Recovering More Classes than Available Bands for Mixed Pixels in Remote Sensing

1998 ◽  
Author(s):  
M. Faraklioti ◽  
M. Petrou
Keyword(s):  
Remote Sensing ◽  
Mixed Pixels

scholarly journals EXTRACTING BUILT-UP FEATURES IN COMPLEX BIOPHYSICAL ENVIRONMENTS BY USING A LANDSAT BANDS RATIO

2020 ◽  
Vol XLIV-M-2-2020 ◽  
pp. 79-85
Author(s):  
A. H. Ngandam Mfondoum ◽  
P. G. Gbetkom ◽  
R. Cooper ◽  
S. Hakdaoui ◽  
M. B. Mansour Badamassi
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Kappa Coefficient ◽  
Landsat 8 ◽  
Experimental Site ◽  
Soil Features ◽  
Mixed Pixels ◽  
Shortwave Infrared ◽  
Normalization Process

Abstract. This paper addresses the remote sensing challenging field of urban mixed pixels on a medium spatial resolution satellite data. The tentatively named Normalized Difference Built-up and Surroundings Unmixing Index (NDBSUI) is proposed by using Landsat-8 Operational Land Imager (OLI) bands. It uses the Shortwave Infrared 2 (SWIR2) as the main wavelength, the SWIR1 with the red wavelengths, for the built-up extraction. A ratio is computed based on the normalization process and the application is made on six cities with different urban and environmental characteristics. The built-up of the experimental site of Yaoundé is extracted with an overall accuracy of 95.51% and a kappa coefficient of 0.90. The NDBSUI is validated over five other sites, chosen according to Cameroon’s bioclimatic zoning. The results are satisfactory for the cities of Yokadouma and Kumba in the bimodal and monomodal rainfall zones, where overall accuracies are up to 98.9% and 97.5%, with kappa coefficients of 0.88 and 0.94 respectively, although these values are close to those of three other indices. However, in the cities of Foumban, Ngaoundéré and Garoua, representing the western highlands, the high Guinea savannah and the Sudano-sahelian zones where built-up is more confused with soil features, overall accuracies of 97.06%, 95.29% and 74.86%, corresponding to 0.918, 0.89 and 0.42 kappa coefficients were recorded. Difference of accuracy with EBBI, NDBI and UI are up to 31.66%, confirming the NDBSUI efficiency to automate built-up extraction and unmixing from surrounding noises with less biases.

scholarly journals Estimating daily evapotranspiration based on a model of evaporative fraction (EF) for mixed pixels

2019 ◽  
Vol 23 (2) ◽  
pp. 949-969
Author(s):  
Fugen Li ◽  
Xiaozhou Xin ◽  
Zhiqing Peng ◽  
Qinhuo Liu
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Heihe River ◽  
Natural Ecosystems ◽  
Evaporative Fraction ◽  
Mixed Pixel ◽  
Mixed Pixels

Abstract. Currently, applications of remote sensing evapotranspiration (ET) products are limited by the coarse resolution of satellite remote sensing data caused by land surface heterogeneities and the temporal-scale extrapolation of the instantaneous latent heat flux (LE) based on satellite overpass time. This study proposes a simple but efficient model (EFAF) for estimating the daily ET of remotely sensed mixed pixels using a model of the evaporative fraction (EF) and area fraction (AF) to increase the accuracy of ET estimate over heterogeneous land surfaces. To accomplish this goal, we derive an equation for calculating the EF of mixed pixels based on two key hypotheses. Hypothesis 1 states that the available energy (AE) of each sub-pixel is approximately equal to that of any other sub-pixels in the same mixed pixel within an acceptable margin of error and is equivalent to the AE of the mixed pixel. This approach simplifies the equation, and uncertainties and errors related to the estimated ET values are minor. Hypothesis 2 states that the EF of each sub-pixel is equal to that of the nearest pure pixel(s) of the same land cover type. This equation is designed to correct spatial-scale errors for the EF of mixed pixels; it can be used to calculate daily ET from daily AE data. The model was applied to an artificial oasis located in the midstream area of the Heihe River using HJ-1B satellite data with a 300 m resolution. The results generated before and after making corrections were compared and validated using site data from eddy covariance systems. The results show that the new model can significantly improve the accuracy of daily ET estimates relative to the lumped method; the coefficient of determination (R2) increased to 0.82 from 0.62, the root mean square error (RMSE) decreased to 1.60 from 2.47 MJ m−2(decreased approximately to 0.64 from 0.99 mm) and the mean bias error (MBE) decreased from 1.92 to 1.18 MJ m−2 (decreased from approximately 0.77 to 0.47 mm). It is concluded that EFAF can reproduce daily ET with reasonable accuracy; can be used to produce the ET product; and can be applied to hydrology research, precision agricultural management and monitoring natural ecosystems in the future.

scholarly journals Remote-sensing algorithm for surface evapotranspiration considering landscape and statistical effects on mixed-pixels

2016 ◽  
Author(s):  
Z. Q. Peng ◽  
X. Z. Xin ◽  
J. J. Jiao ◽  
T. Zhou ◽  
Q. H. Liu
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Input Parameter ◽  
Surface Heterogeneity ◽  
Mixed Pixel ◽  
Flux Aggregation ◽  
Mixed Pixels ◽  
Land Types ◽  
Statistical Effects ◽  
Remote Sensing Algorithm

Abstract. Evapotranspiration (ET) plays an important role in surface-atmosphere interactions. Remote sensing has long been identified as a technology that is capable of monitoring ET. However, spatial problems greatly affect the accuracy of ET retrievals by satellite. The objective of this paper is to reduce the spatial-scale uncertainty produced by surface heterogeneity using Chinese HJ-1B data. Two upscaling schemes with area-weighting aggregation for different steps and variables were applied. One scheme is input parameter upscaling (IPUS), which refers to parameter aggregation, and the other is temperature sharpening and flux aggregation (TSFA). Footprint validation results show that TSFA is more accurate and less uncertain than IPUS, and additional analysis shows that TSFA can capture land surface heterogeneities and integrate the effect of overlooked land types in the mixed pixel.

Remote Sensing Image Classification Using Fuzzy-PSO Hybrid Approach

2015 ◽  
pp. 435-468
Author(s):  
Anasua Sarkar ◽  
Rajib Das
Keyword(s):  
Remote Sensing ◽  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Hybrid Approach ◽  
Particle Swarm ◽  
Ground Truth ◽  
Optimization Methods ◽  
Pixel Classification ◽  
Swarm Optimization ◽  
Mixed Pixels

Pixel classification among overlapping land cover regions in remote sensing imagery is a challenging task. Detection of uncertainty and vagueness are always key features for classifying mixed pixels. This chapter proposes an approach for pixel classification using hybrid approach of Fuzzy C-Means and Particle Swarm Optimization methods. This new unsupervised algorithm is able to identify clusters utilizing particle swarm optimization based on fuzzy membership values. This approach addresses overlapping regions in remote sensing images by uncertainties using fuzzy set membership values. PSO is a population-based stochastic optimization technique inspired from the social behavior of bird flocks. The authors demonstrate the algorithm for segmenting a LANDSAT image of Shanghai. The newly developed algorithm is compared with FCM and K-Means algorithms. The new algorithm-generated clustered regions are verified with the available ground truth knowledge. The validity and statistical analysis are performed to demonstrate the superior performance of the new algorithm with K-Means and FCM algorithms.

scholarly journals A Novel Change Detection Approach Based on Spectral Unmixing from Stacked Multitemporal Remote Sensing Images with a Variability of Endmembers

2021 ◽  
Vol 13 (13) ◽  
pp. 2550
Author(s):  
Ke Wu ◽  
Tao Chen ◽  
Ying Xu ◽  
Dongwei Song ◽  
Haishan Li
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Change Detection ◽  
Kappa Coefficient ◽  
Spectral Unmixing ◽  
Remote Sensing Images ◽  
Unified Framework ◽  
Thematic Map ◽  
Detection Approach ◽  
Mixed Pixels

Due to the high temporal repetition rates, median/low spatial resolution remote sensing images are the main data source of change detection (CD). It is worth noting that they contain a large number of mixed pixels, which makes adequately capturing the details in the resulting thematic map challenging. The spectral unmixing (SU) method is a potential solution to this problem, as it decomposes mixed pixels into a set of fractions of the land covers. However, there are accumulated errors in the fractional difference images, which lead to a poor change detection results. Meanwhile, the spectra variation of the endmember and the heterogeneity of the land cover materials cannot be fully considered in the traditional framework. In order to solve this problem, a novel change detection approach with image stacking and dividing based on spectral unmixing while considering the variability of endmembers (CD_SDSUVE) was proposed in this paper. Firstly, the remote sensing images at different times were stacked into a unified framework. After that, several patch images were produced by dividing the stacked images so that the similar endmembers according to each land cover can be completely extracted and compared. Finally, the multiple endmember spectral mixture analysis (MESMA) is performed, and the abundant images were combined to produce the entire change detection thematic map. This proposed algorithm was implemented and compared to four relevant state-of-the-art methods on three experimental data, whereby the results confirmed that it effectively improved the accuracy. In the simulated data, the overall accuracy (OA) and Kappa coefficient values were 99.61% and 0.99. In the two real data, the maximum of OA were acquired with 93.26% and 80.85%, which gained 14.88% and 13.42% over the worst results at most. Meanwhile, the Kappa coefficient value was consistent with the OA.

scholarly journals MULTI-MODAL REMOTE SENSING DATA FUSION FRAMEWORK

2017 ◽  
Vol XLII-4/W2 ◽  
pp. 85-89
Author(s):  
M. A. A. Ghaffar ◽  
T. T. Vu ◽  
T. H. Maul
Keyword(s):  
Remote Sensing ◽  
Data Fusion ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Machine Learning Techniques ◽  
Landsat 8 ◽  
Classification Problems ◽  
Learning Techniques ◽  
Level Of Details ◽  
Mixed Pixels

The inconsistency between the freely available remote sensing datasets and crowd-sourced data from the resolution perspective forms a big challenge in the context of data fusion. In classical classification problems, crowd-sourced data are represented as points that may or not be located within the same pixel. This discrepancy can result in having mixed pixels that could be unjustly classified. Moreover, it leads to failure in retaining sufficient level of details from data inferences. In this paper we propose a method that can preserve detailed inferences from remote sensing datasets accompanied with crowd-sourced data. We show that advanced machine learning techniques can be utilized towards this objective. The proposed method relies on two steps, firstly we enhance the spatial resolution of the satellite image using Convolutional Neural Networks and secondly we fuse the crowd-sourced data with the upscaled version of the satellite image. However, the covered scope in this paper is concerning the first step. Results show that CNN can enhance Landsat 8 scenes resolution visually and quantitatively.

scholarly journals Estimating Daily Evapotranspiration Based on a Model of Evapotranspiration Fraction (EF) for Mixed Pixels

2018 ◽  
Author(s):  
Fugen Li ◽  
Xiaozhou Xin ◽  
Zhiqing Peng ◽  
Qinhuo Liu
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Heihe River ◽  
Mixed Pixel ◽  
Before And After ◽  
Mixed Pixels

Abstract. Currently, applications of remote sensing evapotranspiration (ET) products are limited by the low resolution of satellite remote sensing data caused by land surface heterogeneities and the temporal scale extrapolation of the instantaneous latent heat flux (LE) based on satellite overpass time. This study proposes a simple but efficient model (EFAF) for estimating the daily ET of remotely sensed mixed pixels using a model of the evapotranspiration fraction (EF) and area fraction (AF). To accomplish this goal, we derive an equation for calculating the EF of mixed pixels based on two key hypotheses. Hypothesis 1 states that the available energy (AE) of each sub-pixel is approximately equal to that of any other sub-pixels in the same mixed pixel within an acceptable margin of bias and is equivalent to the AE of the mixed pixel. This approach simplifies the equation, and uncertainties and errors related to the estimated ET values are minor. Hypothesis 2 states that the EF of each sub-pixel is equal to that of the nearest pure pixel(s) of the same land cover type. This equation is designed to correct spatial scale errors for the EF of mixed pixels; it can be used to calculate daily ET from daily AE data. The model was applied to an artificial oasis located in the midstream area of the Heihe River using HJ-1B satellite data with a 300 m resolution. Results generated before and after making corrections were compared and validated using sites data from eddy covariance systems. The results show that the new model can significantly improve the accuracy of daily ET estimates relative to the lumped method; the coefficient of determination (R2) increased to 0.82 from 0.62, the root mean square error (RMSE) decreased to 1.60 from 2.47 MJ·m−2, and the mean bias error (MBE) decreased from 1.92 to 1.18 MJ·m−2.

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