Number Of Mixed Pixels Research Articles

Land cover target mapping at subpixel scale for Landsat 8 OLI image by using multiscale-infrared information

ABSTRACT Land remote-sensing satellite (System, Landsat) 8 operational land imager (OLI) can provide land cover target mapping (LCTM) information. Due to the limitation of hardware and complexity of environment, Landsat 8 OLI image sometimes contains a large number of mixed pixels, which brings a challenge for LCTM. Super-resolution mapping (SM) handles the mixed pixels to obtain LCTM at subpixel scale. However, the scale information is single and the infrared information is not rich in the existing SM methods. In order to solve these issues, this paper proposes SM based on multiscale-infrared information (MII). There are two terms (i.e. multiscale and infrared terms) in MII. The multiscale term with multiscale information is produced through deep Laplacian pyramid network (DLPN), multiresolution segmentation, and extended random walker in turn. The infrared term with infrared information is derived by calculating the normalized difference target index (NDTI). The two terms are combined to generate a minimization term with multiscale-infrared information. Particle swarm optimization algorithm is applied to the minimization term to obtain the LCTM result. The experimental results on four Landsat 8 OLI images show that the MII provides better LCTM results than the traditional SM methods.

Spectral characteristics of talc and mineral abundance mapping in the Jahazpur Belt of Rajasthan, India using AVIRIS-NG data

ABSTRACT Spaceborne data with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Short Wave Infrared (SWIR) bands have been used worldwide in discriminating different clay minerals. However, due to wide bandwidth of ASTER sensors, it is difficult to distinguish between pure and impure variety of talc mineral. The present study highlights the efficacy of airborne hyperspectral data Airborne Visible/Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG) for spectral analysis in talc mineral in the Jahazpur Belt of Bhilwara district in Rajasthan, India. Due to its better spectral and spatial resolution, AVIRIS-NG is found to be extremely useful in identifying talc mineral in SWIR region. Talc is characterized by doublet shaped spectral pattern in AVIRIS-NG image with strong absorption dips at 2289.75 nm and 2314.80 nm which could not be picked up in ASTER image. Spectral matching of talc pixels is verified with the field spectra of samples and available spectral library of United States Geological Survey, Jet Propulsion Laboratory, and International Geological Correlation Programme. Variation in reflectance percentage of different talc pixels is attributed to variation in chemical composition of talc. However, a large number of mixed pixels with spectral pattern of both talc and kaolinite minerals have been identified in AVIRIS-NG image which are found to be an impure variety of talc. Whole-pixel to sub-pixel classification techniques with Spectral Angle Mapper, Spectral Feature Fitting, Mixture Tuned Matched Filtering, and Linear Spectral Unmixing (LSU) techniques have been applied for talc abundance mapping in Jahazpur area. LSU is found to be the most useful and accurate technique in classifying spectrally pure to impure variety of talc.

Optimized Kernel Minimum Noise Fraction Transformation for Hyperspectral Image Classification

This paper presents an optimized kernel minimum noise fraction transformation (OKMNF) for feature extraction of hyperspectral imagery. The proposed approach is based on the kernel minimum noise fraction (KMNF) transformation, which is a nonlinear dimensionality reduction method. KMNF can map the original data into a higher dimensional feature space and provide a small number of quality features for classification and some other post processing. Noise estimation is an important component in KMNF. It is often estimated based on a strong relationship between adjacent pixels. However, hyperspectral images have limited spatial resolution and usually have a large number of mixed pixels, which make the spatial information less reliable for noise estimation. It is the main reason that KMNF generally shows unstable performance in feature extraction for classification. To overcome this problem, this paper exploits the use of a more accurate noise estimation method to improve KMNF. We propose two new noise estimation methods accurately. Moreover, we also propose a framework to improve noise estimation, where both spectral and spatial de-correlation are exploited. Experimental results, conducted using a variety of hyperspectral images, indicate that the proposed OKMNF is superior to some other related dimensionality reduction methods in most cases. Compared to the conventional KMNF, the proposed OKMNF benefits significant improvements in overall classification accuracy.

Mixed pixel decomposition of mineral spectrum based on EMD-ICA method

Mineral spectrum is a mixed pixel consists of various mineral spectra. The types and contents of the component minerals can be obtained through mixed pixel decomposition based on EMD-ICA method. The independent component analysis (ICA) can decompose the mixed pixels, but requires that the number of mixed pixels should be no less than the number of end members. However, this requirement usually cannot be met in practice, due to limitations on measuring conditions. Besides, it is hard to find out the number of source signals in measurement signals in advance. To remedy this, the empirical mode decomposition (EMD) is used to obtain intrinsic mode functions (IMFs), which can be combined with the mixed pixel spectra as the input data matrix of ICA. With the EMD-ICA method, the estimate of the end member spectra can be separated from a single mixed pixel spectrum. Studies have shown that the higher the spectral content of end members in mixed pixels, the higher the quality of the estimates of end member spectra. Choosing different numbers of IMFs in ICA leads to different numbers of estimates of end member spectra. When the number of the estimates is less than the actual number of end member, the estimates obtained correspond to the end members with higher spectral content. By applying this method in quantitative analysis of mineral spectrum, we can obtain information about the end members it contains.

Snow evolution in a semi-arid mountainous area combining snow modelling and Landsat spectral mixture analysis

Abstract. This study proposes the use of both physically-distributed hydrological modelling in combination with satellite remote sensing images, to study the evolution of the snowpack in the Sierra Nevada mountains, in southern Spain. The snowmelt-accumulation module inside WiMMed (Watershed Integrated Management in Mediterranean Environment) hydrological model was employed, which includes the use of depletion curves to expand the energy and water balance equations over a grid representation. Snow maps obtained from spectral mixture analysis of Landsat images were used to evaluate this model at the study site. The results show a significant agreement between observed and simulated snow pixels in the area, which allows production of sequences of snow maps with greater resolution than the remote sensing images employed. However, some mismatches do appear at the boundaries of the snow area, mainly related to: (a) the great number of mixed pixels; and (b) the influence of the snow transport by wind.

IMPROVING LINEAR SPECTRAL UNMIXING THROUGH LOCAL ENDMEMBER DETECTION

Abstract. There are a considerable number of mixed pixels in remotely sensed images. Different sub-pixel analyses have been recently developed correspondingly. A well-known method is linear spectral unmixing which obtains an abundance of each endmember in a given pixel. This model assumes that each pixel is a linear combination of all endmembers in a scene. This assumption is not correct since each pixel can only be a composition of some surrounding endmembers. Even though, a fully mathematical technique is used for spectral analysis, the output of the model may not represent the physical nature of the objects over the pixel under test. In this regard, this paper proposes a Local Linear Spectral Unmixing which is based on neighbor pixels classes. Having classified the image, using a supervised classifier, it is scanned through a window of an appropriate size. For each pixel at the center of the window, the endmember matrix is formed only based on the majority classes existed in the window. Then the amount of each one is calculated. The LLSU method was evaluated on an AVIRIS data set collected from an agricultural area of northern Indiana. The results of the proposed method demonstrate a significant improvement in comparison with the LSU results. Moreover, due to the dimension reduction of the endmember matrix in this method, the computation time of the LLSU speeds up by three to eight times compared to the conventional Linear Spectral Unmixing method. As a result, the proposed method is efficient over the spectral unmixing tasks.

Subpixel land cover change mapping with multitemporal remote-sensed images at different resolution

Due to the lack of support for a high-resolution image in a short time, land cover change detection is always applied on the multitemporal remote-sensed images with different resolutions. The coarse-resolution image contains a large number of mixed pixels, which can seriously limit the utility of the change detection. Soft classification (SC) can be applied to improve this situation through deriving the abundances and generating the fractional change map, but it cannot provide the spatial distribution of the subpixels. Subpixel mapping (SPM) is a potential solution to resolve this problem, and is designed to use the proportions to obtain a sharpened thematic map at a subpixel scale. Based on this thought, the subpixel scale land cover change mapping result can be realized by integrating these two key techniques. However, in practice, there is a serious limitation to the detail and accuracy of the result, because when the scale factor between the different resolution images is large, the subpixel configuration is complex and the data volumes will be amplified. Moreover, with the high proportion of the changed area in the whole image, the change detection process at the subpixel level gets more difficult. The SPM technique is generally performed based only on the abundances of each and the spatial dependence assumption, so it cannot satisfy the demand. In order to overcome this shortcoming, several new reasonable subpixel scale change detection rules are defined in this paper, which dictate the land cover change map must be constructed according to the existing high-resolution image. The output from SC and prior information of the subpixel feature arrangement is applied into a modified cellular automata (CA) model, which can be regarded as a more reasonable tool to monitor the subpixel changes to resolve the big-data problem. Two experiments are performed and the results prove that the proposed method can effectively improve the accuracy of the change detection maps of the spatial distribution in a subpixel scale.

Super-resolution land-cover mapping based on the selective endmember spectral mixture model in hyperspectral imagery

Hyperspectral imagery contains a large number of mixed pixels, which limits its utility. Super-resolution mapping is a potential solution to this problem, designed to use the proportion of land covers to obtain a sharpened thematic map with higher resolution. Endmember is a fundamental variable in the process, which is a critical issue for decomposing the mixed pixels and sharpening the subpixel level images. In most cases, the forms of the endmember combination in diverse pixels are very distinct. However, traditional soft classification methods neglect this point and model endmembers as fixed composition entities. Due to the reliance on this flawed spectral mixture model, the super-resolution mapping is unable to represent detail in the following result image precisely and effectively. In this work, therefore, endmember variability is considered, focusing on identifying the most suitable form of the endmember combination. This issue is addressed by applying a new selective endmember spectral mixture (SESM) model, which allows the endmember number and type to vary at a per pixel level, and then super-resolution mapping can be subsequently performed according to the produced spectral abundances. Two different types of hyperspectral data are used in our experiments. First, the SESM model is tested individually for validation of its applicability. Then the complete algorithm integrating SESM and super-resolution mapping based on a back-propagation neural network is evaluated. It showed that a more accurate endmember combination in the parent pixel results in a finer representation image. The experimental results prove that the proposed algorithm can effectively improve the accuracy of the super-resolution mapping results compared to the traditional method.

Analysis of the relationship between spatial pattern and spectral detectability of areas burned in southern Africa using satellite data

Fires in Africa affect atmospheric emissions and carbon sequestration, landscape patterns, and regional and global climatic conditions. Studies of these effects require accurate estimation of the extent of measurable fire events. The goal of this study was to assess the influence of burned area spatial patterns on the spectral detectability of burned areas. Six Landsat‐7 ETM+ images from the southern Africa were used for burned area mapping and spatial pattern analysis, while contemporaneous MODIS 500 m spatial resolution images were used to measure the spectral detectability of burned areas. Using a 15 by 15 km sample quadrats analysis, we showed that above a burned area proportion threshold of approximately 0.5 the spectral detectability of burned areas increase due to the decrease in the number of mixed pixels. This was spatially related to the coalescence of burned patches and the decrease in the total burned area perimeter. Simple burned area shapes were found at the Botswana site, where the absence of tree cover and the presence of bright surfaces (soil and dry grass) enhanced the spectral contrast of the burned surfaces, thus enabling better estimates of burned area extent. At the Zambia and Congo sites, landscape fragmentation due to human activity and the presence of a tree vegetation layer, respectively, contribute to the presence of small burned area patches, which may remain undetectable using moderate spatial resolution satellite imagery, leading to less accurate burned area extent estimates.

High spatial resolution hyperspectral mapping of in-stream habitats, depths, and woody debris in mountain streams

This article evaluates the potential of 1-m resolution, 128-band hyperspectral imagery for mapping in-stream habitats, depths, and woody debris in third- to fifth-order streams in the northern Yellowstone region. Maximum likelihood supervised classification using principal component images provided overall classification accuracies for in-stream habitats (glides, riffles, pools, and eddy drop zones) ranging from 69% for third-order streams to 86% for fifth-order streams. This scale dependency of classification accuracy was probably driven by the greater proportion of transitional boundary areas in the smaller streams. Multiple regressions of measured depths ( y) versus principal component scores ( x 1, x 2,…, x n ) generated R 2 values ranging from 67% for high-gradient riffles to 99% for glides in a fifth-order reach. R 2 values were lower in third-order reaches, ranging from 28% for runs and glides to 94% for pools. The less accurate depth estimates obtained for smaller streams probably resulted from the relative increase in the number of mixed pixels, where a wide range of depths and surface turbulence occurred within a single pixel. Matched filter (MF) mapping of woody debris generated overall accuracies of 83% in the fifth-order Lamar River. Accuracy figures for the in-stream habitat and wood mapping may have been misleadingly low because the fine-resolution imagery captured fine-scale variations not mapped by field teams, which in turn generated false “misclassifications” when the image and field maps were compared. The use of high spatial resolution hyperspectral (HSRH) imagery for stream mapping is limited by the need for clear water to measure depth, by any tree cover obscuring the stream, and by the limited availability of airborne hyperspectral sensors. Nonetheless, the high accuracies achieved in northern Yellowstone streams indicate that HSRH imagery can be a powerful tool for watershed-wide mapping, monitoring, and modeling of streams.