An enhanced supervised spatial decision support system of image classification: consideration on the ancillary information of paddy rice area

Abstract

The analysis, measurement, and computation of remote sensing images often require an enhanced supervised classification technique to develop an efficient spatial decision support system. Rice is a crop of global importance, which has drawn a great interest in using remote sensing techniques for evaluating its production. Ancillary information is widely used to improve the classification accuracy of satellite images. However, few of these studies questioned the importance and strategies of using this ancillary information. The enhanced decision support system in our study has two stages. In the first stage, the images are obtained from the remote sensing technique and the ancillary information is employed to increase the accuracy of classification. In the second stage, it is decided to construct an efficiently supervised classifier, which is used to evaluate the ancillary information. Back-propagation neural network (BPN) with extended delta bar delta (EDBD) algorithm is incorporated into our decision support classifier system. This classifier renders two crucial contributions: (1) the EDBD algorithm accelerates the convergence speed of the learning process and (2) the relative importance (RI) on each band of ancillary information is evaluated rationally.

A knowledge-based decision support system to analyze the debris-flow problems at Chen-Yu-Lan River, Taiwan

“…Few of the studies focused on the geomorphological and hydraulic factors influencing the relation on debris-flow occurrence or nonoccurrence. But in the few years, Data Mining approaches [40,42] had become a brand new solution in analyzing debris-flow. Assessments and nonlinear characteristics of the landslide phenomena, utilization of this technique can be considered as an effective approach when insufficient data exist statistically, especially for large areas [31].…”

Mapping Early, Middle and Late Rice Extent Using Sentinel-1A and Landsat-8 Data in the Poyang Lake Plain, China

“…Many efforts have been made to map paddy rice planting areas by using various classification algorithms and data sources, including optical- and microwave-based remotely sensed data. In terms of classification approaches, they can generally be divided into unsupervised classification [ 10 , 11 ] and supervised classification methods [ 12 , 13 ]. Knowledge- [ 14 ] and phenology-based approaches [ 15 ] are typical methods used in supervised classification.…”

Object-Based Paddy Rice Mapping Using HJ-1A/B Data and Temporal Features Extracted from Time Series MODIS NDVI Data

“…Recently, significant efforts have been carried out towards the mapping of paddy rice using the time series data [ 13 , 14 , 15 , 16 ]. A variety of techniques have been used for paddy rice mapping such as supervised classification [ 17 , 18 ], a thresholding based method [ 19 , 20 , 21 ], phenology based mapping [ 13 , 22 ] and a subtraction based method [ 23 , 24 ]. However, a number of limitations exist with these methods.…”