Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Fine spatial resolution satelite sensor imagery for land cover mapping in the United Kingdom. / Aplin, Paul; Atkinson, Peter M.; Curran, Paul J.
In: Remote Sensing of Environment, Vol. 68, No. 3, 1999, p. 206-216.Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Fine spatial resolution satelite sensor imagery for land cover mapping in the United Kingdom
AU - Aplin, Paul
AU - Atkinson, Peter M.
AU - Curran, Paul J.
N1 - M1 - 3
PY - 1999
Y1 - 1999
N2 - This article presents a set of techniques developed to classify land cover on a per-parcel (herein termed per-field) basis by integrating fine spatial resolution simulated satellite sensor imagery with digital vector data. Classification, based on the spectral and spatial properties of the imagery, was carried out on a per-pixel basis. The resulting classified images were then integrated with vector data to classify on a per-field basis. Four tools were adopted or developed to increase the accuracy and utility of the per-field classification and a fifth was proposed. The spectral variability within agricultural fields resulted in misclassification within the per-pixel classification, and this was overcome using a per-field classification. Mixed land cover in urban areas also resulted in misclassification. A low pass smoothing filter and a “texture” filter applied to the per-pixel classified image increased the classification accuracy of this land cover prior to per-field classification. The flexibility of the integration process enabled the exploitation of spectral and spatial variation between pixels within individual parcels to produce new classes during per-field classification and to identify fields with a high likelihood of misclassification.
AB - This article presents a set of techniques developed to classify land cover on a per-parcel (herein termed per-field) basis by integrating fine spatial resolution simulated satellite sensor imagery with digital vector data. Classification, based on the spectral and spatial properties of the imagery, was carried out on a per-pixel basis. The resulting classified images were then integrated with vector data to classify on a per-field basis. Four tools were adopted or developed to increase the accuracy and utility of the per-field classification and a fifth was proposed. The spectral variability within agricultural fields resulted in misclassification within the per-pixel classification, and this was overcome using a per-field classification. Mixed land cover in urban areas also resulted in misclassification. A low pass smoothing filter and a “texture” filter applied to the per-pixel classified image increased the classification accuracy of this land cover prior to per-field classification. The flexibility of the integration process enabled the exploitation of spectral and spatial variation between pixels within individual parcels to produce new classes during per-field classification and to identify fields with a high likelihood of misclassification.
U2 - 10.1016/S0034-4257(98)00112-6
DO - 10.1016/S0034-4257(98)00112-6
M3 - Journal article
VL - 68
SP - 206
EP - 216
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
SN - 0034-4257
IS - 3
ER -