  Human vision possesses strong invariance in image recognition. The cognitive
capability of deep convolutional neural network (DCNN) is close to the human
visual level because of hierarchical coding directly from raw image. Owing to
its superiority in feature representation, DCNN has exhibited remarkable
performance in scene recognition of high-resolution remote sensing (HRRS)
images and classification of hyper-spectral remote sensing images. In-depth
investigation is still essential for understanding why DCNN can accurately
identify diverse ground objects via its effective feature representation. Thus,
we train the deep neural network called AlexNet on our large scale remote
sensing image recognition benchmark. At the neuron level in each convolution
layer, we analyze the general properties of DCNN in HRRS image recognition by
use of a framework of visual stimulation-characteristic response combined with
feature coding-classification decoding. Specifically, we use histogram
statistics, representational dissimilarity matrix, and class activation mapping
to observe the selective and invariance representations of DCNN in HRRS image
recognition. We argue that selective and invariance representations play
important roles in remote sensing images tasks, such as classification,
detection, and segment. Also selective and invariance representations are
significant to design new DCNN liked models for analyzing and understanding
remote sensing images.
