Using ILU image to easily distinguish some areas corresponding to different surface types, several samples of different surfaces (forest, fields–which include farmlands, rice, fruit trees, etc., water, urban and layover) were selected. For these surface samples, the histograms of intensity, coherence and intensity change were extracted. These histograms are showed in figures 1~3.

Fig. 2 Histogram of Coherence



Fig. 3 Histogram of Intensity change
Some conclusions can be directly derived from the previous histograms:

• SAR intensity allows easy separation between (1) urban and layover areas and (2) fields, forest and water areas. There is a high overlap between intensity values of areas corresponding to forest and fields and some overlap with intensity values of forest and water bodies;
• Coherence clearly discriminates between (1) water and forest and (2) urban and fields. Values of coherence present certain overlap over layover and water areas;
• Intensity change is able to partially solve the ambiguity between some water and forest areas.

Taking into account the above comments, a hierarchical classification methodology can be derived. The algorithm and the threshold values used for the classification are graphically described in fig.4. This procedure allows not only the classification of forest/ non-forest areas, but provides also information on the type of surface cover over the non-forest areas.
Fig. 4 Hierarchical classification tree

Note: “ int” represents the intensity mean and “int_dif” stands for the intensity change between both acquisitions
Clasification Resulit Evaluation

Available Ground Truth data
A digital land use map of the Zengcheng county mapped in 1990 was used to validate the results obtained with ERS. This land use map provides detailed information of the type of surface, as it can be observed in fig.5. In order to simplify the evaluation of the results, the map corresponding to the forest class was extracted from this complete land use map and it is shown in fig. 6.

Fig. 5 Land Use Map of Zengcheng (forest is in dark green)	Fig. 6 Forest class extracted from the Land Use Map in fig.5

Classification results
Applying the algorithm described in fig. 4, six images showing the pixels classified as each one of the six distinguishable surfaces (forest, fields, water, urban, layover and unambiguous water-or-forest) were obtained. For the purpose of results evaluation, we are only interested in the forest class. The pixels classified as FOREST are showed in fig. 7.

Fig. 7 Forest class extracted from classification using ILU image
Comparing the ground truth forest (fig. 6) and the classified forest (fig.7), the first quantitative values of the accuracy of forest-non forest classification are derived (table 2).
Table 2 Forest classification accuracy

Items	Percentage
Forest classified as forest	70 %
Forest classified as non-forest	15 %
Non-forest classified as forest	10%
Non-forest classified as Non-forest	5%
Total classification accuracy	75%