Textural information of multitemporal ERS-1 and JERS-1 SAR images with applications to land and forest type classification in boreal zone

The textural information of a multitemporal set of ERS-1 and JERS-1 synthetic aperture radar (SAR) images was studied with the first- and second-order statistical measures. These measures had a higher information value for the land-cover and forest type classification than the SAR image intensity. The multitemporal approach was beneficial for the application of the textural measures; the textural parameters significantly improved the classification of land-cover and forest types. Based on the SAR image texture, the overall classification accuracy for seven land-cover types was 65%, while with the SAR image intensity, the classification accuracy was 50%, respectively. In the forest type classification based on the SAR image texture and intensity, the overall classification accuracy for four forest types was 66%, while with the intensity, the accuracy was 40%, respectively. The weather and seasonal conditions had a significant effect on the textural information of SAR images. The best separability of the signatures and the best land-cover and forest type classification accuracy was achieved under summer conditions. The snow cover and arid conditions decreased the textural information of the SAR images.

[1]  L. Kurvonen,et al.  Capability of radar and microwave radiometer to classify snow types in forested areas , 1994, Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium.

[2]  Kamal Sarabandi,et al.  Michigan microwave canopy scattering model , 1990 .

[3]  F. Ulaby,et al.  Textural Infornation in SAR Images , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Martti Hallikainen,et al.  The use of ERS-1 SAR data in snow melt monitoring , 1997, IEEE Trans. Geosci. Remote. Sens..

[5]  Eric Rignot,et al.  Extraction Of Textural Features In Sar Images: Statistical Model And Sensitivity , 1990, 10th Annual International Symposium on Geoscience and Remote Sensing.

[6]  Fawwaz T. Ulaby,et al.  Land-cover classification and estimation of terrain attributes using synthetic aperture radar , 1995 .

[7]  Martti Hallikainen,et al.  Monitoring of boreal forests with multitemporal special sensor microwave imager data , 1998 .

[8]  Martti Hallikainen,et al.  Monitoring of boreal forests with multitemporal SSM/I data , 1996 .

[9]  C. Oliver.  Optimum texture estimators for SAR clutter , 1993 .

[10]  Martti Hallikainen,et al.  Retrieval of forest parameters from multitemporal spaceborne SAR data , 1996, IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium.

[11]  Kamal Sarabandi,et al.  Knowledge-based classification of polarimetric SAR images , 1994, IEEE Trans. Geosci. Remote. Sens..

[12]  Daniele D. Giusto,et al.  SAR image recognition by integration of intensity and textural information , 1991 .

[13]  Philip H. Swain,et al.  Remote Sensing: The Quantitative Approach , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Martti Hallikainen,et al.  Retrieval of biomass in boreal forests from multitemporal ERS-1 and JERS-1 SAR images , 1999, IEEE Trans. Geosci. Remote. Sens..

[15]  E. Rignot,et al.  Characterization of spatial statistics of distributed targets in SAR data , 1993, International Journal of Remote Sensing.

[16]  R.M. Haralick,et al.  Statistical and structural approaches to texture , 1979, Proceedings of the IEEE.

[17]  J.T. Pulliainen,et al.  Seasonal dynamics of C-band backscatter of boreal forests with applications to biomass and soil moisture estimation , 1996, IEEE Trans. Geosci. Remote. Sens..

[18]  D. R. Paudyal,et al.  Textural information in SAR images for land-cover applications , 1995, 1995 International Geoscience and Remote Sensing Symposium, IGARSS '95. Quantitative Remote Sensing for Science and Applications.

[19]  Li-jen Du,et al.  Texture Segmentation Of SAR Images Using Localized Spatial Filtering , 1990, 10th Annual International Symposium on Geoscience and Remote Sensing.

[20]  D. Barber,et al.  SAR sea ice discrimination using texture statistics : a multivariate approach , 1991 .