Proceedings of the Symposium on Global and Environmental Monitoring: Proceedings of the Symposium on Global and Environmental Monitoring

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Multivolume work

Persistent identifier:: 856665355

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts ; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856665355

Language:: English

Document type:: Multivolume work

Volume

Persistent identifier:: 856669164

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Scope:: XIV, 912 Seiten

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856669164

Illustration:: Illustrationen, Diagramme, Karten

Signature of the source:: ZS 312(28,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [THP-1 HIGH SPECTRAL RESOLUTION MEASUREMENT]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: OPTIMIZATION OF UNSUPERVISED LEARNING FOR HIGH RESOLUTION REMOTE SENSING DATA. SHIMODA,HARUHISA; MATSUMAE,YOSHIAKI; NATENOPPARATH,AMNARJ; FUKUE,KIYONARI; SAKATA,TOSHIBUMI

Document type:: Multivolume work

Structure type:: Chapter

587 The reason of conclusion (1) can be attributed to the unbalance of areas of each category. The reason of conclusion (2) can be considered as follows. Generally speaking, larger number of clusters can bring higher classification accuracies. However, if number of clusters are too much compared to sample size, generated clusters are statistcally unstable, and classification accuracies will decrease. If these assumptions are true, the peaks(c1 uster numbers corresponding to maximum classification accuracy) in each sample size column will move to larger number of clusters according to increasing sample size and the maximum classification accuracy will be obtained at the maximum sample size. Actually, the former estimation seems to be realized in Table 3(c), but for the latter estimation, the maximum classification accuracy is obtained when sample size is 45 x 45. The classified result of the maximum classification accuracy is shown in Fig.6. If you compare Fig.3(a) (supervised learning case) and Fig.6, it is evident that high density urban areas are almost disappeared in Fig.6. This phenomena cannot be observed from classification accuracies, because high density urban areas and residential areas are merged in the classification accuracy assessment. The reason that high density urban areas have disappeared is evident. The spectral signatures of high density urban areas and residential areas are very similar, so there occurs much misclassifications between these two categories. However, as the areas of residential areas are far larger than high density urban areas, most of clusters corresponding to high density urban areas are assigned to residential areas because of the nature of area assignment. The reason of this phenomena were considered to be the unbalanced test site. To improve the balance of areas between categories, new test sites were added to test site a. 4.2 Results for test site b Table 4(a) and (b) shows the results for test site b. Table 4(a) corresponds to area weighted mean and Table 4(b) corresponds to an arithmetic mean. From Table 4(a), the following conclusions can be obtained: (1) The absolute accuracies have increased about 10% compared to test site a. (2) The variation among classification accuracies has increased a little (about 10%), but if we omit very low accuracies (in the case of 10 clusters), variations are still very small (about 5%). (3) The tendency of peaks seems more apparent compared to the case of test site a. This tendency is also shown as a graph in Fig.5. However, it is very difficult to say that it is statistically significant. (4) The maximum classification accuracy is still obtained at the case of sample size 45 x 45. In order to acquire more distinct results, simple average were calculated and is shown in Table 4(b). From this result the following conclusion were obtained: (1) The absolute accuracies are now almost the same with Table 4(a). (2) The tendency of peaks also exists, but it is broken at sample size 50 x 50. (3) The variations of classification accuracies are still very small and it is difficult to obtain statistically meaningful conclusion. (4) The maximum classification accuracy is obtained when sample size is 45 x 45 and number of clusters is 83, but the accuracy in the case of when sample size is 30 x 30 and 28 clusters were almost the same (only 0.3% difference). (5) The maximum classification accuracy does not increase with the sample size. In order to avoid this deficit, percentage assignment has been applied. The classified result are shown in Fig.7. From this figure, the inverse effect can be observed. That is, clusters are tend to be assigned to small area categories. For instance, if there is a category which has only 3 pixels in the test site, and 2 pixels in the same cluster are classified to that category, the percentage of that cluster to that category is 66% and this cluster will be assigned to that category even when other 100 pixels of that cluster are classified to another category which has 200 pixels. From the above results the following conclusions were obtained: (1) It is natural that classification accuracies increase with the number of sample size, but this assumption has not been certified in this experiments. (2) There exists the optimal number of clusters according to the sample size. (3) Variations among classification accuracies were very small and classified results were worse than the case of supervised learning though the estimated classification accuracies using test site data were better. (4) Most of the above conclusions were mainly dominated by the category assignment procedure, and a better procedure is necessary to obtain more distinct conclusion.

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