evaluation
wiesehahn
2020-07-23
Last updated: 2020-08-04
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Knit directory: baumarten/analysis/
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Introduction
Validation Steps
(classification steps and validation possibilities)
Input Data
- Choice of satellite imagery (date, quality)
- Preprocessing steps (atmospheric, topographic, … correction)
- Image masking (clouds, cloud shadows, nodata)
Reference Data
- Choice of reference data (distribution)
- Label reference data
- Extraction of input data for reference areas
- Splitting in train and test data sets
Build Classification Model
- Choice of classification features
- Choice of model hyperparameters
Apply Classification Model
- Input data classification
- Class probability calculation
Postprocessing
- Apply filter (Minimal Mapping Unit, Focal filter)
- Integration of class probabilities
Accuracy Assessment
- Calculate accuracy metrics from validation data
Reference Data
The reference data was sampled in three project areas (Harz, Heide, Solling).Reference data sampling locations
Number of reference data polygons by tree species
Number of reference data polygons by site
Number of reference data polygons by tree species and site
Classification
Study sites
Solling
Tree species classification result for Solling
Harz
Tree species classification result for Harz
Heide
Tree species classification result for Heide
Probability
The Random Forest algorithm simply counts the fraction of trees in a forest that vote for a certain class to generate the predicted class. This class probability can be generated separately and provides insights in classification certainty.
Overview
Pixelwise classification probability (and average by latitude and longitiude) for the entire study region
Study sites
Solling
Pixelwise classification probability (and average by latitude and longitiude) for Solling
Harz
Pixelwise classification probability (and average by latitude and longitiude) for Harz
Heide
Pixelwise classification probability (and average by latitude and longitiude) for Heide
Reference Probability
Classification probabilities were extracted for each pixel inside reference polygons. The extracted values grant insight in classification certainty by tree species and reference site.
Classification probability by site and tree species for reference data locations
Reference Accuracy
Class predictions for all reference pixels were extracted from the model prediction raster. These predictions were thought to be compared with the reference data label to produce an error matrix. The accuracy was expected to be biased since we used part of the reference data for training the model. But instead, all reference data was classified correctly. This might suggest that the model is overfitted to the reference data, performing very well on the reference data but weaker outside.
Error Matrix| Eiche | Buche | Fichte | Douglasie | Kiefer | Laerche | |
|---|---|---|---|---|---|---|
| Eiche | 6082 | 0 | 0 | 0 | 0 | 0 |
| Buche | 0 | 8025 | 0 | 0 | 0 | 0 |
| Fichte | 0 | 0 | 9495 | 0 | 0 | 0 |
| Douglasie | 0 | 0 | 0 | 1760 | 0 | 0 |
| Kiefer | 0 | 0 | 0 | 0 | 1896 | 0 |
| Laerche | 0 | 0 | 0 | 0 | 0 | 3232 |
R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)
Matrix products: default
locale:
[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252
[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C
[5] LC_TIME=German_Germany.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] rasterVis_0.48 latticeExtra_0.6-29 kableExtra_1.1.0
[4] RColorBrewer_1.1-2 caret_6.0-86 lattice_0.20-41
[7] plotly_4.9.2.1 forcats_0.5.0 stringr_1.4.0
[10] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
[13] tidyr_1.1.0 tibble_3.0.3 ggplot2_3.3.2
[16] tidyverse_1.3.0 formattable_0.2.0.1 raster_3.3-13
[19] leaflet_2.0.3 rgdal_1.5-12 sp_1.4-2
loaded via a namespace (and not attached):
[1] colorspace_1.4-1 ellipsis_0.3.1 class_7.3-17
[4] rprojroot_1.3-2 fs_1.4.2 rstudioapi_0.11
[7] hexbin_1.28.1 farver_2.0.3 prodlim_2019.11.13
[10] fansi_0.4.1 lubridate_1.7.9 xml2_1.3.2
[13] codetools_0.2-16 splines_4.0.2 knitr_1.29
[16] jsonlite_1.7.0 workflowr_1.6.2 pROC_1.16.2
[19] broom_0.7.0 dbplyr_1.4.4 png_0.1-7
[22] compiler_4.0.2 httr_1.4.2 backports_1.1.7
[25] assertthat_0.2.1 Matrix_1.2-18 lazyeval_0.2.2
[28] cli_2.0.2 later_1.1.0.1 leaflet.providers_1.9.0
[31] htmltools_0.5.0 tools_4.0.2 gtable_0.3.0
[34] glue_1.4.1 reshape2_1.4.4 Rcpp_1.0.5
[37] cellranger_1.1.0 vctrs_0.3.2 nlme_3.1-148
[40] iterators_1.0.12 crosstalk_1.1.0.1 timeDate_3043.102
[43] gower_0.2.2 xfun_0.15 rvest_0.3.6
[46] lifecycle_0.2.0 zoo_1.8-8 MASS_7.3-51.6
[49] scales_1.1.1 ipred_0.9-9 hms_0.5.3
[52] promises_1.1.1 parallel_4.0.2 yaml_2.2.1
[55] rpart_4.1-15 stringi_1.4.6 highr_0.8
[58] foreach_1.5.0 e1071_1.7-3 lava_1.6.7
[61] rlang_0.4.7 pkgconfig_2.0.3 evaluate_0.14
[64] recipes_0.1.13 htmlwidgets_1.5.1 tidyselect_1.1.0
[67] plyr_1.8.6 magrittr_1.5 R6_2.4.1
[70] generics_0.0.2 DBI_1.1.0 pillar_1.4.6
[73] haven_2.3.1 whisker_0.4 withr_2.2.0
[76] survival_3.2-3 nnet_7.3-14 modelr_0.1.8
[79] crayon_1.3.4 rmarkdown_2.3 jpeg_0.1-8.1
[82] grid_4.0.2 readxl_1.3.1 data.table_1.12.8
[85] blob_1.2.1 git2r_0.27.1 ModelMetrics_1.2.2.2
[88] reprex_0.3.0 digest_0.6.25 webshot_0.5.2
[91] httpuv_1.5.4 stats4_4.0.2 munsell_0.5.0
[94] viridisLite_0.3.0