Space Model for Hyperspectral Image Classification
In S$^2$Mamba, two selective structured state space models through different dimensions are designed for feature extraction, one for spatial, and the other for spectral, along with a spatial-spectral mixture gate for optimal fusion.
SpectralGPT: Spectral Remote Sensing Foundation Model
The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner.
Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances
In this era of rapid technical evolution, this review aims to present a comprehensive review of the recent achievements in deep learning based RSOD methods.
Guided Hybrid Quantization for Object detection in Multimodal Remote Sensing Imagery via One-to-one Self-teaching
More concretely, we first design a structure called guided quantization self-distillation (GQSD), which is an innovative idea for realizing lightweight through the synergy of quantization and distillation.
Ranked #1 on
Object Detection
on VEDAI
Pair-Relationship Modeling for Latent Fingerprint Recognition
Latent fingerprints are important for identifying criminal suspects.
Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review
Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI).
Dual-Stage Approach Toward Hyperspectral Image Super-Resolution
Hyperspectral image produces high spectral resolution at the sacrifice of spatial resolution.
Attention mechanism-based generative adversarial networks for cloud removal in Landsat images
First, attention maps of the input cloudy images are generated to extract the cloud distributions and features through an attentive recurrent network.
Fusion of PCA and Segmented-PCA Domain Multiscale 2-D-SSA for Effective Spectral-Spatial Feature Extraction and Data Classification in Hyperspectral Imagery
As hyperspectral imagery (HSI) contains rich spectral and spatial information, a novel principal component analysis (PCA) and segmented-PCA (SPCA)-based multiscale 2-D-singular spectrum analysis (2-D-SSA) fusion method is proposed for joint spectral–spatial HSI feature extraction and classification.
Multi-Attention Generative Adversarial Network for Remote Sensing Image Super-Resolution
The main body of the generator contains two blocks; one is the pyramidal convolution in the residual-dense block (PCRDB), and the other is the attention-based upsample (AUP) block.
An Attention-Fused Network for Semantic Segmentation of Very-High-Resolution Remote Sensing Imagery
Semantic segmentation is an essential part of deep learning.
Pseudo Supervised Solar Panel Mapping based on Deep Convolutional Networks with Label Correction Strategy in Aerial Images
First, a convolutional neural network is trained with positive and negative samples with image-level labels.
Online Structured Sparsity-based Moving Object Detection from Satellite Videos
O-LSD reformulates the batch-based low-rank matrix decomposition with the structured sparse penalty to its equivalent frame-wise separable counterpart, which then defines a stochastic optimization problem for online subspace basis estimation.
Error Bounded Foreground and Background Modeling for Moving Object Detection in Satellite Videos
It is integrated with structured sparsity regularization to achieve background subtraction in the developed method of Low-rank and Structured Sparse Decomposition (LSD).
Conditional Random Field and Deep Feature Learning for Hyperspectral Image Segmentation
Image segmentation is considered to be one of the critical tasks in hyperspectral remote sensing image processing.
Effective Sequential Classifier Training for SVM-based Multitemporal Remote Sensing Image Classification
In this study, a SVM-based Sequential Classifier Training (SCT-SVM) approach is proposed for multitemporal remote sensing image classification.
A New Path to Construct Parametric Orientation Field: Sparse FOMFE Model and Compressed Sparse FOMFE Model
Orientation field, representing the fingerprint ridge structure direction, plays a crucial role in fingerprint-related image processing tasks.
A Fully Automated Latent Fingerprint Matcher with Embedded Self-learning Segmentation Module
Latent fingerprint has the practical value to identify the suspects who have unintentionally left a trace of fingerprint in the crime scenes.
