Abstract
Interval type-2 fuzzy c-means (IT2FCM) clustering methods for remote-sensing data classification are based on interval type-2 fuzzy sets and can effectively handle uncertainty of membership grade. However, most of these methods neglect the spatial information when they are used in image clustering. The spatial information and spectral indices are useful in remote-sensing data classification. Thus, determining how to integrate them into IT2FCM to improve the quality and accuracy of the classification is very important. This paper proposes an enhanced IT2FCM* (EnIT2FCM*) algorithm by combining spatial information and spectral indices for remote-sensing data classification. First, the new comprehensive spatial information is defined as the combination of the local spatial distance and attribute distance or membership-grade distance. Then, a novel distance metric is proposed by combining this new spatial information and the selected spectral indices; these selected spectral indices are treated as another dataset in this distance metric. To test the effectiveness of the EnIT2FCM* algorithm, four typical validity indices along with the confusion matrix and kappa coefficient are used. The experimental results show that the spatial information definition proposed here is effective, and some spectral indices and their combinations improve the performance of the EnIT2FCM*. Thus, the selection of suitable spectral indices is crucial, and the combination of soil adjusted vegetation index (SAVI) and the Automated Water Extraction Index (AWEIsh) is the best choice of spectral indices for this method.
Highlights
Land-use/land-cover (LULC) mapping using remote-sensing data is crucial
normalized difference vegetation index (NDVI), EVI and soil adjusted vegetation index (SAVI) belong to vegetation indices (VIs); normalized difference water index (NDWI), MNDWI, AWEInsh and AWEIsh belong to the water indices (WIs); and normalized difference built-up index (NDBI), NDBaI, and morphological building index (MBI) belong to the build indices (BIs)
Yang et al reported that the combinations of fuzzy c-means (FCM) with WIs can essentially be divided into four scenarios, and the scenario in which the WIs are regarded as newly generated bands achieves a balance between simplicity and effectiveness [36]
Summary
Land-use/land-cover (LULC) mapping using remote-sensing data is crucial. The main task of LULC mapping is to identify different land-use types by mapping multi-scale remote-sensing data.The fuzzy c-means (FCM) clustering [1] is a classical unsupervised soft clustering method and widely used in this domain because of its ability to handle fuzzy uncertainty. Land-use/land-cover (LULC) mapping using remote-sensing data is crucial. The main task of LULC mapping is to identify different land-use types by mapping multi-scale remote-sensing data. The fuzzy c-means (FCM) clustering [1] is a classical unsupervised soft clustering method and widely used in this domain because of its ability to handle fuzzy uncertainty. Classical FCM clustering methods are based on type-1 fuzzy set theory, which cannot address uncertainties associated with membership grade [2]. Some researchers adopt the interval type-2 fuzzy sets (IT2 FSs) to improve FCM clustering, and there are three strategies to avoid this problem: (1) The remote-sensing data expressed by real values are extended to interval numbers [3,4]. The widths of the intervals are difficult to determine because a single value must be related to an interval value, which can artificially increase the uncertainty in the data. (2) Hwang and Rhee [5] proposed the IT2FCM method using two fuzzifiers (m1 and m2 ) and IT2 FS; this strategy is being extensively discussed. (3) The land-use
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