The purpose of this study was to evaluate the feasibility and applicability of object-oriented crop classification using Sentinel-1 images in the Google Earth Engine (GEE). In this study, two study areas (Keshan farm and Tongnan town) with different average plot sizes in Heilongjiang Province, China, were selected. The research time was two consecutive years (2018 and 2019), which were used to verify the robustness of the method. Sentinel-1 images of the crop growth period (May to September) in each study area were composited with three time intervals (10 d, 15 d and 30 d). Then, the composite images were segmented by simple noniterative clustering (SNIC) according to different sizes and finally, the training samples and processed images were input into a random forest classifier for crop classification. The results showed the following: 1) the overall accuracy of using the object-oriented classification method combined composite Sentinel-1 image represented a great improvement compared with the pixel-based classification method in areas with large average plots (increase by 10%), the applicable scope of the method depends on the plot size of the study area; 2) the shorter time interval of the composite Sentinel-1 image was, the higher the crop classification accuracy was; 3) the features with high importance of composite Sentinel-1 images with different time intervals were mainly distributed in July, August and September, which was mainly due to the large differences in crop growth in these months; and 4) the optimal segmentation size of crop classification was closely related to image resolution and plot size. Previous studies usually emphasize the advantages of object-oriented classification. Our research not only emphasizes the advantages of object-oriented classification but also analyzes the constraints of using object-oriented classification, which is very important for the follow-up research of crop classification using object-oriented and synthetic aperture radar (SAR).

Matrix completion is a widely used technique for personalized recommender systems. In this paper, we focus on the idea of Bounded Matrix Completion (BMC) which imposes bounded constraints into the standard matrix completion problem. It has been shown that BMC works well for several real world datasets, and an efficient coordinate descent solver called BMA has been proposed in [R. Kannan and Park., 2012]. However, we observe that BMA can sometimes converge to nonstationary points, resulting in a relatively poor accuracy in those cases. To overcome this issue, we propose our new approach for solving BMC under the ADMM framework. The proposed algorithm is guaranteed to converge to stationary points. Experimental results on real world datasets show that our algorithm can reach a lower objective function value, obtain a higher prediction accuracy and have better scalability compared with existing bounded matrix completion approaches. Moreover, our method outperforms the state-of-art standard matrix factorization in terms of prediction error in many real datasets.

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Extending computational harmonic analysis tools from the classical setting of regular lattices to the more general setting of graphs and networks is very important, and much research has been done recently. The generalized Haar–Walsh transform (GHWT) developed by Irion and Saito (2014) is a multiscale transform for signals on graphs, which is a generalization of the classical Haar and Walsh–Hadamard transforms. We propose the extended generalized Haar–Walsh transform (eGHWT), which is a generalization of the adapted time–frequency tilings of Thiele and Villemoes (1996). The eGHWT examines not only the efficiency of graph-domain partitions but also that of “sequency-domain” partitions simultaneously. Consequently, the eGHWT and its associated best-basis selection algorithm for graph signals significantly improve the performance of the previous GHWT with the similar computational cost, $$O(N \log N)$$ O ( N log N ) , where N is the number of nodes of an input graph. While the GHWT best-basis algorithm seeks the most suitable orthonormal basis for a given task among more than $$(1.5)^N$$ ( 1.5 ) N possible orthonormal bases in $$\mathbb {R}^N$$ R N , the eGHWT best-basis algorithm can find a better one by searching through more than $$0.618\cdot (1.84)^N$$ 0.618 · ( 1.84 ) N possible orthonormal bases in $$\mathbb {R}^N$$ R N . This article describes the details of the eGHWT best-basis algorithm and demonstrates its superiority using several examples including genuine graph signals as well as conventional digital images viewed as graph signals. Furthermore, we also show how the eGHWT can be extended to 2D signals and matrix-form data by viewing them as a tensor product of graphs generated from their columns and rows and demonstrate its effectiveness on applications such as image approximation.

Extending computational harmonic analysis tools from the classical setting of regular lattices to the more general setting of graphs and networks is very important and much research has been done recently. The Generalized Haar-Walsh Transform (GHWT) developed by Irion and Saito ( 2014) is a multiscale transform for signals on graphs, which is a generalization of the classical Haar and Walsh-Hadamard Transforms. We propose the extended Generalized Haar-Walsh Transform (eGHWT), which is a generalization of the adapted time-frequency tilings of Thiele and Villemoes (1996). The eGHWT examines not only the eciency of graph-domain partitions but also that of "sequency-domain" partitions simultaneously. Consequently, the eGHWT and its associated best-basis selection algorithm for graph signals signi cantly improve the performance of the previous GHWT with the similar computational cost, ( log ), where is the number of nodes of an input graph. While the GHWT best-basis algorithm seeks the most suitable orthonormal basis for a given task among more than (1.5) possible orthonormal bases in ℝ , the eGHWT best-basis algorithm can nd a better one by searching through more than 0.618 ⋅ (1.84) possible orthonormal bases in ℝ . This article describes the details of the eGHWT best-basis algorithm and demonstrates its superiority using several examples including genuine graph signals as well as conventional digital images viewed as graph signals. Furthermore, we also show how the eGHWT can be extended to 2D signals and matrix-form data by viewing them as a tensor product of graphs generated from their columns and rows and demonstrate its e ectiveness on applications such as image approximation.Keywords Graph wavelets and wavelet packets ⋅ Haar-Walsh wavelet packet transform ⋅ best basis selection ⋅ graph signal approximation ⋅ image analysis