Yihang Zhang

Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band

Monitoring open water bodies accurately is an important and basic application in remote sensing. Various water body mapping approaches have been developed to extract water bodies from multispectral images. The method based on the spectral water index, especially the Modified Normalized Difference Water Index (MDNWI) calculated from the green and Shortwave-Infrared (SWIR) bands, is one of the most popular methods. The recently launched Sentinel-2 satellite can provide fine spatial resolution multispectral images. This new dataset is potentially of important significance for regional water bodies’ mapping, due to its free access and frequent revisit capabilities. It is noted that the green and SWIR bands of Sentinel-2 have different spatial resolutions of 10 m and 20 m, respectively. Straightforwardly, MNDWI can be produced from Sentinel-2 at the spatial resolution of 20 m, by upscaling the 10-m green band to 20 m correspondingly. This scheme, however, wastes the detailed information available at the 10-m resolution. In this paper, to take full advantage of the 10-m information provided by Sentinel-2 images, a novel 10-m spatial resolution MNDWI is produced from Sentinel-2 images by downscaling the 20-m resolution SWIR band to 10 m based on pan-sharpening. Four popular pan-sharpening algorithms, including Principle Component Analysis (PCA), Intensity Hue Saturation (IHS), High Pass Filter (HPF) and A Trous Wavelet Transform (ATWT), were applied in this study. The performance of the proposed method was assessed experimentally using a Sentinel-2 image located at the Venice coastland. In the experiment, six water indexes, including 10-m NDWI, 20-m MNDWI and 10-m MNDWI, produced by four pan-sharpening algorithms, were compared. Three levels of results, including the sharpened images, the produced MNDWI images and the finally mapped water bodies, were analysed quantitatively. The results showed that MNDWI can enhance water bodies and suppressbuilt-up features more efficiently than NDWI. Moreover, 10-m MNDWIs produced by all four pan-sharpening algorithms can represent more detailed spatial information of water bodies than 20-m MNDWI produced by the original image. Thus, MNDWIs at the 10-m resolution can extract more accurate water body maps than 10-m NDWI and 20-m MNDWI. In addition, although HPF can produce more accurate sharpened images and MNDWI images than the other three benchmark pan-sharpening algorithms, the ATWT algorithm leads to the best 10-m water bodies mapping results. This is no necessary positive connection between the accuracy of the sharpened MNDWI image and the map-level accuracy of the resultant water body maps.

Interpolation-based super-resolution land cover mapping

Super-resolution mapping (SRM) is a technique to produce a land cover map with finer spatial resolution by using fractional proportion images as input. A two-step SRM approach has been widely used. First, a fine-resolution indicator map is estimated for each class from the coarse-resolution fractional image. All indicator maps are then combined to create the final fine-resolution land cover map. In this letter, three popular interpolation methods, Inverse Distance Weighted (IDW), Spline and Kriging, as well as four indicator map combination strategies, including the maximal value strategy and the sequential assignment strategy with and without normalization, were assessed. Based on the application to two simulated images, the performance of all SRM algorithms was assessed. The results show that the two-step SRM approach can obtain smoother land cover maps than hard classification. An increase in zoom factor results in the appearance of numerous small patches and linear artefacts in the SRM results. The accuracies of Spline and Kriging are similar and are both higher than that of IDW. The maximal value strategy can generate a smoother land cover map than the sequential assignment strategy in most cases, and a normalizing indicator value has a mixed effect on the result.

Example-Based Super-Resolution Land Cover Mapping Using Support Vector Regression

Super-resolution mapping (SRM) is a promising technique to generate a fine resolution land cover map from coarse fractional images by predicting the spatial locations of different land cover classes at subpixel scale. In most cases, SRM is accomplished by using the spatial dependence principle, which is a simple method to describe the spatial patterns of different land cover classes. However, the spatial dependence principle used in existing SRM models does not fully reflect the real-world situations, making the resultant fine resolution land cover map often have uncertainty. In this paper, an example-based SRM model using support vector regression (SVR_SRM) was proposed. Without directly using an explicit formulation to describe the prior information about the subpixel spatial pattern, SVR_SRM generates a fine resolution land cover map from coarse fractional images, by learning the nonlinear relationships between the coarse fractional pixels and corresponding labeled subpixels from the selected best-match training data. Based on the experiments of two subset images of National Land Cover Database (NLCD) 2001 and a subset of real hyperspectral Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) image, the performance of SVR_SRM was evaluated by comparing with the traditional pixel-based hard classification (HC) and several existing typical SRM algorithms. The results show that SVR_SRM can generate fine resolution land cover maps with more detailed spatial information and higher accuracy at different spatial scales.

Spatially Adaptive Superresolution Land Cover Mapping With Multispectral and Panchromatic Images

Superresolution land cover mapping (SRM) is a technique for generating land cover maps with a finer spatial resolution than the input image. In general, either the original multispectral (MS) images or the spectral unmixing results of the MS image are used as input for SRM models. Panchromatic (PAN) images are often captured together with MS images by many remote sensors and provide more spatial information due to their higher spatial resolution compared with the MS image. In this paper, a spatially adaptive spatial-spectral managed SRM model (SA_SSMSRM) that incorporates both MS and PAN images is proposed. SA_SSMSRM aims to better smooth homogeneous regions of objects (which represent a territory within which there is a uniformity in terms of land cover class) and preserve land cover class boundaries simultaneously by using the PAN image pixel photometric distance (i.e., gray-level distance or pixel value difference). Homogeneous regions in the PAN images are usually characterized by the photometric (pixel value) similarity, whereas class boundaries are usually characterized by photometric dissimilarity. The SA_SSMSRM smoothing parameter, which controls the contribution of the prior term (which encodes prior knowledge about land cover spatial patterns), is designed to be spatially adaptive, with its value decreasing if the photometric similarity of neighboring PAN image pixels decreases. SA_SSMSRM was examined on high-spatial-resolution QuickBird images, IKONOS images, and Advanced Land Observing Satellite (ALOS) images with both MS and PAN data. Results showed that the proposed SA_SSMSRM can generate more accurate superresolution maps than other SRM models.

Fusion of Landsat 8 OLI and Sentinel-2 MSI Data

Sentinel-2 is a wide-swath and fine spatial resolution satellite imaging mission designed for data continuity and enhancement of the Landsat and other missions. The Sentinel-2 data are freely available at the global scale, and have similar wavelengths and the same geographic coordinate system as the Landsat data, which provides an excellent opportunity to fuse these two types of satellite sensor data together. In this paper, a new approach is presented for the fusion of Landsat 8 Operational Land Imager and Sentinel-2 Multispectral Imager data to coordinate their spatial resolutions for continuous global monitoring. The 30 m spatial resolution Landsat 8 bands are downscaled to 10 m using available 10 m Sentinel-2 bands. To account for the land-cover/land-use (LCLU) changes that may have occurred between the Landsat 8 and Sentinel-2 images, the Landsat 8 panchromatic (PAN) band was also incorporated in the fusion process. The experimental results showed that the proposed approach is effective for fusing Landsat 8 with Sentinel-2 data, and the use of the PAN band can decrease the errors introduced by LCLU changes. By fusion of Landsat 8 and Sentinel-2 data, more frequent observations can be produced for continuous monitoring (this is particularly valuable for areas that can be covered easily by clouds, thereby, contaminating some Landsat or Sentinel-2 observations), and the observations are at a consistent fine spatial resolution of 10 m. The products have great potential for timely monitoring of rapid changes.

Superresolution Land Cover Mapping With Multiscale Information by Fusing Local Smoothness Prior and Downscaled Coarse Fractions

Superresolution mapping (SRM) is a technique for translating original coarse-resolution fractions into a fine-resolution land cover map by dividing a coarse-resolution pixel into a few finer resolution pixels and determining their class labels. SRM can be solved by considering it a maximum a posteriori principle-based classification problem and by assigning each fine pixel as the class with the highest probability. Fine-pixel class membership probabilities (CMPs) can be calculated at two different scales: at the fine scale, in which the target fine pixel is compared with other fine pixels, and at the coarse scale, in which coarse fractions are downscaled into fine-pixel probabilities. The fine-scale CMP is suitable for representing local land cover features but not for maintaining global features. The coarse-scale CMP is the opposite of the fine-scale CMP. This paper proposes a novel multiscale approach to overcome this shortcoming by fusing the CMP calculated at both fine and coarse scales with the tau model. With the fused CMP, a simulated-annealing algorithm is applied to produce a fine-resolution land cover map. The land cover maps generated from QuickBird and IKONOS images and the National Land Cover Database were used to validate the effectiveness of the proposed SRM algorithms. The proposed SRM algorithms were evaluated visually and quantitatively by comparing them with several existing SRM algorithms. The results indicate that the accuracy of land cover maps at fine spatial resolution increased significantly compared with that obtained from all existing SRM algorithms.

Unsupervised Subpixel Mapping of Remotely Sensed Imagery Based on Fuzzy C-Means Clustering Approach

Subpixel mapping (SPM) is a technique to obtain a land cover map with finer spatial resolution than the original remotely sensed imagery. An image-based SPM model that directly uses the original image data as input by integrating both the spectral and spatial information has been demonstrated as a promising SPM model. However, all existing image-based SPM models are based on a supervised approach, since the spectral term in these SPM models is composed of a supervised unmixing method. The endmembers and training samples for different land cover classes must be determined before implementing these supervised SPM algorithms. In this letter, a novel unsupervised image-based SPM model based on the fuzzy c-means (FCM) clustering approach (usFCM_SPM) was proposed. By incorporating the unsupervised unmixing criterion of the FCM clustering algorithm and the maximal land cover spatial-dependence principle, the proposed usFCM_SPM can generate a subpixel land cover map without any prior endmember information. Both synthetic multispectral image and real IKONOS image experiments demonstrate that the usFCM_SPM can generate higher accuracy subpixel land cover maps than the traditional unsupervised pixel-scale classification approaches and the unsupervised pixel-swapping model.

Learning-Based Spatial–Temporal Superresolution Mapping of Forest Cover With MODIS Images

Forest mapping from satellite sensor imagery provides important information for the timely monitoring of forest growth and deforestation, bioenergy potential assessment, and modeling of carbon flux, among others. Due to the daily global revisit rate and wide swath width, MODerate-resolution Imaging Spectroradiometer (MODIS) images are used commonly for satellite-derived forest mapping at both regional and global scales. However, the spatial resolution of MODIS images is too coarse to observe fine spatial variation in forest cover. The last few decades have seen the production of several fine-spatial-resolution satellite-derived global forest cover maps, such as Hansens global tree canopy cover map of 2000, which includes abundant spectral, temporal, and spatial prior information about forest cover at a fine spatial resolution. In this paper, a novel learning-based spatial-temporal superresolution mapping approach is proposed to integrate both current MODIS images and prior maps of Hansens tree canopy cover, to map present forest cover with a fine spatial resolution. The novel approach is composed of three main stages: 1) automatic generation of 240-m forest proportion images from both 240- and 480-m MODIS images using a nonlinear learning-based spectral unmixing method; 2) downscaling the 240-m forest proportion images to 30 m to predict the class possibilities at the subpixel scale using a temporal-example learning-based downscaling method; and 3) final production of the fine-spatial-resolution forest map by solving a regularization-based optimization problem. The novel approach produced more accurate fine-spatial-resolution forest cover maps in terms of both visual and quantitative evaluation than traditional pixel-based classification and the latest subpixel based superresolution mapping methods. The results show the great efficiency and potential of the novel approach for producing fine-spatial-resolution forest maps from MODIS images.

Spectral–spatial based sub-pixel mapping of remotely sensed imagery with multi-scale spatial dependence

Sub-pixel mapping (SPM) is a technique used to obtain a land-cover map with a finer spatial resolution than input remotely sensed imagery. Spectral–spatial based SPM can directly apply original remote-sensing images as input to produce fine-resolution land-cover maps. However, the existing spectral–spatial based SPM algorithms only use the maximal spatial dependence principle (calculated at the sub-pixel scale) as the spatial term to describe the local spatial distribution of different land-cover features, which always results in an over-smoothed and discontinuous land-cover map. The spatial dependence can also be calculated at the coarse-pixel scale to maintain the holistic land-cover pattern information of the resultant fine-resolution land-cover map. In this article, a novel spectral–spatial based SPM algorithm with multi-scale spatial dependence is proposed to overcome the limitation in the existing spectral–spatial based SPM algorithms. The objective function of the proposed SPM algorithm is composed of three parts, namely spectral term, sub-pixel scale based spatial term, and coarse-pixel scale based spatial term. Synthetic multi-spectral, degraded Landsat multi-spectral and real IKONOS multi-spectral images are employed in the experiments to validate the performance of the proposed SPM algorithm. The proposed algorithm is evaluated visually and quantitatively by comparing with the hard-classification method and two traditional SRM algorithms including pixel-swapping (PS) and Markov-random-field (MRF) based SPM. The results indicate that the proposed algorithm can generate fine-resolution land-cover maps with higher accuracies and more detailed spatial information than other algorithms.

Post-processing of interpolation-based super-resolution mapping with morphological filtering and fraction refilling

Interpolation-based super-resolution mapping (SRM) is a popular model to produce a super-resolution land-cover map from coarse-resolution fraction images. This model can maintain the holistic land-cover features; however, it also results in a super-resolution land-cover map that includes many speckle and linear artefacts, due to errors caused by both the interpolation and the label assignment steps. In this article, we propose a novel two-step post-processing algorithm for interpolation-based SRM. The first step is morphological filtering, which is used to eliminate artefacts and to preserve land-cover features in the super-resolution land-cover map produced by interpolation-based SRM. The second step is fraction refilling, which is applied to make the fraction constraints satisfied and the super-resolution land-cover map locally smooth. Based on the application to three simulated images with various interpolation algorithms and morphological filter operations, the performance of the proposed post-processing algorithm was assessed. The results show that the proposed post-processing algorithm increases the accuracy of the super-resolution land-cover map and is suitable for different interpolation-based SRM models.