Search Results for author:
Found 28 papers, 13 papers with code
Txt2Img-MHN: Remote Sensing Image Generation from Text Using Modern Hopfield Networks
To better evaluate the realism and semantic consistency of the generated images, we further conduct zero-shot classification on real remote sensing data using the classification model trained on synthesized images.
Semi-Supervised Hyperspectral Image Classification Using a Probabilistic Pseudo-Label Generation Framework
The generated pseudo labels of our proposed framework can be fed to various DNNs to improve their generalization capacity.
Ranked #1 on
Semi-Supervised Image Classification
on Salinas
(using extra training data)
Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection
We make the multi-source landslide benchmark data (Landslide4Sense) and the tested DL models publicly available at \url{www. landslide4sense. org}, establishing an important resource for remote sensing, computer vision, and machine learning communities in studies of image classification in general and applications to landslide detection in particular.
Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives
Therefore, remote sensing image understanding often faces the problems of incomplete, inexact, and inaccurate supervised information, which will affect the breadth and depth of remote sensing applications.
HyDe: The First Open-Source, Python-Based, GPU-Accelerated Hyperspectral Denoising Package
Furthermore, we present a method for training DNNs for denoising HSIs which are not spatially related to the training dataset, i. e., training on ground-level HSIs for denoising HSIs with other perspectives including airborne, drone-borne, and space-borne.
Nonnegative-Constrained Joint Collaborative Representation with Union Dictionary for Hyperspectral Anomaly Detection
CR-based detectors approximate the image by a linear combination of background dictionaries and the coefficient matrix, and derive the detection map by utilizing recovery residuals.
Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark
Despite their simplicity, the proposed methods can generate transferable adversarial examples that deceive most of the state-of-the-art deep neural networks in both scene classification and semantic segmentation tasks with high success rates.
Consistency-Regularized Region-Growing Network for Semantic Segmentation of Urban Scenes with Point-Level Annotations
To this end, we further propose the consistency regularization strategy, where a base classifier and an expanded classifier are employed.
Asymmetric Hash Code Learning for Remote Sensing Image Retrieval
In this paper, we propose a novel deep hashing method, named asymmetric hash code learning (AHCL), for RSIR.
NFANet: A Novel Method for Weakly Supervised Water Extraction from High-Resolution Remote Sensing Imagery
Therefore, we study how to utilize point labels to extract water bodies and propose a novel method called the neighbor feature aggregation network (NFANet).
Unsupervised Data Fusion With Deeper Perspective: A Novel Multisensor Deep Clustering Algorithm
In this article, we propose a multisensor deep clustering (MDC) algorithm for the joint processing of multisource imaging data.
Fully Linear Graph Convolutional Networks for Semi-Supervised Learning and Clustering
This paper presents FLGC, a simple yet effective fully linear graph convolutional network for semi-supervised and unsupervised learning.
Large-Scale Hyperspectral Image Clustering Using Contrastive Learning
Specifically, we exploit a symmetric twin neural network comprised of a projection head with a dimensionality of the cluster number to conduct dual contrastive learning from a spectral-spatial augmentation pool.
Image Restoration for Remote Sensing: Overview and Toolbox
Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community.
Fusion of Dual Spatial Information for Hyperspectral Image Classification
In the spatial optimization stage, a pixel-level classifier is used to obtain the class probability followed by an extended random walker-based spatial optimization technique.
Hierarchical Sparse Subspace Clustering (HESSC): An Automatic Approach for Hyperspectral Image Analysis
In addition, in order to have a comparison with conventional clustering algorithms, HESSC’s performance is compared with K-means and FCM.
Hyperspectral Image Classification with Attention Aided CNNs
Specifically, a spectral attention sub-network and a spatial attention sub-network are proposed for spectral and spatial classification, respectively.
Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics
The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased.
Data Science in Economics
The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models.
Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep (Overview and Toolbox)
The advances in feature extraction have been inspired by two fields of research, including the popularization of image and signal processing as well as machine (deep) learning, leading to two types of feature extraction approaches named shallow and deep techniques.
Classification of Hyperspectral and LiDAR Data Using Coupled CNNs
For the feature-level fusion, three different fusion strategies are evaluated, including the concatenation strategy, the maximization strategy, and the summation strategy.
Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification
In this paper, we propose a solution to address this issue by locally extracting invariant features from hyperspectral imagery (HSI) in both spatial and frequency domains, using a method called invariant attribute profiles (IAPs).
Deep Learning for Hyperspectral Image Classification: An Overview
Specifically, we first summarize the main challenges of HSI classification which cannot be effectively overcome by traditional machine learning methods, and also introduce the advantages of deep learning to handle these problems.
Fusion of Heterogeneous Earth Observation Data for the Classification of Local Climate Zones
This paper proposes a novel framework for fusing multi-temporal, multispectral satellite images and OpenStreetMap (OSM) data for the classification of local climate zones (LCZs).
Cascaded Recurrent Neural Networks for Hyperspectral Image Classification
The first RNN layer is used to eliminate redundant information between adjacent spectral bands, while the second RNN layer aims to learn the complementary information from non-adjacent spectral bands.
Multisource and Multitemporal Data Fusion in Remote Sensing
The sharp and recent increase in the availability of data captured by different sensors combined with their considerably heterogeneous natures poses a serious challenge for the effective and efficient processing of remotely sensed data.
Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Tehran metropolitan area in Iran
In this study the SLEUTH model was used to model the urban expansion and predict the future possible behavior of the urban growth in Tehran.
Integration of LiDAR and Hyperspectral Data for Land-cover Classification: A Case Study
In this paper, an approach is proposed to fuse LiDAR and hyperspectral data, which considers both spectral and spatial information in a single framework.
