Crack　is　one　of　the　important　pavement　damages.　The　number　and　types　of　cracks　are　usually　collected　and　inspected　by　human,　but　lately　computer　vision　has　shown　great　potential　for　automatically　identifying　pavement　cracks.　However,　training　a　convolutional　neural　network　(CNN)　requires　large　amount　of　data.　The　limited　number　of　crack　images　and　the　variety　of　crack　image　background　put　challenges　for　developing　CNN　model　with　high　accuracy.　This　study　proposes　an　intelligent　method　for　crack　pattern　classification　based　on　limited　field　images.　The　proposed　method　has　a　two-step　data　preprocessing.　It　first　applied　data　augmentation　method　to　enlarge　the　dataset,　solving　the　data　imbalance　problem　and　providing　more　training　data.　Then　a　crack　extraction　method　was　applied　to　convert　the　original　image　into　a　binary　back-and-white　image.　This　step　significantly　reduced　the　input　feature　and　simplified　the　model.　Both　of　data　preprocessing　steps　were　designed　to　decrease　the　bias　of　the　model.　Then　we　performed　a　model　selection　and　hyper-parameter　tunning　for　CNN.　We　explored　the　application　of　AlexNet,　SE-Net,　and　ResNet　with　a　variety　of　configurations.　The　results　show　that　the　proposed　data　augmentation　can　enlarge　the　dataset　significantly.　The　crack　extraction　also　significantly　increased　the　test　accuracy.　The　ResNet　with　50　layers　has　the　highest　test　accuracy.　With　data　augmentation,　crack　extraction　and　model　selection　combining　together,　the　final　approach　can　improve　the　test　accuracy　from　52.68%　to　87.50%　compared　to　the　original　limited　images.　The　result　suggests　our　proposed　method　works　well　to　classify　crack　images　of　asphalt　pavement　with　limited　sample　size.