AltiKa Radiometer: Instrument Description and In–Flight Performance

On 25 February 2013, the Satellite for Argos and AltiKa (SARAL) was launched from the Indian Sriharikota launch site. The AltiKa payload consisted of an altimeter and a radiometer. This paper describes the AltiKa radiometer. This instrument has been studied for several years by CNES, TAS-F, ASTRIUM-F and a set of science laboratories, and AltiKa is the first compact instrument embedding simultaneously the altimeter and radiometer functions. AltiKa radiometer is a dual frequency instrument working in K (23.8 GHz) and Ka band (37 GHz), it is based on the total power principle, with direct detection receivers. On-ground acceptance tests exhibited a very high level of performance: less than 0.2 dB has been estimated for both sensitivity and absolute accuracy in both frequencies. This paper focuses on the in-flight performances that have been observed since the launch. All the instrument observable characterizations are nominal, and in-flight sensitivity has been estimated lower than 0.2 K.     

Region-based classification of PolSAR data using radial basis kernel functions with stochastic distances

Region-based classification of PolSAR data can be effectively performed by seeking for the assignment that minimizes a distance between prototypes and segments. Silva et al. [“Classification of segments in PolSAR imagery by minimum stochastic distances between wishart distributions.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6 (3): 1263–1273] used stochastic distances between complex multivariate Wishart models which, differently from other measures, are computationally tractable. In this work we assess the robustness of such approach with respect to errors in the training stage, and propose an extension that alleviates such problems. We introduce robustness in the process by incorporating a combination of radial basis kernel functions and stochastic distances with Support Vector Machines (SVM). We consider several stochastic distances between Wishart: Bhatacharyya, Kullback-Leibler, Chi-Square, Rényi, and Hellinger. We perform two case studies with PolSAR images, both simulated and from actual sensors, and different classification scenarios to compare the performance of Minimum Distance and SVM classification frameworks. With this, we model the situation of imperfect training samples. We show that SVM with the proposed kernel functions achieves better performance with respect to Minimum Distance, at the expense of more computational resources and the need of parameter tuning. Code and data are provided for reproducibility.