K. Palaniappan, F. Zhu, X. Zhuang, Y. Zhao, and A. Blanchard

IEEE Int. Geoscience and Remote Sensing Symposium (IGARSS), Volume II, pgs. 688--692, 2000

gis, classification, segmentation, data mining, machine learning, remote sensing

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Abstract

The development of automatic land cover classification maps using validated statewide datasets supported by state and federal agencies are becoming an important tool for monitoring change, planning, and land use impact assessment. Accurate and fast classification algorithms, adaptive to different data sources and scales, will facilitate cost effective routine updating of land cover maps using current satellite imagery to monitor and detect a broad array of land cover phenomena. A new binary decision tree classifier incorporating an evolutionary genetic learning algorithm is proposed for land cover classification. The new classifier referred to as the Enhanced Binary Tree Genetic Algorithm (BTGA+) has been applied to automatically classify tens of millions of pixels in two full scenes of Landsat TM data using multispectral multitemporal radiance features. The BTGA+ classifier can assign pixels into eight land cover categories for Landsat TM scenes in central Missouri with nearly 90% classification accuracy.