Search Results for author: Pedram Ghamisi

Found 28 papers, 13 papers with code

Txt2Img-MHN: Remote Sensing Image Generation from Text Using Modern Hopfield Networks

1 code implementation8 Aug 2022 Yonghao Xu, Weikang Yu, Pedram Ghamisi, Michael Kopp, Sepp Hochreiter

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.

Text to image generation Text-to-Image Generation +1

Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection

no code implementations1 Jun 2022 Omid Ghorbanzadeh, Yonghao Xu, Pedram Ghamisi, Michael Kopp, David Kreil

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.

Image Classification

Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives

no code implementations18 Apr 2022 Jun Yue, Leyuan Fang, Pedram Ghamisi, Weiying Xie, Jun Li, Jocelyn Chanussot, Antonio J Plaza

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.

Change Detection Image Classification +3

HyDe: The First Open-Source, Python-Based, GPU-Accelerated Hyperspectral Denoising Package

1 code implementation14 Apr 2022 Daniel Coquelin, Behnood Rasti, Markus Götz, Pedram Ghamisi, Richard Gloaguen, Achim Streit

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.

Hyperspectral Image Denoising Image Denoising

Nonnegative-Constrained Joint Collaborative Representation with Union Dictionary for Hyperspectral Anomaly Detection

no code implementations18 Mar 2022 Shizhen Chang, Pedram Ghamisi

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.

Anomaly Detection

Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark

1 code implementation14 Feb 2022 Yonghao Xu, Pedram Ghamisi

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.

Adversarial Attack Scene Classification +1

Consistency-Regularized Region-Growing Network for Semantic Segmentation of Urban Scenes with Point-Level Annotations

1 code implementation8 Feb 2022 Yonghao Xu, Pedram Ghamisi

To this end, we further propose the consistency regularization strategy, where a base classifier and an expanded classifier are employed.

Semantic Segmentation

Asymmetric Hash Code Learning for Remote Sensing Image Retrieval

1 code implementation15 Jan 2022 Weiwei Song, Zhi Gao, Renwei Dian, Pedram Ghamisi, Yongjun Zhang, Jón Atli Benediktsson

In this paper, we propose a novel deep hashing method, named asymmetric hash code learning (AHCL), for RSIR.

Image Retrieval

NFANet: A Novel Method for Weakly Supervised Water Extraction from High-Resolution Remote Sensing Imagery

no code implementations10 Jan 2022 Ming Lu, Leyuan Fang, Muxing Li, Bob Zhang, Yi Zhang, Pedram Ghamisi

Therefore, we study how to utilize point labels to extract water bodies and propose a novel method called the neighbor feature aggregation network (NFANet).

Fully Linear Graph Convolutional Networks for Semi-Supervised Learning and Clustering

1 code implementation15 Nov 2021 Yaoming Cai, Zijia Zhang, Zhihua Cai, Xiaobo Liu, Yao Ding, Pedram Ghamisi

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

1 code implementation15 Nov 2021 Yaoming Cai, Zijia Zhang, Yan Liu, Pedram Ghamisi, Kun Li, Xiaobo Liu, Zhihua Cai

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.

Contrastive Learning Online Clustering +1

Image Restoration for Remote Sensing: Overview and Toolbox

no code implementations1 Jul 2021 Benhood Rasti, Yi Chang, Emanuele Dalsasso, Loïc Denis, Pedram Ghamisi

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.

Image Restoration

Fusion of Dual Spatial Information for Hyperspectral Image Classification

1 code implementation23 Oct 2020 Puhong Duan, Pedram Ghamisi, Xudong Kang, Behnood Rasti, Shutao Li, Richard Gloaguen

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.

Classification General Classification +1

Hyperspectral Image Classification with Attention Aided CNNs

1 code implementation25 May 2020 Renlong Hang, Zhu Li, Qingshan Liu, Pedram Ghamisi, Shuvra S. Bhattacharyya

Specifically, a spectral attention sub-network and a spatial attention sub-network are proposed for spectral and spatial classification, respectively.

Classification Classification Of Hyperspectral Images +2

Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

no code implementations21 Mar 2020 Amir Mosavi, Pedram Ghamisi, Yaser Faghan, Puhong Duan

The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased.

reinforcement-learning

Data Science in Economics

no code implementations19 Mar 2020 Saeed Nosratabadi, Amir Mosavi, Puhong Duan, Pedram Ghamisi

The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models.

Marketing

Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep (Overview and Toolbox)

1 code implementation5 Mar 2020 Behnood Rasti, Danfeng Hong, Renlong Hang, Pedram Ghamisi, Xudong Kang, Jocelyn Chanussot, Jon Atli Benediktsson

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.

General Classification Hyperspectral Image Classification

Classification of Hyperspectral and LiDAR Data Using Coupled CNNs

no code implementations4 Feb 2020 Renlong Hang, Zhu Li, Pedram Ghamisi, Danfeng Hong, Guiyu Xia, Qingshan Liu

For the feature-level fusion, three different fusion strategies are evaluated, including the concatenation strategy, the maximization strategy, and the summation strategy.

Classification General Classification

Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification

no code implementations18 Dec 2019 Danfeng Hong, Xin Wu, Pedram Ghamisi, Jocelyn Chanussot, Naoto Yokoya, Xiao Xiang Zhu

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).

General Classification Hyperspectral Image Classification

Deep Learning for Hyperspectral Image Classification: An Overview

no code implementations26 Oct 2019 Shutao Li, Weiwei Song, Leyuan Fang, Yushi Chen, Pedram Ghamisi, Jón Atli Benediktsson

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.

BIG-bench Machine Learning Classification +2

Fusion of Heterogeneous Earth Observation Data for the Classification of Local Climate Zones

no code implementations29 May 2019 Guichen Zhang, Pedram Ghamisi, Xiao Xiang Zhu

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).

Classification General Classification

Cascaded Recurrent Neural Networks for Hyperspectral Image Classification

no code implementations28 Feb 2019 Renlong Hang, Qingshan Liu, Danfeng Hong, Pedram Ghamisi

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.

Classification General Classification +1

Multisource and Multitemporal Data Fusion in Remote Sensing

no code implementations19 Dec 2018 Pedram Ghamisi, Behnood Rasti, Naoto Yokoya, Qunming Wang, Bernhard Hofle, Lorenzo Bruzzone, Francesca Bovolo, Mingmin Chi, Katharina Anders, Richard Gloaguen, Peter M. Atkinson, Jon Atli Benediktsson

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

no code implementations3 Aug 2017 Shaghayegh Kargozar Nahavandya, Lalit Kumar, Pedram Ghamisi

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

no code implementations9 Jul 2017 Pedram Ghamisi, Gabriele Cavallaro, Dan, Wu, Jon Atli Benediktsson, Antonio Plaza

In this paper, an approach is proposed to fuse LiDAR and hyperspectral data, which considers both spectral and spatial information in a single framework.

General Classification

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