Automatic weed detection and classification faces the challenges of large intraclass variation and high spectral similarity to other vegetation. With the availability of new high-resolution remote sensing data from various platforms and sensors, it is possible to capture both spectral and spatial characteristics of weed species at multiple scales. Effective multi-resolution feature learning is then desirable to extract distinctive intensity, texture and shape features of each category of weed to enhance the weed separability. We propose a feature extraction method using a Convolutional Neural Network (CNN) and superpixel based Local Binary Pattern (LBP). Both middle and high level spatial features are learned using the CNN. Local texture features from superpixel-based LBP are extracted, and are also used as input to Support Vector Machines (SVM) for weed classification. Experimental results on the hyperspectral and remote sensing datasets verify the effectiveness of the proposed method, and show that it outperforms several feature extraction approaches.