In this paper, an effective approach is proposed to recognize the order among codewords from the randomly binary and non-binary interleaved linear block coded streams if the code minimum distance is known, where the interleaver lengthNn+dis not the multiple of the linear block code lengthn. Then, an efficient approach is developed without knowing linear block code parameters such as code rate and the code generator matrix (except the code length) to recognize the whole random interleaving pattern if the greatest common divisor (GCD) ofnanddis GCD(n, d) = 1 for both binary and non-binary interleaved linear block coded symbols. Further, for the case GCD(n, d) =q̸= 1, the whole random interleaving pattern can be recognized with the probability of no more than (q!)−(Nn+d)/qfor the following cases: (1) the non-binary interleaved coded symbols without knowing the non-binary code generator matrix; (2) the binary interleaved coded symbols.

Multilabel remote sensing image classification can provide comprehensive object-level semantic descriptions of remote sensing images. However, most existing methods cannot fully mine the fine-grained features of images and labels, resulting in low classification accuracy. To address this issue, we propose a novel multitask framework for multilabel remote sensing image classification. The framework establishes the class-specific feature extraction as a binary classification auxiliary task to assist the main multilabel classification task, which can improve the model’s local and global feature extraction ability. Meanwhile, the framework updates the label correlation graph using the graph transformer layer to accurately identify label node pairs with potential correlation, which effectively mines the correlation of multiple labels to generate more accurate label co-occurrence embedding for image label prediction. Experimental results on UCM, AID, and DFC15 multilabel datasets show that the proposed method outperforms existing state-of-the-art methods.