Polarimetric signatures for different tropical land covers were extracted from RADARSAT-2 data. Subsequently, the data were classified. The objective of this ...
POLARIMETRIC SIGNATURES AND CLASSIFICATION OF TROPICAL LAND COVERS Tatiana Mora Kuplich1, Yosio Edemir Shimabukuro2, Emerson Servello1, Edson Sano3 1
Centro Regional Sul de Pesquisas Espaciais (CRS) - INPE Caixa Postal 5021 - 97110-970 - Santa Maria, RS, Brasil 2
Instituto Nacional de Pesquisas Espaciais (INPE) Av. dos Astronautas 1758 - 12227-010 - São José dos Campos, SP, Brasil {tmk, yosio, servello}@dsr.inpe.br 3
Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) BR-020 km 18, Planaltina -73301-970 - Brasilia, DF, Brasil
ABSTRACT Polarimetric signatures for different tropical land covers were extracted from RADARSAT-2 data. Subsequently, the data were classified. The objective of this work was to assess the potential of RADARSAT-2 polarimetric C band data on land cover mapping. RADARSAT-2 data were acquired over Tapajos National Forest, a tropical forest reserve in Brazil, and surroundings, in September 2008. A field campaign was conducted during the same week of the SAR data recording. Polarimetric signatures for the different land covers were extracted for co- and cross-polarised bands and results indicated the variety of scattering mechanisms in the study area. Following that, the coherence and covariance matrices were used for the Freeman-Durden target decomposition, which decomposed the image targets in new bands representing the main scattering mechanism in the resolution cells – corner reflection, volumetric and superficial. Data were later classified by a k-means-Wishart classifier. The bands representing volumetric and superficial scattering helped discriminating vegetated and nonvegetated areas. Classification accuracy reached around 80% for forest and pasture/bare soil classes. For the remaining classes, the classification accuracy results did not reach 50%. Index Terms— polarimetric signatures, tropical land cover, RADARSAT-2, target decomposition, classification. 1.
INTRODUCTION
The study of tropical forest and associated covers along with their dynamics was benefited by cloud penetrating SAR (Synthetic Aperture Radar) remotely sensed data. Since 1990’s data from orbital SAR systems are available in
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regular revisit periods, which enabled studies in tropical regions and knowledge about their potential for discriminating land covers [1]. Although data from polarimetric SAR systems were also available for this period (e.g., 1994 Shuttle Imaging Radar (SIR-C/X-SAR), few studies were conducted for tropical regions. Recently, however, some studies showed the importance of polarimetric data for forest structure discrimination, biomass estimates [2, 3], and also for agricultural targets [4]. Additionally, the availability of SAR phase information allowed the use of a variety of different analysis and classification approaches [5]. Target decomposition techniques, which intend to isolate different sources of backscattering in the SAR scenes resolution cells [6], can be applied before classification schemes, for increased accuracy results [7]. The objective of this work was to assess the potential of RADARSAT-2 polarimetric C band data on land cover mapping, assessing the discrimination of tropical land covers in polarimetric signatures (PS), bands derived from a target decomposition technique and a classification approach.
2. STUDY AREA AND DATA
The study area is located in the Pará state, Brazil, and includes a region of tropical forest reserve (Tapajós National Forest) and surroundings with agriculture and cattle grazing activities (Figure 1). Illegal deforestation activities are increasing in the area, although a controlled exploitation of the forest reserve is allowed. A field campaign was conducted in September 2008, where several places were visited and land cover observed.
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IGARSS 2009
3. METHODOLOGICAL APPROACH
Figure 1: Study area in South America, Pará State (Mosaic of Terra/MODIS and Landsat/Thematic Mapper images) and detail of a RADARSAT-2 scene used in this work.
RADARSAT-2 polarimetric data were converted from slant to ground range. Following that, data were transformed into coherence and covariance matrices, requirements for the Freeman-Durden target decomposition and classification purpose. RAT and PolSARpro were the (free) SAR processing systems used in this work. Polarimetric signatures (PS) were generated to evaluate land cover classes characteristics. Polarimetric signatures analysis included: (i) Scattering heterogeneity determination, (ii) Polarisation “purity” of the targets responses, and (iii) determination of the polarisation state for target discrimination. The points checked in the field were located in the scene and PS were generated, in co- and crosspolarisations, normalised for backscattering (σ°). Also, σ° minimum and maximum values were selected and polarisation fraction and coefficient of variation were estimated. Additionally, elipticity and orientation angles for each class were observed. Five land cover classes were selected: bare soil (ploughed), (dense) forest, regenerating forest, pasture and agriculture (crops). The Freeman-Durden decomposition technique was applied and generated new bands containing information about corner reflection (double-bounce), surface and volumetric scattering mechanisms. The bands described above were classified with a k-meansWishart classifier, described in [8]. The number of interactions was set to 1, 4 and 10, and classification accuracy was checked. 4. RESULTS
RADARSAT-2 data were acquired during the same week of the field campaign, with the following characteristics (Table 1):
Table 1: RADARSAT-2 scene characteristics Band/frequency Band width Polarisation Spatial resolution Scene width Incidence angle Looks
C/5.3 cm/5.6 Hz 100 MHz Full (HH-HV-VH-VV plus phase) 25 x 28 m 25 x 25 Km 27.07° 1
Polarisation signatures for the five classes (Figure 2) showed distinct responses. The minimum intensity indicates the "pedestal height" of the polarization signature, which is related to the main type of scattering mechanism for that target. For ploughed bare soil class there was no pedestal and the PS looked similar to the one of a trihedral target (regular orientation of the rows in the soil might have an influence on that). For agriculture, the PS was similar to the soil, although with a pedestal height, suggesting the influence of the remaining of previous crop for the depolarisation of the incident wave. For forest and regenerating forest classes, RPs presented a pedestal height, although graph shapes were different. In general, dense vegetation was “able” to depolarize the incident wave. For pasture, no pedestal height suggested little depolarisation of the incident C band wave. Analysis of the minimum responses (not presented here) showed increasing depolarisation of the incident wave as the amount of vegetation increases, from bare soil to regenerating forest.
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scattering band (Fig. 3b) allowed discrimination of the main land cover types and was the band that contributed most for the classification. Stronger volumetric scattering can be seen in the dense and regenerating forests (greenish areas), which cover the main part of the study area. Pasture and bare soil covers presented weaker volumetric scattering responses (areas in blue). Surface scattering (Fig. 3c) was present all over the study area and a 100% response (a red dot North of the area) can be seen for a bare soil cover.
Ploughed bare soil
Agriculture
Figure 3. New bands generated after Freeman-Durden target decomposition showing intensity of each scattering mechanism considered for the study area: (a) corner reflection, (b) volumetric, and (c) superficial.
Forest
The classification with 4 interactions presented the higher global accuracy, with 67.7% (Fig. 4b). Dense forest and pasture classes presented accuracy higher than 80%. The remaining classifications (Fig. 4a and 4c) presented less accurate results, around 52% and 39%, respectively. Regenerating forest 5. FINAL REMARKS
Pasture Figure 2: Polarisation signatures for the five land cover classes considered. At the left side, co-polarisation responses and at the right side cross-polarisation responses. Z-axis represents the normalised power, x-axis the orientation angle (varies from –90 to 90 degrees) and y-axis the elipticity angle (varies from – 45 to 45 degrees).
The Freeman-Durden decomposition technique generated new bands containing information about the following scattering mechanisms: Corner reflection (double-bounce), surface, and volumetric scattering (Figure 3). Volumetric
Polarimetric RADARSAT-2 data and SAR image processing techniques allowed discrimination of five tropical land cover classes in Brazilian Amazonia. Polarimetric Signatures allowed observing the variety of scattering mechanisms and depolarisation amount for the different classes in the area. Volumetric scattering was the main mechanism observed in the study area and this information contributed most for the land cover classification. The band containing information about corner reflection (double-bounce) mechanism, however, was not useful for the classification, and it was of difficult interpretation. Results of land cover classification in the bands generated from Freeman-Durden target decomposition with k-means-Wishart and 4 interactions reached around 68%, with variable accuracy for different classes. Classification accuracy reached around 80% for forest and pasture/bare soil classes, confirming results of visual analysis. For the remaining classes, the classification accuracy did not reach 50%. Regenerating forest presented a very similar response to dense forest. Future work will
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evaluate different classification schemes, along with distinct approaches for interpreting polarimetric SAR data.
[6] S.R. Cloude, and E. Pottier,. “A review of target decomposition theorems in radar polarimetry”. IEEE Transactions on Geoscience and Remote Sensing, v. 34, n. 2, p. 498-518, 1996. [7] Z. Li-wen, Z. Xiao-guang, J. Yong-mei, and K. Gang-yao, “Iterative classification of Polarimetric SAR image based on the Freeman decomposition and scattering entropy”. Synthetic Aperture Radar, 2007. APSAR 2007. 1st Asian and Pacific Conference on Synthetic Aperture Radar. p.473-476, 5-9 Nov. 2007. [8] M. Neumann, et al. “Multibaseline PolinSAR module for SAR data processing and analysis in RAT (Radar Tools)”. Available at: www.cartesia.org/geodoc/isprs2004/cmm7/papers/55.pdf, viewed in November 2008.
(a)
(b)
(c)
Figure 4. Classified image using 3 distinct interactions: in (a) classification using one interaction, in (b) four interactions, and (c) ten interactions. Code for the classes are: blue for regenerating forest, red for agriculture, green for pasture, magenta for dense forest and light blue for bare soil classes.
6. REFERENCES
[1] C.C.F. Yanasse, et al. “Exploratory study of relationship between tropical forest regeneration stages and SIR C-L and C data”. Remote Sensing of Environment 59, p. 180 – 190, 1997. [2] J.R. Santos, C.C. Freitas, L.S. Araujo, L.V. Dutra, J.C. Mura, F.F. Gama, L.S. Soler, and S.J.S. Sant’Anna, “Airborne P-band SAR applied to the aboveground biomass studies in the Brazilian tropical rainforest”. Remote Sensing of Environment, n° 87, p. 482–493, 2003. [3] D.H. Hoekman, and M.J. Quiñones, “Land cover type and biomass classification using AirSAR data for evaluation of monitoring scenarios in the Colombian Amazon”. IEEE Transactions on Geoscience and Remote Sensing, vol. 2, n° 38, p. 685– 696, 2000. [4] H. McNairn, and B. Brisco, “The application of C-band polarimetric SAR for agriculture: a review”. Canadian Journal of Remote Sensing, vol. 30, n°. 3, p. 525–542, 2004. [5] J.J. van der Sanden, “Anticipated applications potential of RADARSAT-2 data”. Canadian Journal of Remote Sensing, vol. 30, n°. 3, p. 369–379, 2004.
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