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The use of step‐wise density slicing in classifying high‐resolution panchromatic photographs

The present paper provides an improvised, simple and easy‐to‐follow methodology in which step‐wise density slicing was used to classify high‐resolution panchromatic photographs to levels of spatial detail and accuracy that offer end products comparable with what is obtainable from conventional signature‐based classification of multispectral images. By tapping on a range of manipulative remote sensing and GIS techniques, we provide an illustrated example that takes the reader from on‐screen image display to final compilation and accuracy assessment of a classified map output. Comprising digital classification by image segmentation and visually‐guided grey scale coding, the methodology we offer was practically tested in a change detection investigation around the Okavango Delta in northern Botswana. We conveniently selected one site from this broad study and sampled results over four time‐slices for illustrative purposes. With this improvisation giving results with an accuracy level of 69%, we suggest that this methodology may be used for detailed classification of high‐resolution panchromatic images.