Large area change detection by differencing radiometrically-normalized images

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Large area change detection by differencing radiometrically-normalized images

The difference image, now in grid-format change map, was classified into two categories of change and no-change using a threshold. Figure 3 illustrates a subarea of the study site with the change areas in grey and no-change areas in white. The known locations were also classified into two categories of change (symbolized as small crosses) and no-change (as small circles). Superimposing the known locations over the classified change map gives a visual clue of how accurate the specified threshold provides. At each location, the change detection is correct if a cross is over a grey area or a circle is over a white area. A cross over a white area is a false alarm or commission error while a circle over a grey area means omission error. An Arcview’s avenue script was developed to facilitate this accuracy determination. Table 2 shows the result.

Table 2. Accuracy of change detection. Highest accuracy for each band is highlighted.

Threshol d	Band	# Ground Truth Locations	# Correct Detection	Accuracy (%)	# Commission Error	# Omission Error	Total Error (%)
5	1	180	128	71.1	50	2	28.9
nbsp;	2	180	136	75.6	41	3	24.4
nbsp;	3	180	129	71.7	51	0	28.3
nbsp;	4	180	111	61.7	48	21	38.3
Threshol d	Band	# Ground Truth Locations	# Correct Detection	Accuracy (%)	# Commission Error	# Omission Error	Total Error (%)
10	1	180	132	73.3	45	3	26.7
nbsp;	2	180	130	72.2	35	15	27.8
nbsp;	3	180	128	71.1	49	3	28.9
nbsp;	4	180	106	58.9	37	37	41.1
Threshol	Band	# Ground Truth Locations	# Correct Detection	Accuracy (%)	# Commission Error	# Omission Error	Total Error (%)
15	1	180	133	73.4	37	10	26.6
nbsp;	2	180	119	66.1	27	34	33.9
nbsp;	3	180	131	72.8	45	4	27.2
nbsp;	4	180	89	49.4	34	57	50.6
Threshold 20 Threshold	Band 1 2 3 4 Band	# Ground Truth Locations 180 180 180 180 180 # Ground Truth locations	# Correct Detection 132 112 136 78 # Correct Detection	Accuracy (%) 73.3 62.2 75.6 43.3 accuracy (%)	# Commission Error 30 22 37 27 # commission Error	# Omission Error 18 46 7 75 # Omission Error	Total Error (%) 26.7 37.8 24.4 56.7 Total Error (%)
25	1	180	129	71.7	24	27	28.3
nbsp;	2	180	102	56.7	18	60	43.3
nbsp;	3	180	131	72.8	34	15	27.2
nbsp;	4	180	73	40.6	18	89	59.4

The experiment shows that most accurate results can be achieved by using band 2 with the threshold value of 5 or band 3 with the threshold value of 20. Comparing to other bands, the use of band 4 results in a consistently less accurate detection, falling below 50 percent once the threshold is set to 15 and over. Between the threshold value of 5 and 20, mixed result is obtained with accuracy increases with threshold in some bands and decreases in others. But the drop of correct detections in every band when the threshold is increased from 20 to 25 indicates that the value of around 20 is the optimal threshold. Overall the experiment demonstrates that large area change detection with the accuracy of around 70 percent can be achieved. Bias in check point selection may play a role in explaining why Landsat TM band 4 yields significantly less accurate result than those obtained from other bands but this is not yet fully understood and is subject to further investigation.

Conclusion
To really see the pattern of changes, activities such as environmental state monitoring, coastal resource monitoring, crop replacement or urban expansion monitoring are normally performed over large area. Differencing radiometrically normalized images is a simple and intuitive technique that can be used effectively for this purpose. Most experiments so far, however, have focused on areas relatively small comparing to the whole satellite image scene. The result of this research clearly demonstrates that such technique can be used over large area and satisfactory accuracy can be achieved.

Further research is being undertaken to determine appropriate spectral band for each kind of change. Another area of investigation is to verify if the same level of detection accuracy can be achieved in the area not well-covered by normalization targets.

References

Chavez Jr, P.S., 1996. Image-Based Atmospheric Corrections – Revisited and Improved. Photogrammetric Engineering and Remote Sensing, 62 (9), pp. 1025 – 1036.
Fung, T., and Ledrew, E., 1988. The determination of optimal threshold levels for change detection using various accuracy indices. Photogrammetric Engineering and Remote Sensing, 54 (10), pp. 1449 – 1454..Fung, T., 1990. An Assessment of TM Imagery for Land-Cover Change Detection. IEEE Transactions on Geoscience and Remote Sensing, 28 (4), pp. 681-684.
Heo, J., and Fitzhugh, T.W., 2000. A Standardized Radiometric Normalization Method for Change Detection Using Remotely Sensed Imagery. 66 (2), pp. 173-181.
Hu C., Carder K.L., and Muller-Kargen F.E., 2000. Atmospheric Correction of SeaWiFS Imagery over Turbid Coastal Waters: A Practical Method. Remote Sensing of Environment, 74, pp. 195 – 206.
Jensen, J.R., Rutchey K., Koch M.S., and Narumalani S., 1995. Inland Wetland Change Detection in the Everglades Water Conservation Area 2A Using a Time Series of Normalized Remotely Sensed Data. Photogrammetric Engineering and Remote Sensing, 61 (2), pp. 199-209.
Mather, P.M., 1999. Computer Processing of Remotely-Sensed Images. John Wiley & Sons, pp. 114 – 115.
Triola, M.F., 1995. Elementary Statistics. Addison-Wesley, New York, pp. 476 – 482.

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