Mixed Pixel Analysis Research Articles

Assessment of space-based tropical smouldering peatlands: mixed pixel analysis

Detection of tropical smouldering peatlands from space remains a challenge in a mixed pixel issue. This study aims to discriminate the mixed pixel containing smouldering fire resulting from Tropical Peatland Combustion Algorithm (ToPeCAl) applied to Landsat-8. The assessment of the smouldering pixel was conducted by applying a modification of the contextual test to select the smouldering pixel and then proceed it to linear spectral mixture analysis. The smouldering and burnt areas used for endmembers were determined from field observation. Our study areas span from South to West Kalimantan’s peatlands covering six path-rows of Landsat-8 in the fire seasons of 2018-2019. There were 53,224 candidate smouldering pixels from ToPeCAl which 80% of them passed the contextual test. Based on mixed pixel analysis, most of the selected smouldering pixels consisted of more than 0.79 of smouldering fraction. This was confirmed by the smoke features on the ground from the high spatial PlanetScope images acquired on the same day with Landsat-8. The actual false alarms pixel may consist of 0.74 of smouldering fraction and more than 0.20 of the burnt area or vegetated area fraction with no smoke features on the ground. Further research on smoke mapping or aerosol properties over burning peatland could enhance the performance of smouldering pixel detection.

Real-Time Spatiotemporal Spectral Unmixing of MODIS Images

Mixed pixels are a ubiquitous problem in remote sensing images. Spectral unmixing has been used widely for mixed pixel analysis. However, up to now, most spectral unmixing methods require endmembers and cannot consider fully intraclass spectral variation. The recently proposed spatiotemporal spectral unmixing (STSU) method copes with the aforementioned problems through exploitation of the available temporal information. However, this method requires coarse-to-fine spatial image pairs both before and after the prediction time and is, thus, not suitable for important real-time applications (i.e., where the fine spatial resolution data after the prediction time are unknown). In this article, we proposed a real-time STSU (RSTSU) method for real-time monitoring. RSTSU requires only a single coarse-to-fine spatial resolution image pair before, and temporally closest to, the prediction time, coupled with the coarse image at the prediction time, to extract samples automatically to train a learning model. By fully incorporating the multiscale spatiotemporal information, the RSTSU method inherits the key advantages of STSU; it does not need endmembers and can account for intraclass spectral variation. More importantly, RSTSU is suitable for real-time analysis and, thus, facilitates the timely monitoring of land cover changes. The effectiveness of the method was validated by experiments on four Moderate Resolution Imaging Spectroradiometer (MODIS) datasets. RSTSU utilizes and enriches the theory underpinning the advanced STSU method and enhances greatly the applicability of spectral unmixing for time-series data.

Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review

Reliable estimates of savanna vegetation constituents (i.e., woody and herbaceous vegetation) are essential as they are both responders and drivers of global change. The savanna is a highly heterogenous biome with high variability in land cover types while also being very dynamic at both temporal and spatial scales. To understand the spatial-temporal dynamics of savannas, using Earth Observation (EO) data for mixed-pixel analysis is crucial. Mixed pixel analysis provides detailed land cover data at a sub-pixel level which are essential for conservation purposes, understanding food supply for herbivores, quantifying environmental change, such as bush encroachment, and fuel availability essential for understanding fire dynamics, and for accurate estimation of savanna biomass. This review paper consulted 197 studies employing mixed-pixel analysis in savanna ecosystems. The review indicates that studies have so far attempted to resolve the savanna mixed-pixel issues by using mainly coarse resolution data, such as Terra-Aqua MODIS and AVHRR and medium resolution Landsat, to provide fractional cover data. Hence, there is a lack of spatio-temporal mixed-pixel analysis for savannas at high spatial resolutions. Methods used for mixed-pixel analysis include parametric and non-parametric methods which range from pixel-unmixing models, such as linear spectral mixture analysis (SMA), time series decomposition, empirical methods to link the green vegetation parameters with Vegetation Indices (VIs), and machine learning methods, such as regression trees (RT) and random forests (RF). Most studies were undertaken at local and regional scale, highlighting a research gap for savanna mixed pixel studies at national, continental, and global level. Parametric methods for modeling spatio-temporal mixed pixel analysis were preferred for coarse to medium resolution remote sensing data, while non-parametric methods were preferred for very high to high spatial resolution data. The review indicates a gap for long time series spatio-temporal mixed-pixel analysis of savannas using high resolution data at various scales. There is potential to harmonize the available low resolution EO data with new high-resolution sensors to provide long time series of the savanna mixed pixel, which, according to this review, is missing.

ミクセル解析を目的としたLandsat/OLIデータとMODISデータの重ね合わせにおける要求精度

Currently, many land observation satellites can be used in order to monitor natural environment. JAXA has a plan to launch new satellite GCOM-C1. SGLI is mounted on GCOM-C1, which can observe 380-12000nm spectral range in 250m sampling distance and 1150km swath. A mixed pixel analysis is a very important issue for an accurate environmental monitoring. Therefore, required accuracy of geometric transformation for GCOM-C1/SGLI should be understood. In this study, Landsat/OLI was used as reference data for geometric transform. The accuracy of superposing OLI on MODIS was evaluated by using simulated MODIS data. MODIS has a same ground sampling distance with SGLI. As the results, 1/5 pixel accuracy is required for mixed pixel analysis. Finally, Mixed pixel analysis was carried out real Landsat OLI and MODIS. The positional error of MODIS showed around 40-60m. The root mean square error between OLI and MODIS showed 12.6 (W/m2/sr/μm). It was bigger than simulation results. The reason might be come from transit time difference of satellite.

Study of high-dimensional fuzzy classification based on random forest algorithm

The spatial resolution of hyperspectral data is generally very low,the mixed pixels are extensively distributed,and hence fuzzy classification is commonly used in the mixed pixel analysis. As the accuracy of fuzzy classification is often limited by the feature dimensions and fuzzy samples selection,the random forest( RF)algorithm is put forward in this paper to select features and obtain fuzzy samples; in the low- dimensional feature space,fuzzy samples are used to make fuzzy classification. Fuzzy classification and RF are merged by using two-step classification,following the principle of unanimity assumption. Using different samples,different experimental areas and different partition optimization situations,the authors conducted three comparative experiments,and the results show that the method proposed in this paper solves the limitation of fuzzy classification and improves its accuracy. It is also proved that the classification accuracy of the method is robust for the original sample.

A Method for Multi-Spectral Remote Sensing Classification Based on Mixed Pixel Analysis

A Method for Multi-Spectral Remote Sensing Classification Based on Mixed Pixel Analysis

Low-bit rate exploitation-based lossy hyperspectral image compression

Hyperspectral image compression has become increasingly important in data exploitation because of enormous data volumes and high redundancy provided by hundreds of contiguous spectral channels. Since a hyperspectral image can be viewed as a 3-dimensional (3D) image cube, many efforts have been devoted to extending 2D image compression techniques to perform 3D image compression on hyperspectral image cubes. Unfortunately, some major issues generally encountered in hyperspectral data exploitation at low or very low-bit rate compression, for example, subpixels and mixed pixels which do not occur in traditional pure pixel-based image compression are often overlooked in such a 2D-to-3D compression. Accordingly, a direct application of 2D-to-3D compression techniques to hyperspectral image cubes without taking precaution may result in significant loss of crucial spectral information provided by subtle substances such as small objects, anomalies during low bit-rate lossy compression. This paper takes a rather different view by investigating lossy hyperspectral compression from a perspective of exploring spectral information, referred to as exploitation-based lossy compression and further develops spectral/spatial hyperspectral image compression to effectively preserve crucial and vital spectral information of objects which are generally missed by commonly used mean-squared error (MSE) or signal-to-noise ratio (SNR)-based compression techniques when lossy compression is performed at low bit rates. In order to demonstrate advantages of the proposed spectral/spatial compression approach applications of subpixel target detection and mixed pixel analysis are used for experiments for performance evaluation.

Near-lossless and lossy compression of imaging spectrometer data: comparison of information extraction performance

We investigate the ability to derive meaningful information from decompressed imaging spectrometer data. Hyperspectral images are compressed with near-lossless and lossy coding methods. Linear prediction between the bands is used in both cases. Each band is predicted by a previously transmitted band. The residual is formed by subtracting the prediction from the original data and then is compressed either with a near-lossless bit-plane coder or with the lossy JPEG2000 algorithm. We study the effects of these two types of compression on hyperspectral image processing such as mineral and vegetation content classification using whole- and mixed pixel analysis techniques. The results presented in this paper indicate that an efficient lossy coder outperforms near-lossless method in terms of its impact on final hyperspectral data applications.

ICA-Aided Mixed-Pixel Analysis of Hyperspectral Data in Agricultural Land

This letter proposes an independent component analysis (ICA)-aided mixed-pixel analysis of periodically distributed hyperspectral data in agricultural land. This method simultaneously estimates the pure spectra and coverage of endmembers, such as crop and soil, from mixed-pixel data which is inevitably included in images observed from high-altitude sensors. The method is effective for agricultural management because the change of observed mixed-pixel data is distinguished into a qualitative spectral one, due to chlorophyll quantity or crop variety, and the quantitative coverage due to growth stages. This method introduces a priori knowledge which is independent of the type of crop and effective in deriving a scaling factor for the independent component (IC), estimated from the ICA process. The fundamental investigation, using hyperspectral data obtained from a crane and an aircraft, shows the applicability of the method.

A new approach to mixed pixel classification of hyperspectral imagery based on extended morphological profiles

This paper presents a new approach to the analysis of hyperspectral images, a new class of image data that is mainly used in remote sensing applications. The method is based on the generalization of concepts from mathematical morphology to multi-channel imagery. A new vector organization scheme is described, and fundamental morphological vector operations are defined by extension. Theoretical definitions of extended morphological operations are used in the formal definition of the concept of extended morphological profile, which is used for multi-scale analysis of hyperspectral data. This approach is particularly well suited for the analysis of image scenes where most of the pixels collected by the sensor are characterized by their mixed nature, i.e. they are formed by a combination of multiple underlying responses produced by spectrally distinct materials. Experimental results demonstrate the applicability of the proposed technique in mixed pixel analysis of simulated and real hyperspectral data collected by the NASA/Jet Propulsion Laboratory Airborne Visible/Infrared Imaging Spectrometer and the DLR Digital Airborne (DAIS 7915) and Reflective Optics System Imaging Spectrometers. The proposed method works effectively in the presence of noise and low spatial resolution. A quantitative and comparative performance study with regards to other standard hyperspectral analysis methodologies reveals that the combined utilization of spatial and spectral information in the proposed technique produces classification results which are superior to those found by using the spectral information alone.