scholarly journals A Novel Classification Optimization Approach Integrating Class Adaptive MRF and Fuzzy Local Information for High Spatial Resolution Multispectral Imagery

2018 ◽  
Vol 8 (10) ◽  
pp. 1792 ◽  
Author(s):  
Yuejin Zhou ◽  
Hua Zhang ◽  
Xiaoding Xu ◽  
Mingpeng Li ◽  
Lihui Zheng ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Energy Function ◽  
High Spatial Resolution ◽  
Local Information ◽  
Data Sets ◽  
Optimization Approach ◽  
Spatial Dependency ◽  
Multispectral Imagery ◽  
Iterated Conditional Modes ◽  
Class Labels

This paper develops a novel classification optimization approach integrating class adaptive Markov Random Field (MRF) and fuzzy local information (CAMRF-FLI) for high spatial resolution multispectral imagery (HSRMI). Firstly, the raw classification results, including initial fuzzy memberships and class labels of every pixel, are achieved by a pixel-wise classification method for a given image. Secondly, the class adaptive MRF-based data energy function is developed to integrate class spatial dependency information. Thirdly, a novel spatial energy function integrating fuzzy local information is constructed. Finally, based on the total of data and spatial energies, the raw classification map is regularized by a global minimization of the energy function using its iterated conditional modes (ICM). The effectiveness of CAMRF-FLI is performed by two data sets. The results indicate it can refine the classification map in homogeneous areas, meanwhile, reduce most of the edge blurring artifact, and improve the classification accuracy compared with some conventional approaches.

scholarly journals High-resolution mapping of the NO<sub>2</sub> spatial distribution over Belgian urban areas based on airborne APEX remote sensing

2017 ◽  
Vol 10 (5) ◽  
pp. 1665-1688 ◽  
Author(s):  
Frederik Tack ◽  
Alexis Merlaud ◽  
Marian-Daniel Iordache ◽  
Thomas Danckaert ◽  
Huan Yu ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Urban Areas ◽  
High Spatial Resolution ◽  
European Space Agency ◽  
Correlation Coefficients ◽  
Time Frame ◽  
Quantitative Information ◽  
Data Sets ◽  
Vertical Column

Abstract. We present retrieval results of tropospheric nitrogen dioxide (NO2) vertical column densities (VCDs), mapped at high spatial resolution over three Belgian cities, based on the DOAS analysis of Airborne Prism EXperiment (APEX) observations. APEX, developed by a Swiss-Belgian consortium on behalf of ESA (European Space Agency), is a pushbroom hyperspectral imager characterised by a high spatial resolution and high spectral performance. APEX data have been acquired under clear-sky conditions over the two largest and most heavily polluted Belgian cities, i.e. Antwerp and Brussels on 15 April and 30 June 2015. Additionally, a number of background sites have been covered for the reference spectra. The APEX instrument was mounted in a Dornier DO-228 aeroplane, operated by Deutsches Zentrum für Luft- und Raumfahrt (DLR). NO2 VCDs were retrieved from spatially aggregated radiance spectra allowing urban plumes to be resolved at the resolution of 60  ×  80 m2. The main sources in the Antwerp area appear to be related to the (petro)chemical industry while traffic-related emissions dominate in Brussels. The NO2 levels observed in Antwerp range between 3 and 35  ×  1015 molec cm−2, with a mean VCD of 17.4 ± 3.7  ×  1015 molec cm−2. In the Brussels area, smaller levels are found, ranging between 1 and 20  ×  1015 molec cm−2 and a mean VCD of 7.7 ± 2.1  ×  1015 molec cm−2. The overall errors on the retrieved NO2 VCDs are on average 21 and 28 % for the Antwerp and Brussels data sets. Low VCD retrievals are mainly limited by noise (1σ slant error), while high retrievals are mainly limited by systematic errors. Compared to coincident car mobile-DOAS measurements taken in Antwerp and Brussels, both data sets are in good agreement with correlation coefficients around 0.85 and slopes close to unity. APEX retrievals tend to be, on average, 12 and 6 % higher for Antwerp and Brussels, respectively. Results demonstrate that the NO2 distribution in an urban environment, and its fine-scale variability, can be mapped accurately with high spatial resolution and in a relatively short time frame, and the contributing emission sources can be resolved. High-resolution quantitative information about the atmospheric NO2 horizontal variability is currently rare, but can be very valuable for (air quality) studies at the urban scale.

scholarly journals PWNet: An Adaptive Weight Network for the Fusion of Panchromatic and Multispectral Images

2020 ◽  
Vol 12 (17) ◽  
pp. 2804
Author(s):  
Junmin Liu ◽  
Yunqiao Feng ◽  
Changsheng Zhou ◽  
Chunxia Zhang
Keyword(s):  
Neural Network ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
State Of The Art ◽  
Spectral Information ◽  
Data Sets ◽  
Multispectral Images ◽  
Image Prior ◽  
Adaptive Weight ◽  
Component Substitution

Pansharpening is a typical image fusion problem, which aims to produce a high resolution multispectral (HRMS) image by integrating a high spatial resolution panchromatic (PAN) image with a low spatial resolution multispectral (MS) image. Prior arts have used either component substitution (CS)-based methods or multiresolution analysis (MRA)-based methods for this propose. Although they are simple and easy to implement, they usually suffer from spatial or spectral distortions and could not fully exploit the spatial and/or spectral information existed in PAN and MS images. By considering their complementary performances and with the goal of combining their advantages, we propose a pansharpening weight network (PWNet) to adaptively average the fusion results obtained by different methods. The proposed PWNet works by learning adaptive weight maps for different CS-based and MRA-based methods through an end-to-end trainable neural network (NN). As a result, the proposed PWN inherits the data adaptability or flexibility of NN, while maintaining the advantages of traditional methods. Extensive experiments on data sets acquired by three different kinds of satellites demonstrate the superiority of the proposed PWNet and its competitiveness with the state-of-the-art methods.

Terrain classification in urban wetlands with high-spatial-resolution multispectral imagery

2003 ◽  
Author(s):  
Richard C. Olsen ◽  
Jamada J. Garner ◽  
Eric J. Van Dyke
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Urban Wetlands ◽  
Multispectral Imagery ◽  
Terrain Classification

Spatial-Attraction-Based Markov Random Field Approach for Classification of High Spatial Resolution Multispectral Imagery

2014 ◽  
Vol 11 (2) ◽  
pp. 489-493 ◽  
Author(s):  
Hua Zhang ◽  
Wenzhong Shi ◽  
Yunjia Wang ◽  
Ming Hao ◽  
Zelang Miao
Keyword(s):  
Random Field ◽  
Spatial Resolution ◽  
Markov Random Field ◽  
High Spatial Resolution ◽  
Multispectral Imagery ◽  
Field Approach ◽  
Markov Random ◽  
Spatial Attraction
Sign in / Sign up

Export Citation Format

Share Document