Qunming Wang

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.

Allocating Classes for Soft-Then-Hard Subpixel Mapping Algorithms in Units of Class

There is a type of algorithm for subpixel mapping (SPM), namely, the soft-then-hard SPM (STHSPM) algorithm that first estimates soft attribute values for land cover classes at the subpixel scale level and then allocates classes (i.e., hard attribute values) for subpixels according to the soft attribute values. This paper presents a novel class allocation approach for STHSPM algorithms, which allocates classes in units of class (UOC). First, a visiting order for all classes is predetermined, and the number of subpixels belonging to each class is calculated using coarse fraction data. Then, according to the visiting order, the subpixels belonging to the being visited class are determined by comparing the soft attribute values of this class, and the remaining subpixels are used for the allocation of the next class. The process is terminated when each subpixel is allocated to a class. UOC was tested on three remote sensing images with five STHSPM algorithms: back-propagation neural network, Hopfield neural network, subpixel/pixel spatial attraction model, kriging, and indicator cokriging. UOC was also compared with three existing allocation methods, i.e., linear optimization technique (LOT), sequential assignment in units of subpixel (UOS), and a method that assigns subpixels with highest soft attribute values first (HAVF). Results show that for all STHSPM algorithms, UOC is able to produce higher SPM accuracy than UOS and HAVF; compared with LOT, UOC is able to achieve at least comparable accuracy but needs much less computing time. Hence, UOC provides an effective and real-time class allocation method for STHSPM algorithms.

Indicator Cokriging-Based Subpixel Land Cover Mapping With Shifted Images

Subpixel mapping (SPM) is a technique for predicting the spatial distribution of land cover classes in remote sensing images at a finer spatial resolution level than those of the input images. Indicator cokriging (ICK) has been found to be an effective and efficient SPM method. The accuracy of this model, however, is limited by insufficient constraints. In this paper, the accuracy of the ICK-based SPM model is enhanced by using additional information gained from multiple shifted images (MSIs). First, each shifted image is utilized to compute the conditional probability of class occurrence at any fine spatial resolution pixel (i.e., subpixel) using ICK, and a set of conditional probability maps for all classes are generated for each image. The multiple ICK-derived conditional probability maps are then integrated, according to the estimated subpixel shifts of MSI. Lastly, class allocation at the subpixel scale is implemented to produce SPM results. The proposed algorithm was tested on two synthetic coarse spatial resolution remote sensing images and a set of real Moderate Resolution Imaging Spectroradiometer (MODIS) data. It was evaluated both visually and quantitatively. The experimental results showed that more accurate SPM results can be generated with MSI than with a single observed coarse image in conventional ICK-based SPM. In addition, the accuracy of the proposed method is higher than that of the existing Hopfield neural network (HNN)-based SPM and the HNN with MSI.

Land Cover Change Detection at Subpixel Resolution With a Hopfield Neural Network

In this paper, a new subpixel resolution land cover change detection (LCCD) method based on the Hopfield neural network (HNN) is proposed. The new method borrows information from a known fine spatial resolution land cover map (FSRM) representing one date for subpixel mapping (SPM) from a coarse spatial resolution image on another, closer date. It is implemented by using the thematic information in the FSRM to modify the initialization of neuron values in the original HNN. The predicted SPM result was compared to the original FSRM to achieve subpixel resolution LCCD. The proposed method was compared with the original unmodified HNN method as well as six state-of-the-art methods for LCCD. To explore the effect of uncertainty in spectral unmixing, which mainly originates from spectral separability in the input, coarse image, and the point spread function (PSF) of the sensor, a set of synthetic multispectral images with different class separabilities and PSFs was used in experiments. It was found that the proposed LCCD method (i.e., HNN with an FSRM) can separate more real changes from noise and produce more accurate LCCD results than the state-of-the-art methods. The advantage of the proposed method is more evident when the class separability is small and the variance in the PSF is large, that is, the uncertainty in spectral unmixing is large. Furthermore, the utilization of an FSRM can expedite the HNN-based processing required for LCCD. The advantage of the proposed method was also validated by applying to a set of real Landsat-Moderate Resolution Imaging Spectroradiometer (MODIS) images.

Fast Subpixel Mapping Algorithms for Subpixel Resolution Change Detection

Due to rapid changes on the Earths surface, it is important to perform land cover change detection (CD) at a fine spatial and fine temporal resolution. However, remote sensing images with both fine spatial and temporal resolutions are commonly not available or, where available, may be expensive to obtain. This paper attempts to achieve fine spatial and temporal resolution land cover CD with a new computer technology based on subpixel mapping (SPM): The fine spatial resolution land cover maps (FRMs) are first predicted through SPM of the coarse spatial but fine temporal resolution images, and then, subpixel resolution CD is performed by comparison of class labels in the SPM results. For the first time, five fast SPM algorithms, including bilinear interpolation, bicubic interpolation, subpixel/pixel spatial attraction model, Kriging, and radial basis function interpolation methods, are proposed for subpixel resolution CD. The auxiliary information from the known FRM on one date is incorporated in SPM of coarse images on other dates to increase the CD accuracy. Based on the five fast SPM algorithms and the availability of the FRM, subpixels for each class are predicted by comparison of the estimated soft class values at the target fine spatial resolution and borrowing information from the FRM. Experiments demonstrate the feasibility of the five SPM algorithms using FRM in subpixel resolution CD. They are fast methods to achieve subpixel resolution CD.

Utilizing Multiple Subpixel Shifted Images in Subpixel Mapping With Image Interpolation

In this letter, multiple subpixel shifted images (MSIs) were utilized to increase the accuracy of subpixel mapping (SPM), based on the fast bilinear and bicubic interpolation. First, each coarse spatial resolution image of MSI is soft classified to obtain class fraction images. Using bilinear or bicubic interpolation, all fraction images of MSI are upsampled to the desired fine spatial resolution. The multiple fine spatial resolution images for each class are then integrated. Finally, the integrated fine spatial resolution images are used to allocate hard class labels to subpixels. Experiments on two remote sensing images showed that, with MSI, both bilinear and bicubic interpolation-based SPMs are more accurate. The new methods are fast and do not need any prior spatial structure information.

Indicator Cokriging-Based Subpixel Mapping Without Prior Spatial Structure Information

Indicator cokriging (ICK) has been shown to be an effective subpixel mapping (SPM) algorithm. It is noniterative and involves few parameters. The original ICK-based SPM method, however, requires the semivariogram of land cover classes from prior information, usually in the form of fine spatial resolution training images. In reality, training images are not always available, or laborious work is needed to acquire them. This paper aims to seek spatial structure information for ICK when such prior land cover information is not obtainable. Specifically, the fine spatial resolution semivariogram of each class is estimated by the deconvolution process, taking the coarse spatial resolution semivariogram extracted from the class proportion image as input. The obtained fine spatial resolution semivariogram is then used to estimate class occurrence probability at each subpixel with the ICK method. Experiments demonstrated the feasibility of the proposed ICK with the deconvolution approach. It obtains comparable SPM accuracy to ICK that requires semivariogram estimated from fine spatial resolution training images. The proposed method extends ICK to cases where the prior spatial structure information is unavailable.

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.

A New Geostatistical Solution to Remote Sensing Image Downscaling

The availability of the panchromatic (PAN) band in remote sensing images gives birth to so-called image fusion techniques for increasing the spatial resolution of images to that of the PAN band. The spatial resolution of such spatially sharpened images, such as for the MODIS and Landsat sensors, however, may not be sufficient to provide the required detailed land-cover/land-use information. This paper proposes an area-to-point regression kriging (ATPRK)-based geostatistical solution to increase the spatial resolution of remote sensing images beyond that of any input images, including the PAN band. The new approach is a two-stage approach, including covariate downscaling and ATPRK-based image fusion. The new approach treats the PAN band as the covariate and takes advantages of its textural information. It explicitly accounts for the size of support, spatial correlation, and the point spread function of the sensor and has the characteristic of perfect coherence with the original coarse data. Moreover, the new downscaling approach can be extended readily by incorporating other ancillary information. The proposed approach was examined using both Landsat and MODIS images. The results show that it can produce more accurate sharpened images than four benchmark approaches.

Spatial–Spectral Information-Based Semisupervised Classification Algorithm for Hyperspectral Imagery

Semisupervised learning has shown its great potential in land cover mapping. It exploits the information of unlabeled training samples and converts those samples to labeled training samples to enhance classification. In this paper, the spatial information extracted by a two-dimensional (2-D) Gabor filter was stacked with spectral information first, and then the spatial neighborhood information of labeled training samples was combined with active learning (AL) algorithm to select the most useful and informative samples, which were used as the unlabeled set to aid the probability model-based supervised support vector machine (SVM). Experiments on two hyperspectral datasets showed that the spatial-spectral information-based semisupervised classification algorithm (