This article presents an approach to automating the creation of land-use/land-cover classification (LULC) maps from satellite images using deep neural networks that were developed to perform semantic segmentation of natural images. This work is important since the production of accurate and timely LULC maps is becoming essential to government and private companies that rely on them for large-scale monitoring of land resource changes. In this work, deep neural networks re trained to classify each pixel of a satellite image into one of a number of LULC classes. The presented deep neural networks are all pre-trained using the ImageNet Large-Scale Visual Recognition Competition (ILSVRC) datasets and then fine-tuned using approximately 19,000 Landsat 5/7 satellite images of resolution 224 × 224 taken of the Province of Manitoba in Canada. The result is an automated solution that can produce LULC maps images significantly faster than current semi-automated methods. The contributions of this article are the observation that deep neural networks developed for semantic segmentation can be used to automate the task of producing LULC maps; the use of these networks to produce LULC maps; and a comparison of several popular semantic segmentation architectures for solving the problem of automated LULC map production.

Feature extraction is a crucial step in pattern recognition problems as well as in methods for characterizing the quality of a product surface (Liu, J. Ph.D. thesis, McMaster University, Canada, 2004). In this paper, different types of wavelet transforms, that is, the wavelet packet transform and the discrete wavelet transform, are compared in the feature extraction step for classification of the surface quality of rolled steel sheets (Bharati, M.; et al. Chemom. Intell. Lab. Syst. 2004, 72, 57-71). Using this real-world industrial example, we have experimentally shown that the wavelet packet transform is superior to the discrete wavelet transform in terms of classification performance and Fisher's criterion. We also propose an easy but powerful method to determine the optimal decomposition level. A closer look at the characteristics of the image data reveals that as a result of its equal frequency bandwidth, wavelet packet transform is more suitable for extracting textural features when textural information from different classes of images is not confined within a certain (spatial) frequency region.

We propose an optimal-basis texture classification strategy performed in the wavelet packet domain, in order to characterize quality-related information from a set of images. The proposed method enables one to select the discriminative texture in accordance with class information. The proposed methodology has several stages: feature extraction, feature selection, feature reduction, and classification. In the feature extraction stage, we used wavelet energy signatures obtained from wavelet packet transform. In the feature selection stage, two simple optimal-basis methods (top-down and bottom-up searching) were used to select discriminative signatures with high Fisher’s criterion values. These approaches improve classification accuracy and reduce the number of features used to classify the quality. Our proposed methodology was applied and validated to classify the surface quality of rolled steel sheets. Using this real-world industrial example, we have experimentally shown that the proposed optimal-basis approach is superior to a full-wavelet-packet-based approach, in terms of classification performance and the number of features used.