A Research on the Combination Strategies of Multiple Features for Hyperspectral Remote Sensing Image Classification

<div>Classification maps of the PU dataset by the multiple kernel method; (a)–(e), respectively, represent different groups of input features: spectral features, GF, TF, GF combined with TF, and all features along with the spectral features. <svg height="9.49473pt" id="M74" style="vertical-align:-0.2063999pt" version="1.1" viewbox="-0.0498162 -9.28833 7.34169 9.49473" width="7.34169pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M530 686C535 705 530 712 521 712C504 712 448 684 359 674L358 648H393C437 648 439 646 429 593L400 435C372 447 345 448 332 448C286 448 194 414 144 373C68 311 23 203 23 111C23 26 57 -12 91 -12C120 -12 147 3 188 29C227 54 290 102 341 170H343L322 71C308 6 320 -12 341 -12C373 -12 442 27 501 96L485 120C455 91 422 67 408 67C401 67 401 76 404 91C440 294 479 473 530 686ZM387 375L355 241C326 187 200 53 142 53C126 53 109 73 109 130C109 217 154 337 218 381C240 396 265 404 297 404S372 390 387 375Z" id="g113-101"></path></g></svg> is the dimension associated with the best performance.</div>

Journal of Sensors

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Figure 13

Figure 13: A Research on the Combination Strategies of Multiple Features for Hyperspectral Remote Sensing Image Classification 