Yun Du

Super-resolution land-cover mapping using multiple sub-pixel shifted remotely sensed images

Super-resolution land-cover mapping is a promising technology for prediction of the spatial distribution of each land-cover class at the sub-pixel scale. This distribution is often determined based on the principle of spatial dependence and from land-cover fraction images derived with soft classification technology. However, the resulting super-resolution land-cover maps often have uncertainty as no information about sub-pixel land-cover patterns within the low-resolution pixels is used in the model. Accuracy can be improved by incorporating supplemental datasets to provide more land-cover information at the sub-pixel scale; but the effectiveness of this is limited by the availability and quality of these additional datasets. In this paper, a novel super-resolution land-cover mapping technology is proposed, which uses multiple sub-pixel shifted remotely sensed images taken by observation satellites. These satellites take images over the same area once every several days, but the images are not identical because of slight orbit translations. Low-resolution pixels in these remotely sensed images therefore contain different land-cover fractions that can provide useful information for super-resolution land-cover mapping. We have constructed a Hopfield Neural Network (HNN) model to solve it. Maximum spatial dependence is the goal of the proposed model, and the fraction maps of all images are constraints added to the energy function of HNN. The model was applied to synthetic artificial images as well as to a real degraded QuickBird image. The output maps derived from different numbers of images at different zoom factors were compared visually and quantitatively to the super-resolution map generated from a single image. The resulting land-cover maps with multiple remotely sensed images were more accurate than was the single image map. The use of multiple remotely sensed images is therefore a promising method for decreasing the uncertainty of super-resolution land-cover mapping. Moreover, remotely sensed images with similar spatial resolution from different satellite platforms can be used together, allowing a fusion of information obtained from remotely sensed imagery.

Sedimentary evidence for changes in the pollution status of Taihu in the Jiangsu region of eastern China

As part of a study using lake sediments to determine the extent and causes of human impacts to lakes along an east–west transect following the Yangtse River, sediment cores were taken from Taihu in eastern China. Previous studies have focussed on the impacts of direct inputs of pollutants from municipal and industrial wastewater but little work has been undertaken on trends in atmospheric deposition from the many industrial sources surrounding the lake. Analysis of the Taihu sediment cores for atmospheric pollutant indicators such as trace metals, magnetic parameters and spheroidal carbonaceous particles (SCPs) show the lake has become increasingly contaminated over the last 40–50 years. Sediment levels of atmospherically deposited pollutants are currently similar to some of the more contaminated lakes in Europe. Further, sediment nitrogen, phosphorus and geochemical analyses confirm the dramatic increase in eutrophication at the site and periods of recent soil erosion in the catchment.

Lake area changes in the middle Yangtze region of China over the 20th century.

The Jianghan Plain and the Dongting lake area, located in the middle reaches of the Yangtze River are famous for their abundant freshwater lakes. The lakes have undergone changes in size and number over thousands of years due to natural causes and human activities. The 20th century particularly, witnessed dramatic changes in the freshwater resources of this region. This paper traces and analyzes lake evolution in the middle reaches of the Yangtze River over the past century. Lakes greater than 0.1 km(2) in size are mapped using Geographic Information System. Data is acquired and integrated from drainage network maps, relief maps, historical maps and remotely sensed images for different time periods. The results indicate that while there has been little change in the number of lakes over the past century, the lake area has experienced a dramatic decrease of 58.06%. The paper also examines the natural processes and human activities that may have contributed to the decrease in lake area. The results show that the decrease in total lake area appears to coincide with periods of rapid land reclamation in the middle reaches of the Yangtze River. Moreover, uncontrolled land reclamation activities can create an increase in sediment deposition in lakes thereby further reducing the lake size. Reduction of the lake area directly affects flood-control and has a negative ecological impact on the environment and on human life and property.

Land Cover Change Mapping at the Subpixel Scale With Different Spatial-Resolution Remotely Sensed Imagery

Extracting land cover change (LCC) information at the subpixel scale is important when coarse-resolution remotely sensed images are used for change detection. Although fraction images derived from soft-classification technologies can be used for subpixel LCC detection, the spatial distribution of changed subpixels within each coarse-resolution pixel cannot be provided. This letter presents a subpixel LCC mapping (SLCCM) algorithm, aiming to predict the spatial pattern of LCC at the subpixel scale between bitemporal images through comparing the former high-resolution land cover map and the latter fraction images derived from the coarse-resolution image. The resulting subpixel LCC map is determined by the spatial dependence principle and an LCC rule in each mixed pixel. The proposed algorithm was evaluated with simulated and real images, and the results showed the effectiveness of the proposed method for SLCCM.

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.

Waterline mapping at the subpixel scale from remote sensing imagery with high-resolution digital elevation models

Subpixel mapping technology is a promising method of increasing the spatial resolution of the classification results derived from remote sensing imagery. However, for waterline mapping problems, the traditional spatial dependence principle of subpixel mapping is not suitable as the water flow is always controlled by the topography. This letter presents a novel algorithm based on a high spatial resolution digital elevation model (DEM) to address the subpixel waterline mapping problem. The waterline was mapped at the subpixel scale with a proposed rule according to the physical features of the water flow and additional information provided by the DEM. The method was evaluated with degraded real remotely sensed imagery at different spatial resolutions. The results show that the proposed method can provide more accurate classifications than the traditional subpixel mapping method. Moreover, the fine spatial resolution DEM can be used as feasible supplementary data for subpixel waterline mapping from coarser spatial resolution imagery.

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.

Object-based sub-pixel mapping of buildings incorporating the prior shape information from remotely sensed imagery

Abstract Sub-pixel mapping (SPM) is a promising method to predict the spatial locations of land cover classes at the sub-pixel scale for remotely sensed imagery, using the fraction images generated by soft classification as input. At present, SPM treats all sub-pixels of different land cover classes in the same strategy by maximizing their spatial dependence. Although the maximal spatial dependence is a simple method to describe the spatial pattern of land cover classes and has been proved to be an effective principle for SPM, it does not reflect real-world situations. Given that spatial patterns are land cover class- or object-specific, each land cover class or object should be designated its own specific spatial pattern description when SPM is applied. In this paper, a novel object-based sub-pixel mapping (OBSPM) method was proposed to map buildings at the sub-pixel scale. On the basis of the prior information of the building shape (i.e., the building boundaries are parallel or perpendicular to the main orientation), a novel anisotropic spatial dependence model is adopted in the SPM procedure. The proposed OBSPM model includes three main steps: building segmentation, building feature extraction, and anisotropic SPM of buildings. The proposed model is evaluated with a simulated synthetic image and an actual AVIRIS image. The results show that OBSPM obtains more accurate building maps than do conventional SPM models, and the accuracy of fraction images and the spatial resolutions of remotely sensed images are two crucial factors that influence the OBSPM results. Furthermore, extending the OBSPM model to more land cover classes to incorporate more specific prior information is a promising method in enhancing the applicability of SPM to practical situations.

Spatially adaptive smoothing parameter selection for Markov random field based sub-pixel mapping of remotely sensed images

Sub-pixel mapping is a process to provide the spatial distributions of land cover classes with finer spatial resolution than the size of a remotely sensed image pixel. Traditional Markov random field-based sub-pixel mapping (MRF_SPM) adopts a fixed smoothing parameter estimated based on the entire image to balance the spatial and spectral energies. However, the spectra of the remotely sensed pixels are always spatially variable. Adopting a fixed smoothing parameter disregards the local properties provided by each pixel spectrum, and may probably lead to insufficient smoothing in the homogeneous region and over-smoothing between class boundaries simultaneously. This article proposes a spatially adaptive parameter selection method for the MRF_SPM model to overcome the limitation of the fixed parameter. As pixel class proportions are indicators of the type and proportion of land cover classes within each coarse pixel, in the proposed method, fraction images providing pixel class proportions as local properties of each pixel spectrum are employed to constrain the smoothing parameter. Consequently, the smoothing parameter is spatially adaptive to each pixel spectrum of the remotely sensed image. Synthetic images and IKONOS multi-spectral images were employed. Results showed that compared with the hard classification method and the non-spatially adaptive MRF_SPM adopting a fixed smoothing parameter, the spatially adaptive MRF_SPM with the smoothing parameter constrained to each pixel spectrum yielded sub-pixel maps not only with higher accuracy but also with shapes and boundaries visually reconstructed more closely to the reference map.

Super-Resolution Mapping of Forests With Bitemporal Different Spatial Resolution Images Based on the Spatial-Temporal Markov Random Field

High deforestation rates necessitate satellite images for the timely updating of forest maps. Coarse spatial resolution remotely sensed images have wide swath and high temporal resolution. However, the mixed pixel problem lowers the mapping accuracy and hampers the application of these images. The development of remote sensing technology has enabled the storage of a great amount of medium spatial resolution images that recorded the historical conditions of the earth. The combination of timely updated coarse spatial resolution images and previous medium spatial resolution images is a promising technique for mapping forests in large areas with instant updating at low expense. Super-resolution mapping (SRM) is a method for mapping land cover classes with a finer spatial resolution than the input coarse resolution image. This method can reduce the mixed pixel problem of coarse spatial resolution images to a certain extent. In this paper, a novel spatial-temporal SRM based on a Markov random field, called STMRF_SRM, is proposed using a current coarse spatial resolution Moderate-Resolution Imaging Spectroradiometer image and a previous medium spatial resolution Landsat Thematic Mapper image as input. The proposed model encourages the spatial smoothing of land cover classes for spatially neighboring subpixels and keeps temporal links between temporally neighboring subpixels in bitemporal images. Results show that the proposed STMRF_SRM model can generate forest maps with higher overall accuracy and kappa value.