Abstract
Abstract. In this paper, we perform multi-sensor multi-resolution data fusion of Landsat-5 TM bands (at 30 m spatial resolution) and multispectral bands of World View-2 (WV-2 at 2 m spatial resolution) through linear spectral unmixing model. The advantages of fusing Landsat and WV-2 data are two fold: first, spatial resolution of the Landsat bands increases to WV-2 resolution. Second, integration of data from two sensors allows two additional SWIR bands from Landsat data to the fused product which have advantages such as improved atmospheric transparency and material identification, for example, urban features, construction materials, moisture contents of soil and vegetation, etc. In 150 separate experiments, WV-2 data were clustered in to 5, 10, 15, 20 and 25 spectral classes and data fusion were performed with 3x3, 5x5, 7x7, 9x9 and 11x11 kernel sizes for each Landsat band. The optimal fused bands were selected based on Pearson product-moment correlation coefficient, RMSE (root mean square error) and ERGAS index and were subsequently used for vegetation, urban area and dark objects (deep water, shadows) classification using Random Forest classifier for a test site near Golden Gate Bridge, San Francisco, California, USA. Accuracy assessment of the classified images through error matrix before and after fusion showed that the overall accuracy and Kappa for fused data classification (93.74%, 0.91) was much higher than Landsat data classification (72.71%, 0.70) and WV-2 data classification (74.99%, 0.71). This approach increased the spatial resolution of Landsat data to WV-2 spatial resolution while retaining the original Landsat spectral bands with significant improvement in classification.
Highlights
In the geospatial terminology, land cover (LC) refers to the physical state of the Earth's surface in terms of natural environment, such as vegetation, settlement, water, etc
Since fused image should be as identical as possible to the original low spatial resolution (LSR) image once degraded back to its original resolution, so the fused bands were degraded to 30 m using a mean filter
The quality of the degraded fused images was assessed by comparing them with the original Landsat Thematic Mapper (TM) image
Summary
Land cover (LC) refers to the physical state of the Earth's surface in terms of natural environment, such as vegetation, settlement, water, etc. When the MS data have LSR pixels representing mixture of multiple objects (forming mixed pixels), they pose difficulty in classification and interpretation In such cases, image fusion techniques that can fuse or merge MS with MS/hyperspectral bands, such as IRS LISS-III MS bands with MODIS bands, or IKONOS MS bands with Landsat TM MS bands are required. In LMM based fusion, first a HSR data (WV2) is clustered using any clustering technique such as K-means clustering algorithm into user-defined number of unknown classes This clustered image provides abundance or proportion of different classes corresponding to each pixel of the LSR Landsat data. The initial number of clusters in the HSR data and the size of moving window are heuristic In other words, this iteration is continued for all the pixels in the LSR bands while solving the LMM equations with proportions from HSR clustered image to obtain user-defined number of class’s spectra. Covariance matrix of the noise vector is σ2I, where σ2 is the variance, and I is M × M identity matrix
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