PCANet is a simple network using Principal Component Analysis (PCA) for image classification and obtained high accuracies on a variety of datasets. PCA projects explanatory variables on a subspace that the first component has the largest variance. On the other hand, Partial Least Squares (PLS) regression projects explanatory variables on a subspace that the first component has the largest covariance between explanatory and objective variables, and the objective variables are predicted from the subspace. If class labels are used as objective variables for PLS, the subspace is suitable for classification. Stacked PLS is a simple network using PLS for image classification and obtained high accuracy on the MNIST database. However, the performance of Stacked PLS was inferior to PCANet on the others. One of differences between Stacked PLS and PCANet is network architecture. In this paper, we combine the network architecture of PCANet with PLS and propose a new image classification method called PLSNet. It obtained higher accuracies than PCANet on the MNIST and the CIFAR-10 datasets. Furthermore, we change how to make filters for extracting features at the second convolution layer, and we call it Improved PLSNet. It obtained higher accuracies than PLSNet. In addition, we give it deeper network architecture, and we call it Deep Improved PLSNet. It obtained higher accuracies than Improved PLSNet.