CAJ | 학술논문

针对中国北方部分农区夏秋两季易受长时间云、雨、雾影响导致区域农用地信息难以实时准确获取的现状，在Freeman 极化分解模型基础上，该文提出了一种三分量极化分解优化模型农用地合成孔径雷达（SAR）影像自动提取方法，并开展不同作物覆盖条件下农用地信息提取试验研究。该文首先通过引入体散射分量参数，二次散射分量参数和布拉格散射分量参数，对现有Freeman极化分解模型进行优化，使分解结果更符合农业区域不同地物散射特征；然后，在利用优化三分量极化分解方法提取极化分量基础上，结合模糊C均值聚类，实现农用地信息高精度自动提取。最后，该研究以中国重要黄淮海农业区河北衡水枣强县为试验区，以Radarsat-2影像为试验数据源，在作物全覆盖和作物部分覆盖2种条件下，通过将优化三分量-FCM分类和常用雷达分类方法H-Alpha-Lambda分类的农用地提取结果与地面验证样方进行对比，完成该研究所提出SAR影像农用地提取方法的精度验证和评价。结果表明，在作物全覆盖条件下，利用优化三分量-FCM分类提取农用地信息的总体精度和Kappa系数分别为96.12%和0.857，较H-Alpha-Lambda分类方法分别提高了8.69个百分点和0.337；在作物部分覆盖条件下，利用优化三分量-FCM分类提取农用地信息的总体精度和Kappa系数分别为97.53%和0.902，较H-Alpha-Lambda分类分别提高了17.37个百分点和0.595。可见，无论在作物全覆盖还是部分覆盖条件下，优化三分量-FCM分类方法提取的农用地精度均优于H-Alpha-Lambda分类方法，证明了该算法提取农用地信息具有一定可行性和适用性，为SAR影像在农业遥感应用中的农用地信息提取提供了新的思路。
침대중국북방부분농구하추량계역수장시간운、우、무영향도치구역농용지신식난이실시준학획취적현상，재Freeman 겁화분해모형기출상，해문제출료일충삼분량겁화분해우화모형농용지합성공경뢰체（SAR）영상자동제취방법，병개전불동작물복개조건하농용지신식제취시험연구。해문수선통과인입체산사분량삼수，이차산사분량삼수화포랍격산사분량삼수，대현유Freeman겁화분해모형진행우화，사분해결과경부합농업구역불동지물산사특정；연후，재이용우화삼분량겁화분해방법제취겁화분량기출상，결합모호C균치취류，실현농용지신식고정도자동제취。최후，해연구이중국중요황회해농업구하북형수조강현위시험구，이Radarsat-2영상위시험수거원，재작물전복개화작물부분복개2충조건하，통과장우화삼분량-FCM분류화상용뢰체분류방법H-Alpha-Lambda분류적농용지제취결과여지면험증양방진행대비，완성해연구소제출SAR영상농용지제취방법적정도험증화평개。결과표명，재작물전복개조건하，이용우화삼분량-FCM분류제취농용지신식적총체정도화Kappa계수분별위96.12%화0.857，교H-Alpha-Lambda분류방법분별제고료8.69개백분점화0.337；재작물부분복개조건하，이용우화삼분량-FCM분류제취농용지신식적총체정도화Kappa계수분별위97.53%화0.902，교H-Alpha-Lambda분류분별제고료17.37개백분점화0.595。가견，무론재작물전복개환시부분복개조건하，우화삼분량-FCM분류방법제취적농용지정도균우우H-Alpha-Lambda분류방법，증명료해산법제취농용지신식구유일정가행성화괄용성，위SAR영상재농업요감응용중적농용지신식제취제공료신적사로。
Because of the long-time influence of rain, cloud and fog in summer and autumn, the information of agricultural land is difficult to obtain instantly and accurately in local agricultural areas in North China. Radar remote sensing has advantages of all-time, all-weather and high penetration etc, and it can be widely used in cloudy regions. In applications of multi-polarization radar data, polarization decomposition model can get effective polarization characteristics to extract land features accurately. The Freeman decomposition model is a polarization decomposition model used frequently, but it can only be used in the circumstances that satisfy the demand of reflection symmetry, which limits the use of the model to further improve the classification accuracy of remote sensing to a certain extent. On the basis of analyzing the reason that the Freeman decomposition model is not suitable for agricultural area, this paper proposed an automatic extracting method of agricultural land of SAR image using optimized three-component decomposition model (OTDM) which is the improvement of the Freeman decomposition model. In this study, firstly, the orientation processing was joined into polarization decomposition model to inhibit the production of negative power. And the Freeman decomposition model was optimized by introducing volume scattering parameter, secondary scattering parameters and Bragg scattering parameters, so as to improve the performance of Freeman decomposition model which has the shortcoming of lacking adjustable parameters, and make the decomposition results adaptable to the scattering characteristics of different surfaces of agricultural area. Then, combining OTDM and fuzzy C-means clustering (FCM), after land feature categories were merged, agricultural land information was extracted in an automatic way. The results of experiments indicated that when parameters were equivalent to 3, 1.75, 10 and 0.001, respectively, the classification from FCM achieved the better result. Finally, both of the H-Alpha-Lambda and OTDM-FCM were applied in an experiment and compared with the ground samples to verify the effectiveness of OTDM-FCM. In this experiment, the study area was located in Zaoqiang County, Hebei Province in Huang-huai-hai Plain, and Radarsat-2 images were used as the radar data source. The experiment was carried out under the circumstances of full and partial cover crop by selecting the images in appropriate time. The final results of the experiment indicated that under the circumstance of full cover crop, overall accuracy and Kappa coefficient of OTDM-FCM were 96.12% and 0.857, respectively, while the results of H-Alpha-Lambda were 87.43% and 0.520, respectively; under the circumstance of partial cover crop, overall accuracy and Kappa coefficient of OTDM-FCM were 97.53% and 0.902, respectively, while the results of H-Alpha-Lambda were 80.16% and 0.307, respectively. It could be concluded that the classification extraction accuracy of OTDM-FCM was superior to H-Alpha-Lambda classification under the circumstances of both full and partial cover crop. Therefore, under the conditions of different imaging time and different extents of crop covered, OTDM-FCM classification algorithm could effectively extract the information of agricultural land, and it was shown that OTDM-FCM classification had certain feasibility and applicability in the extraction of agricultural land depending on SAR image information. This method put forward in this paper could provide a new thinking for the application of SAR image in the extraction of agricultural land information.