Detection by classification of buildings in multispectral satellite imagery

Tomohiro Ishii, Edgar Simo-Serra, Satoshi Iizuka, Yoshihiko Mochizuki, Akihiro Sugimoto, Hiroshi Ishikawa, Ryosuke Nakamura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

33 Citations (Scopus)


We present an approach for the detection of buildings in multispectral satellite images. Unlike 3-channel RGB images, satellite imagery contains additional channels corresponding to different wavelengths. Approaches that do not use all channels are unable to fully exploit these images for optimal performance. Furthermore, care must be taken due to the large bias in classes, e.g., most of the Earth is covered in water and thus it will be dominant in the images. Our approach consists of training a Convolutional Neural Network (CNN) from scratch to classify multispectral image patches taken by satellites as whether or not they belong to a class of buildings. We then adapt the classification network to detection by converting the fully-connected layers of the network to convolutional layers, which allows the network to process images of any resolution. The dataset bias is compensated by subsampling negatives and tuning the detection threshold for optimal performance. We have constructed a new dataset using images from the Landsat 8 satellite for detecting solar power plants and show our approach is able to significantly outperform the state-of-the-art. Furthermore, we provide an indepth evaluation of the seven different spectral bands provided by the satellite images and show it is critical to combine them to obtain good results.

Original languageEnglish
Title of host publication2016 23rd International Conference on Pattern Recognition, ICPR 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781509048472
Publication statusPublished - 2016 Jan 1
Event23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico
Duration: 2016 Dec 42016 Dec 8

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Other23rd International Conference on Pattern Recognition, ICPR 2016

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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