Deep Convolution Neural Network (DCNN) is a popular scene classification method. However

with the neural network deeper and wider

the difficulty of training network also increases. Some scholars have proposed crop images randomly to reduce the difficulty of network training

which will reduce the relevance of the cropped image to the label. To solve this

we propose a scene classification algorithm based on adaptively regional supervision

which is constructed by three parts: heat map generation layer

adaptively supervised cropping layer

and classification layer. The algorithm generates heat map for each image

adaptively crops the image based on the heat map

and finally classifies the cropped images

which improves the relevance of the cropped images to labels. Experiments on the 15-Scene and MIT Indoor datasets show our algorithm outperforms the original network architecture in training efficiency and recognition performance

which shows the accuracy and robustness of our algorithm.