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Recently, representation-based classifiers have gained increasing interest in hyperspectral image (HSI) classification. In this letter, based on our previously developed joint collaborative representation (JCR) classifier, an improved version, which is called weighted JCR (WJCR) classifier, is proposed. JCR adopts the same weights when extracting spatial and spectral features from surrounding pixels. Differing from JCR, WJCR attempts to utilize more appropriate weights by considering the similarity between the center pixel and its surroundings. Experimental results using two real HSIs demonstrate that the proposed WJCR outperforms the original JCR and some other traditional classifiers, such as the support vector machine (SVM), the SVM with a composite kernel, and simultaneous orthogonal matching pursuit.Index Terms-Collaborative representation based classifier, hyperspectral image (HSI) classification, nearest regularized subspace (NRS) classifier, sparse representation based classifier, spectral-spatial information.

In this letter, kernel collaborative representation with Tikhonov regularization (KCRT) is proposed for hyperspectral image classification. The original data is projected into a highdimensional kernel space by using a nonlinear mapping function to improve the class separability. Moreover, spatial information at neighboring locations is incorporated in the kernel space. Experimental results on two hyperspectral data prove that our proposed technique outperforms the traditional support vector machines with composite kernels and other state-of-the-art classifiers, such as kernel sparse representation classifier and kernel collaborative representation classifier.

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Under complex field conditions, robust and efficient boll detection at maturity is an important tool for pre-harvest strategy and yield prediction. To achieve automatic detection and counting of long-staple cotton in a natural environment, this paper proposes an improved algorithm incorporating deformable convolution and attention mechanism, called YOLO-C, based on YOLOv7: (1) To capture more detailed and localized features in the image, part of the 3 × 3 convolution in the ELAN layer of the backbone is replaced by deformable convolution to improve the expressiveness and accuracy of the model. (2) To suppress irrelevant information, three SENet modules are introduced after the backbone to improve the ability of feature maps to express information, and CBAM and CA are introduced for comparison experiments. (3) A WIoU loss function based on a dynamic non-monotonic focusing mechanism is established to reduce the harmful gradients generated by low-quality examples on the original loss function and improve the model performance. During the model evaluation, the model is compared with other YOLO series and mainstream detection algorithms, and the model mAP@0.5 achieves 97.19%, which is 1.6% better than the YOLOv7 algorithm. In the model testing session, the root mean square error and coefficient of determination (R2) of YOLO-C are 1.88 and 0.96, respectively, indicating that YOLO-C has higher robustness and reliability for boll target detection in complex environments and can provide an effective method for yield prediction of long-staple cotton at maturity.